GRCC Psychology Lecture Series | Joseph Cesario

00:56:24
https://www.youtube.com/watch?v=Yi-FdpLST3Y

摘要

TLDREl contenido aborda la complejidad de los tiroteos policiales fatales en los Estados Unidos, examinando el posible sesgo racial en la decisión de disparar por parte de los oficiales. Hasta 2015 no existían bases de datos completas sobre estos eventos, lo que creó inexactitudes en el entendimiento de su frecuencia y circunstancias. Investigaciones recientes han utilizado bases de datos compiladas por organizaciones periodísticas para evaluar tales eventos, encontrando que el sesgo racial en las decisiones de disparo es una cuestión complicada y no siempre suscribible a simples disparidades de población. Aun cuando los datos muestran disparidades a nivel de población global, cuando se ajustan por la exposición al crimen violento, estas disparidades parecen disminuir. Sin embargo, ciertos tipos de disparos, especialmente casos de malentendidos como confundir una billetera con un arma, presentan grandes incertidumbres debido a la falta de datos. La exposición al crimen y la violencia es clave para comprender estos eventos, más aún que la simple raza del oficial o la víctima. Los resultados revelan la importancia de la exposición al crimen en estas situaciones policiales y ponen en duda ideas simplificadas sobre el sesgo racial.

心得

  • 📊 Antes de 2015 no había datos completos sobre tiroteos policiales en EE.UU.
  • 📉 Disparidades raciales en tiroteos disminuyen al ajustar por crimen violento.
  • 🤔 No se puede simplificar el sesgo racial con los niveles de población.
  • 🔍 Diferencias en crimen violento por raza afectan las estadísticas de disparos.
  • ❓ Incertidumbre sobre sesgos raciales en casos específicos de tiroteos.
  • 🔫 Los tiroteos fatales suelen ocurrir en contextos de criminalidad.
  • 🧩 La raza del oficial no es un factor determinante en el disparo.
  • 📈 El contexto del lugar influye más que la raza del oficial en los tiroteos.
  • 📝 La colecta de datos por medios es crucial para estos análisis.
  • 🧐 Los estudios sugieren que entendimientos comunes sobre el sesgo pueden ser erróneos.

时间轴

  • 00:00:00 - 00:05:00

    En este video se discute la complejidad de las interacciones entre la policía y los ciudadanos, especialmente en el contexto de tiroteos fatales. Se debate sobre la noción de que los tiroteos fatales por parte de la policía están mal entendidos y que encontrar evidencia de sesgo racial en las decisiones de disparar no es tan simple como parece. A través de algunos casos emblemáticos de EE.UU., se plantea que las suposiciones de sesgo racial en tiroteos policiales son complejas y a menudo se basan en datos insuficientes.

  • 00:05:00 - 00:10:00

    Históricamente, no había bases de datos completas sobre los tiroteos fatales de la policía en EE.UU. hasta que organizaciones como The Washington Post y The Guardian comenzaron a documentarlos en 2015. Antes de esto, muchas policía no reportaban estos eventos, y se descubrió que había un subregistro del 50%. Este descubrimiento fue en parte porque grandes ciudades decidieron no reportar para no sobresalir en estadísticas negativas. Sin embargo, la situación está cambiando con nuevas legislaciones.

  • 00:10:00 - 00:15:00

    La interacción entre un oficial de policía y un ciudadano es compleja y dinámica. Puede empezar como una decisión discrecional o no discrecional, y cada interacción puede desarrollarse de manera diferente. Ejemplos de diferentes interacciones resaltan cómo incluso un pequeño cambio en la situación puede llevar a resultados diferentes, algunos de los cuales pueden terminar en eventos fatales. Esto subraya que no todos los tiroteos son iguales.

  • 00:15:00 - 00:20:00

    Al enfrentar un tiroteo policial, es importante considerar la influencia de ambas partes en la interacción: tanto el oficial como el ciudadano. Se destaca que la evidencia de sesgo racial puede no ser consistente a lo largo de diferentes etapas de interacción, y es posible que los prejuicios raciales no aparezcan en todas las decisiones involucradas en el uso de la fuerza letal.

  • 00:20:00 - 00:25:00

    El análisis de los tiroteos fatales muestra que el uso de la fuerza letal por parte de policías está vinculado con situaciones de crímenes violentos. Se discuten ejemplos donde los ciudadanos estaban armados o eran sospechosos de un crimen violento en progreso. Se resalta que una fuerza letal justificada se basa en la percepción de amenaza por el oficial.

  • 00:25:00 - 00:30:00

    Los datos sugieren que la mayoría de las situaciones que llevan a un tiroteo fatal no son aleatorias, sino situaciones de crímenes violentos. En 2015, muchas víctimas de tiroteos estaban armadas. Este análisis indica que los oficiales disparan principalmente en estos contextos, y que el número de espectadores inocentes afectados es bajo.

  • 00:30:00 - 00:35:00

    El uso de proporciones poblacionales para medir disparidades raciales en tiroteos puede ser engañoso, como se demuestra con ejemplos de tratamiento de cáncer y carreras académicas en Física. Para comprender las disparidades, es importante considerar el contexto específico, como quiénes están realmente en las situaciones donde se utiliza la fuerza letal.

  • 00:35:00 - 00:40:00

    Los ciudadanos de diferentes grupos raciales no están involucrados en situaciones de delitos violentos en proporciones iguales a su representación poblacional. Esto afecta cómo se analiza el uso de la fuerza letal, sugiriendo que tasas de crimen violentas son un mejor marcador para evaluar disparidades que meras cifras de población.

  • 00:40:00 - 00:45:00

    Los datos del FBI y el CDC se utilizan para entender mejor la exposición de los distintos grupos raciales a situaciones de crimen violento, que son contextos donde la fuerza letal es más relevante. Estos datos indican que no hay un sesgo anti-negro significativo en los tiroteos, una vez se considera el contexto del crimen violento.

  • 00:45:00 - 00:50:00

    Al examinar casos más ambiguos, como tiroteos de ciudadanos desarmados, los datos son inciertos y limitados, lo que lleva a un estado de desconocimiento. Esto subraya la complejidad de hacer aseveraciones concluyentes sobre el sesgo racial en situaciones específicas de uso de la fuerza letal.

  • 00:50:00 - 00:56:24

    Por último, se analiza la raza de los oficiales involucrados en los tiroteos. No hubo relación entre la raza del oficial y del ciudadano disparado. Los datos sugieren que las tasas de criminalidad local influyen más en estos eventos, y la raza del oficial no es un factor determinante. Esto apunta a que el contexto del crimen violento es clave para entender los tiroteos policiales fatales.

显示更多

思维导图

视频问答

  • ¿En qué contextos suelen ocurrir los tiroteos policiales fatales?

    Las situaciones de tiroteos policiales fatales ocurren principalmente en contextos de crímenes violentos, donde los oficiales sienten amenazas para sus vidas o las de los demás.

  • ¿Siempre hay sesgos raciales en los tiroteos policiales fatales?

    No, no siempre hay pruebas claras de sesgos raciales en los tiroteos policiales fatales una vez que se consideran las tasas de criminalidad por grupo racial.

  • ¿Por qué es engañoso sólo usar proporciones de población para analizar los tiroteos policiales raciales?

    Existen desigualdades en las oportunidades que tienen los diferentes grupos raciales para estar en situaciones donde podría ser relevante el uso de fuerza mortal por parte de la policía.

  • ¿Qué relación se encontró entre la raza de los oficiales y los tiroteos de ciudadanos de minorías?

    Se encontró que el nivel de crimen en un condado está más relacionado con la raza de la persona que es fatalmente disparada que la raza de los oficiales involucrados.

  • ¿Cuándo comenzaron a existir bases de datos completas sobre tiroteos policiales fatales?

    Las bases de datos más completas sobre tiroteos policiales fatales se crearon a partir de 2015 gracias al trabajo de organizaciones periodísticas como The Guardian y The Washington Post.

  • ¿Implica el sesgo racial que más ciudadanos negros sean disparados, independientemente del contexto?

    No, ya que las investigaciones muestran que los tiroteos ocurren principalmente en situaciones de criminalidad violenta y no sólo por el simple sesgo racial.

  • ¿Por qué hay incertidumbre en algunos resultados sobre el sesgo racial y los tiroteos?

    La incertidumbre prevalece debido a la falta de datos adecuados para algunas situaciones específicas de tiroteo, como aquellas que involucran malentendidos de objetos como armas.

  • ¿La raza del oficial afecta a quién disparan?

    La raza del oficial no se relaciona directamente con el hecho de que dispare más a personas de minorías raciales, sino más con el contexto de criminalidad del lugar.

  • ¿Por qué antes de 2015 no había datos completos sobre tiroteos policiales?

    Antes de 2015 no existían bases de datos completas debido a que no era un requerimiento federal reportar tiroteos a nivel nacional.

  • ¿Hay evidencia clara de sesgo racial en los datos de tiroteos policiales analizados?

    El trabajo analítico presentó que incluso en casos de tiroteos no está claro si hay sesgos raciales debido a que muchos factores contextuales influyen.

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    it's a pleasure
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    interests you in that you have
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    on the research what I want to do is
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    maybe give an overview of some of the
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    work that we've done in our lab on the
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    topic of race and police shootings
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    including some analyses of real-world
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    data from police shooting events in the
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    United States and also some experimental
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    laboratory data if we have time as well
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    and where we're gonna end up getting to
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    hopefully at the end of this is a
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    general theme that the the nature of
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    fatal police shootings is often
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    misunderstood
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    in many people's minds and that finding
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    evidence for racial bias in officers
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    decisions to shoot is actually a much
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    more complicated thing than it might
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    appear on the surface okay and at the
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    same time also where we're gonna end up
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    is that there are some things that we
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    just don't know we don't have good
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    enough data for and we are and some
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    questions in a state of uncertainty and
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    and we have to just be comfortable with
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    being in a state of unknown and
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    uncertainty for a while so when we think
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    about police shootings at least in the
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    United States most of us think of a
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    number of widely publicized and really
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    well-known highly tragic cases that have
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    happened in the u.s. okay if you're of a
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    certain age probably roughly my age or
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    older one of the first people that might
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    come to mind is amadou diallo who was
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    shot by the NYPD unarmed African
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    immigrant who was reaching for his
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    wallet and was shot in the late 90s and
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    more recently more in the modern or in
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    the last 10 to 15 year is Michael Brown
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    of course Ferguson Missouri also unarmed
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    when shot and a few years ago Philander
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    Oh Castillo okay who was shot in
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    Minnesota again during a traffic stop
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    was reaching for his wallet and the
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    officer shot and killed him okay
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    much of the discussion around fatal
  • 00:02:11
    police shootings concerns cases like
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    this okay really high-profile
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    extraordinarily tragic heartbreaking
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    cases of fatal shootings in the u.s.
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    well it goes along with that is often
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    the assumption that these
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    are the common kind of fatal police
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    shooting okay
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    that in other words that these are
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    representative of what fatal police
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    shootings really look like so we can ask
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    question of what do we know about the
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    roughly thousand or so US citizens who
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    are shot and killed by the police each
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    year and what do we know about the
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    circumstances surrounding those
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    shootings and what do we know about the
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    role of racial bias or of an
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    individual's race on the likelihood that
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    that person was shot him it turns out
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    that up until a few years ago we
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    actually weren't able to answer these
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    kinds of questions and any kind of
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    comprehensive nationwide level okay
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    that's because prior to 2015 in case you
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    didn't know there were no complete
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    databases of fatal police shootings
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    that's because in an almost unbelievable
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    condition here in the u.s. police
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    departments are not required by law to
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    report fatal police shootings to the
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    federal government okay
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    so when a police department has a fatal
  • 00:03:30
    shooting of a citizen they're under
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    they're encouraged to report but they're
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    under no technical obligation to report
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    that to the to the federal government
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    and so people had suspected that federal
  • 00:03:41
    databases of fatal police shootings were
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    under counts of actual shoots shootings
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    but no one really had any hard data on
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    just how bad those federal databases
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    were in 2015 news organizations like The
  • 00:03:55
    Washington Post and The Guardian
  • 00:03:56
    pictured here did some reporting and
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    developed public databases of all fatal
  • 00:04:02
    police shootings in the US and so they
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    did investigative journalism they had
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    citizens contact them when there was a
  • 00:04:09
    shooting in their city and they went out
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    and gathered information about that and
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    compiled all of those in one place and
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    there are a little bit different from
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    one another to databases aren't exactly
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    the same but they are as far as we know
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    the most comprehensive and complete
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    databases of fatal police shootings and
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    it turned out you could we could now
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    look and ask how much were police
  • 00:04:30
    departments under reporting in the US
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    and they it turned out they were under
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    reporting by about 50% okay so in at the
  • 00:04:36
    federal level we only knew
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    about half of all of the actual
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    shootings that had occurred in the US
  • 00:04:42
    right um there's some rumor that the
  • 00:04:47
    reason why or one of the major reasons
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    why that was the case was at the three
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    major cities in the u.s. particularly
  • 00:04:53
    Los Angeles New York and Chicago all had
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    an unspoken agreement that they wouldn't
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    fully report because none they didn't
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    know which was worse none of them wanted
  • 00:05:01
    to be number one in this statistic so
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    they all just simply failed to report
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    and that's that's a large number of
  • 00:05:07
    shootings from the database but it's not
  • 00:05:09
    all of them so it was the case that many
  • 00:05:12
    departments perhaps most apartments were
  • 00:05:14
    under reporting the the police shootings
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    that they were having okay so that that
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    is an incredible statistic it's still
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    not the case that they're forced to
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    they're working on new legislation to
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    get this but this is really one of the
  • 00:05:27
    the most embarrassing aspects of United
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    States federal law enforcement policy so
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    now that we have these more complete
  • 00:05:36
    databases we can look to them and try to
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    answer some questions about the nature
  • 00:05:40
    of fatal police shootings okay by way of
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    getting to some answers it's a day of
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    technical problems that's fine I can use
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    the computer here by way of getting to
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    some answers we want to first I'll keep
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    something in mind okay when we try to
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    ask the question about fatal police
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    shootings so that we get some clarity
  • 00:06:03
    about what exactly we are and what
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    exactly we aren't able to answer them so
  • 00:06:09
    one important point is that the process
  • 00:06:13
    that leads from initial contact between
  • 00:06:16
    a police officer and a citizen to the
  • 00:06:19
    endpoint of a fatal shooting is a very
  • 00:06:22
    complicated dynamic oftentimes
  • 00:06:26
    vulnerable volatile very uncertain
  • 00:06:30
    process okay and this makes the topic a
  • 00:06:32
    bit difficult to study it is not a
  • 00:06:34
    straightforward movement from initial
  • 00:06:36
    contact to shooting and it's not a
  • 00:06:39
    straightforward series of events even
  • 00:06:41
    from one shooting to the next and so we
  • 00:06:43
    have to appreciate some of the
  • 00:06:44
    uncertainty that's part of this okay if
  • 00:06:46
    we think about you know some kind of
  • 00:06:48
    hypothetical police-citizen interaction
  • 00:06:51
    and there's some initial contact that
  • 00:06:53
    gets made between the officers and the
  • 00:06:55
    citizens sometimes that's discretionary
  • 00:06:58
    where the police officer might make the
  • 00:07:00
    decision to stop someone because they
  • 00:07:02
    look like a suspect or they think
  • 00:07:04
    they're doing something suspicious or
  • 00:07:05
    whatever other times it's
  • 00:07:06
    non-discretionary okay so the officers
  • 00:07:09
    might be responding to 911 911 call they
  • 00:07:11
    don't have a choice and to do that okay
  • 00:07:13
    they have they're compelled to do that
  • 00:07:15
    so there can be discretionary there can
  • 00:07:17
    be non-discretionary initial contacts
  • 00:07:19
    okay once that initial contact is made
  • 00:07:22
    though there's some interaction that
  • 00:07:25
    begins okay some ongoing interaction
  • 00:07:27
    that's gonna unfold over time right it
  • 00:07:30
    might be initial information gathering
  • 00:07:32
    on the part of the officer and during
  • 00:07:34
    that interaction many different things
  • 00:07:36
    can occur that you could imagine that at
  • 00:07:38
    some point the officer is going to make
  • 00:07:40
    the decision to pack down the citizen
  • 00:07:42
    that they're talking with okay for again
  • 00:07:44
    for whatever reason we don't always have
  • 00:07:46
    insight into into that decision okay but
  • 00:07:49
    there's some complicated process and
  • 00:07:50
    complicated interaction going with a
  • 00:07:53
    number of different decision steps along
  • 00:07:55
    the way okay sometimes it ends there and
  • 00:07:58
    that's the end of the the police citizen
  • 00:08:00
    interaction other times as it unfolds it
  • 00:08:04
    can branch off you could think about the
  • 00:08:05
    many different ways that that
  • 00:08:06
    interaction could branch off and follow
  • 00:08:08
    different paths okay just give you a
  • 00:08:11
    couple of examples from recent some
  • 00:08:13
    police shootings and some not police
  • 00:08:15
    shootings okay recently in New York an
  • 00:08:18
    individual was stopped he was a part of
  • 00:08:21
    the group of citizens who was a highly
  • 00:08:23
    publicized event of dumping water on
  • 00:08:26
    some police officers he was stopped for
  • 00:08:28
    a different reason
  • 00:08:30
    the police officers were talking with
  • 00:08:32
    him it was not I wouldn't describe it as
  • 00:08:34
    a friendly interaction okay but it
  • 00:08:36
    wasn't an overtly combative interaction
  • 00:08:38
    either when they went to cuff him then
  • 00:08:40
    he took off running okay now this ended
  • 00:08:43
    up he was you know grabbed and and
  • 00:08:46
    handcuffed and that was the end of it
  • 00:08:47
    and in a different case this is a still
  • 00:08:51
    from body cam image of a event of a
  • 00:08:53
    shooting from Salt Lake City a couple
  • 00:08:55
    years ago an officer was called on a 911
  • 00:08:59
    call got dispatched that there was a gun
  • 00:09:01
    in a convenience store he showed up he
  • 00:09:03
    tried to talk with this
  • 00:09:04
    I believe it was a 19 year old again not
  • 00:09:07
    overly combative but the officer told me
  • 00:09:10
    put his hands up because he thought he
  • 00:09:12
    was the one with the handgun the citizen
  • 00:09:14
    told him no and reached for his
  • 00:09:16
    waistband and the officer shot him
  • 00:09:18
    thinking that he was going for a gun
  • 00:09:19
    that was tucked into his waistband
  • 00:09:21
    it turned out he was unarmed ok so again
  • 00:09:23
    an initial contact that went in a very
  • 00:09:25
    different direction one was Kimura
  • 00:09:27
    combative one was a less combative event
  • 00:09:30
    in Chicago last year this was an event
  • 00:09:34
    where again officers stopped a citizen
  • 00:09:36
    that he was a suspect that they were for
  • 00:09:40
    I believe a murder actually the
  • 00:09:42
    interaction again was going very well
  • 00:09:43
    until they went to pat him down and then
  • 00:09:45
    they discovered he had a gun on his hip
  • 00:09:47
    he then became combative and ran when he
  • 00:09:51
    ran he got stopped by a car spun around
  • 00:09:53
    grabbed his hip and the officer shot him
  • 00:09:56
    ok in that case he didn't grab his gun
  • 00:09:58
    but he would had his hand where his gun
  • 00:10:01
    was and the officers knew that he was
  • 00:10:03
    armed and and sometimes you get much
  • 00:10:06
    clearer cases such as this case where
  • 00:10:09
    again a citizen was stopped he had a gun
  • 00:10:11
    ran from the officers had the gun out
  • 00:10:14
    and turned around to face the officers
  • 00:10:15
    and he also was shot so if you think
  • 00:10:19
    about that series of events ok that long
  • 00:10:24
    complex just highly variable set of
  • 00:10:28
    events leading from initial contact to a
  • 00:10:30
    possible shooting we really want to
  • 00:10:32
    appreciate just the complexity that's
  • 00:10:34
    involved there ok no two shootings are
  • 00:10:37
    going to be exactly alike right within
  • 00:10:41
    this what we also want to keep in mind
  • 00:10:43
    and this is often hard to remember is
  • 00:10:46
    that there are really two parties in any
  • 00:10:49
    one of these interactions ok and each of
  • 00:10:51
    those parties can influence the way in
  • 00:10:54
    which that interaction unfolds at any of
  • 00:10:56
    these points right and so we have the
  • 00:10:58
    officer and we care a lot about the
  • 00:11:00
    officer and we typically study the
  • 00:11:01
    officer and focus on the officer but
  • 00:11:03
    there's also the citizen right both of
  • 00:11:05
    them together are coming together in
  • 00:11:07
    this interaction and influencing how it
  • 00:11:10
    unfolds not all officers are the same
  • 00:11:12
    some are more authoritarian than others
  • 00:11:14
    some are more combative and
  • 00:11:16
    disrespectful than others
  • 00:11:17
    not all citizens are the same okay some
  • 00:11:19
    are more combative and disrespectful
  • 00:11:21
    than others they are more or less
  • 00:11:23
    compliant than others each of those at
  • 00:11:25
    any point can impact how this
  • 00:11:27
    interaction carries out so within all of
  • 00:11:33
    this it's very firm about and also the
  • 00:11:36
    other thing to keep in mind is that if
  • 00:11:38
    we do find evidence of racial bias let's
  • 00:11:41
    say on the part of police officers at
  • 00:11:43
    one stage it doesn't mean there's racial
  • 00:11:45
    bias at every one of these stages okay
  • 00:11:47
    and if we don't find it at one stage it
  • 00:11:48
    doesn't mean that there isn't some
  • 00:11:50
    somewhere else okay so an officer might
  • 00:11:52
    be racially biased let's say in terms of
  • 00:11:54
    the quality of the interaction that they
  • 00:11:57
    have with the citizen where maybe
  • 00:11:58
    they're dis more disrespectful say
  • 00:12:00
    toward minority than non-minority
  • 00:12:01
    citizens okay they might show racial
  • 00:12:04
    bias there and not in some other stuff
  • 00:12:06
    okay so we're gonna try as we move
  • 00:12:08
    through some of the data to answer a
  • 00:12:10
    number of these steps okay but where
  • 00:12:12
    we're gonna start with is that decision
  • 00:12:15
    to use deadly force and the endpoint
  • 00:12:18
    decision here in the moment where the
  • 00:12:20
    officer has to make a decision to fire
  • 00:12:23
    his or her gun or not okay what do we
  • 00:12:26
    know about that decision in the role
  • 00:12:27
    that race might play in it and when we
  • 00:12:30
    ask about racial bias we what we really
  • 00:12:33
    mean is the question that was a famous
  • 00:12:35
    question that was raised a number of
  • 00:12:36
    decades ago of whether officers have
  • 00:12:39
    quote one trigger finger for blacks and
  • 00:12:41
    a separate trigger finger
  • 00:12:42
    for whites okay in other words is it the
  • 00:12:45
    case that officers would not shoot
  • 00:12:47
    people if they were white relative to if
  • 00:12:50
    they were black that's essentially the
  • 00:12:52
    question of racial bias in that decision
  • 00:12:55
    to shoot okay
  • 00:13:00
    so as I said the major question that we
  • 00:13:03
    have then is to try to understand race
  • 00:13:04
    bias in that decision okay in the
  • 00:13:07
    decision to use deadly force and
  • 00:13:08
    understand the degree to which it
  • 00:13:11
    impacts police officers okay you might
  • 00:13:15
    think that's really not a question that
  • 00:13:18
    needs to be asked because we all already
  • 00:13:20
    know in fact that there's a racial
  • 00:13:22
    disparity and who gets shot and we
  • 00:13:24
    already know that police officers are
  • 00:13:26
    biased and that's what you know account
  • 00:13:29
    for that racial disparity in terms of
  • 00:13:31
    citizen race and being fatally shot by
  • 00:13:34
    the police well what we want to do
  • 00:13:37
    actually is look at what the evidence
  • 00:13:38
    for that is and try to understand that
  • 00:13:40
    evidence in a little bit more detail and
  • 00:13:42
    see how solid that really might be and
  • 00:13:45
    what we're gonna conclude here at the
  • 00:13:48
    end of this section is that
  • 00:13:50
    understanding racial by old asperities
  • 00:13:53
    okay understanding whether one group is
  • 00:13:55
    shot more than we would expect relative
  • 00:13:57
    to another group is always relative okay
  • 00:14:00
    we always have to have some standard or
  • 00:14:02
    some comparison that we're dealing with
  • 00:14:05
    okay we're comparing the rate at which
  • 00:14:06
    some group is shot relative to what okay
  • 00:14:09
    and that becomes a really key question
  • 00:14:11
    in understanding racial disparities and
  • 00:14:15
    so just let's take a look at some
  • 00:14:17
    numbers okay these are fatal shooting
  • 00:14:20
    data in the u.s. from 2015 we're gonna
  • 00:14:23
    restrict our dumb numbers here just to
  • 00:14:26
    black or white citizens just for ease of
  • 00:14:29
    analyzing the data but I you know we can
  • 00:14:32
    look at other groups as well if you're
  • 00:14:34
    interested in we can talk about that at
  • 00:14:36
    the end of him and these are only fatal
  • 00:14:39
    shootings so as hard as it is to get
  • 00:14:42
    data and learn something about fatal
  • 00:14:43
    shootings it's even harder to get to any
  • 00:14:46
    data to learn something about non-fatal
  • 00:14:47
    shootings okay so only only conclusions
  • 00:14:51
    that we can draw a really concern the
  • 00:14:53
    case where a citizen is actually killed
  • 00:14:55
    we know almost nothing about when
  • 00:14:57
    citizens aren't killed but they are
  • 00:14:58
    shocked okay and and officers do not
  • 00:15:01
    kill everybody that they they shoot okay
  • 00:15:04
    there's a lot of shootings that occur
  • 00:15:05
    and that don't result in a fatality
  • 00:15:07
    we're just looking at fatal shootings
  • 00:15:09
    here because those are the data that we
  • 00:15:10
    that we have that we can look at and so
  • 00:15:13
    in 2015 there were 261 black citizens in
  • 00:15:18
    the u.s. who were shot fatally by the
  • 00:15:20
    police they imagine each of the people
  • 00:15:22
    up there is a hundred people and and
  • 00:15:25
    there were 526 white citizens who were
  • 00:15:29
    shot by the police in 2015 and so in
  • 00:15:32
    terms of just a simple odds right based
  • 00:15:35
    on those raw numbers we would say that
  • 00:15:37
    whites were twice as likely to be shot
  • 00:15:39
    relative to blacks in the United States
  • 00:15:41
    in 2050
  • 00:15:43
    okay now obviously we all recognize
  • 00:15:45
    right off the bat that that's not a
  • 00:15:47
    particularly meaningful number okay to
  • 00:15:49
    know that whites were twice as likely
  • 00:15:51
    there's 526 versus 261 because of course
  • 00:15:55
    there are more white citizens in the US
  • 00:15:56
    than black citizens okay so even if
  • 00:15:59
    officers were showing a lot of racial
  • 00:16:01
    bias we would still expect more whites
  • 00:16:03
    to be shot just given that there's a
  • 00:16:05
    larger pool of whites to draw from okay
  • 00:16:07
    relative to black citizens in the u.s.
  • 00:16:10
    so we need to do some kind of adjustment
  • 00:16:13
    for that right in other words we need to
  • 00:16:15
    have some benchmark right what to what
  • 00:16:18
    do we benchmark those 261 and those 526
  • 00:16:22
    citizens shot we have to have some
  • 00:16:23
    comparison and the way that this is
  • 00:16:28
    typically done the standard way to do
  • 00:16:30
    for this to do this is to adjust for or
  • 00:16:34
    compared to overall population levels
  • 00:16:37
    okay that is a standard kind of
  • 00:16:39
    comparison to look at disparities of
  • 00:16:41
    really any kind but especially racial
  • 00:16:44
    disparities in the decision to shoot
  • 00:16:46
    okay and when we do that what we're
  • 00:16:48
    asking is our black citizens our white
  • 00:16:51
    citizens more likely to be shot given
  • 00:16:54
    each groups overall population level
  • 00:16:57
    okay so given the overall number of
  • 00:16:59
    black and white citizens in the u.s.
  • 00:17:01
    right benchmarked against that are black
  • 00:17:04
    or white citizens more likely to be to
  • 00:17:07
    be fatally shot okay that's really the
  • 00:17:10
    question that we're asking when we
  • 00:17:11
    benchmark against population values okay
  • 00:17:14
    so again if we take those 261 in the 526
  • 00:17:18
    citizens okay we can benchmark that
  • 00:17:22
    again against the I'm not putting the
  • 00:17:25
    exact numbers roughly 40 million black
  • 00:17:28
    citizens in the US and 200 million white
  • 00:17:30
    citizens in the US okay and we can ask
  • 00:17:33
    what are the odds that a person is shot
  • 00:17:35
    if they're black or white given the
  • 00:17:39
    overall population values okay and the
  • 00:17:42
    way that we would do that which is just
  • 00:17:43
    a standard way again that every
  • 00:17:45
    calculation would do that is you would
  • 00:17:47
    compute the odds of being shot if you're
  • 00:17:49
    a black citizen relative to the odds
  • 00:17:51
    that you're a shot of being a white
  • 00:17:52
    citizen and divided one of the odds over
  • 00:17:55
    the other okay and then
  • 00:17:56
    gives you an odds ratio that tells you
  • 00:17:58
    that one group is acts times as likely
  • 00:18:00
    as another to have something happen
  • 00:18:02
    again this is absolutely ubiquitous if
  • 00:18:05
    you were doing work on the odds of dying
  • 00:18:07
    from a heart attack or something you
  • 00:18:08
    would do exactly the same sort of
  • 00:18:10
    calculation okay and so basically you
  • 00:18:13
    could say you would take the 261 and
  • 00:18:16
    compute the odds that a black citizen is
  • 00:18:18
    getting is going to be shot given the
  • 00:18:20
    population of 40 million that works out
  • 00:18:23
    two point zero zero zero zero zero six
  • 00:18:26
    five okay so you're looking at roughly
  • 00:18:28
    seven in a million okay you do the same
  • 00:18:32
    thing for whites 526 out of 200 and
  • 00:18:35
    million again you you basically divide
  • 00:18:37
    those two numbers you get about three
  • 00:18:39
    out of a million okay
  • 00:18:40
    and what you can see is that relative to
  • 00:18:42
    each groups overall population it's
  • 00:18:45
    clear that black citizens are more
  • 00:18:46
    likely to be shocked okay
  • 00:18:48
    the odds are two-and-a-half times
  • 00:18:49
    greater than a black citizen will be
  • 00:18:51
    shot relative to white citizen seven
  • 00:18:53
    million out versus seven out of a
  • 00:18:55
    million versus three 1/2 out of a
  • 00:18:57
    million okay and that's the standard
  • 00:18:58
    odds ratio that's a well-known value if
  • 00:19:01
    you look at things like unarmed
  • 00:19:02
    shootings that number is even higher
  • 00:19:04
    something closer to three and a half
  • 00:19:06
    times more likely to be shot unarmed if
  • 00:19:08
    you're a black citizen relative to if
  • 00:19:11
    you're a white citizen okay so when we
  • 00:19:12
    say that one group is two-and-a-half
  • 00:19:14
    times more likely to have some event
  • 00:19:16
    that's how we would calculate that okay
  • 00:19:19
    if you prefer you could do the same
  • 00:19:22
    thing in terms of percentages okay you
  • 00:19:23
    could say out of everybody who's shot by
  • 00:19:26
    the police
  • 00:19:26
    black satisfy the police excuse me black
  • 00:19:30
    citizens represent twenty three percent
  • 00:19:31
    of that group okay of that roughly
  • 00:19:34
    thousand people or so that's about
  • 00:19:36
    twenty three percent of that group in
  • 00:19:37
    the US population
  • 00:19:39
    black citizens are roughly thirteen
  • 00:19:40
    percent there's a disparity right
  • 00:19:42
    thirteen is we would expect thirteen
  • 00:19:44
    percent and we get twenty three so again
  • 00:19:46
    there's a disparity there we do the same
  • 00:19:48
    thing with whites and again computer
  • 00:19:49
    odds ratio okay to give us roughly the
  • 00:19:52
    same value there okay so when we do that
  • 00:19:58
    the conclusion is is absolutely clear
  • 00:20:01
    there's no question at all about this
  • 00:20:03
    that relative to the population values
  • 00:20:06
    of each group or the population levels
  • 00:20:08
    of each group blacks are over
  • 00:20:10
    presented in being fatally shot by the
  • 00:20:12
    police okay and so we would state that
  • 00:20:15
    as black citizens are two-and-a-half
  • 00:20:17
    times likely to be shot compared to
  • 00:20:19
    whites given each groups overall number
  • 00:20:22
    in the US population okay and again as I
  • 00:20:29
    said this is a just absolutely standard
  • 00:20:31
    way of doing this kind of work
  • 00:20:32
    anytime you're showing a per capita
  • 00:20:34
    event that you're basically doing the
  • 00:20:37
    same thing as what I just showed you any
  • 00:20:39
    time you know two per 100,000 or two per
  • 00:20:41
    million whatever it might be
  • 00:20:43
    comparing percentages all of these cases
  • 00:20:45
    are basically doing the same thing of
  • 00:20:47
    calculating a ratio based on or
  • 00:20:51
    benchmarked against some population
  • 00:20:54
    value okay but notice what we're saying
  • 00:20:59
    when we make the statement that black
  • 00:21:01
    citizens or any group would be
  • 00:21:03
    two-and-a-half times more likely to be
  • 00:21:04
    shot okay given their overall
  • 00:21:07
    representation in the population okay I
  • 00:21:10
    said earlier that all calculations of
  • 00:21:14
    this type are relative to some standard
  • 00:21:16
    there's always some comparison that we
  • 00:21:19
    have to make in that we have to justify
  • 00:21:20
    the particular comparison that we're
  • 00:21:22
    using okay here the benchmark as we said
  • 00:21:25
    is population proportions it's the forty
  • 00:21:27
    million and the two hundred million
  • 00:21:29
    citizens in the u.s. broadly okay when
  • 00:21:33
    we use that what we're saying is that we
  • 00:21:35
    expect that group members should be
  • 00:21:37
    shocked at the same proportion that they
  • 00:21:40
    exist in the general population okay
  • 00:21:42
    that's the implicit statement that we're
  • 00:21:44
    making when we do this kind of
  • 00:21:46
    comparison and if we deviate from that
  • 00:21:48
    expectation then we say that there's a
  • 00:21:50
    disparity right we expect that black
  • 00:21:52
    citizens should be shot it should be 13%
  • 00:21:55
    of those shot because they're 13% of the
  • 00:21:57
    population we deviate from that and so
  • 00:21:59
    there's a disparity there and but what
  • 00:22:03
    we want to ask actually in digging a
  • 00:22:04
    little deeper is whether it's correct
  • 00:22:06
    in fact and whether it's accurate to
  • 00:22:08
    compare the proportion of a groups
  • 00:22:10
    outcome to that groups overall
  • 00:22:13
    representation in the population okay is
  • 00:22:16
    that actually the right benchmark for us
  • 00:22:18
    to understand this outcome in to gain an
  • 00:22:20
    understanding of racial bias in the
  • 00:22:23
    decision to
  • 00:22:24
    shoot okay okay well why might this be
  • 00:22:27
    misleading let's take a couple non
  • 00:22:29
    shooting examples just to illustrate the
  • 00:22:33
    general problem with this calculation
  • 00:22:35
    okay imagine that there's a new cancer
  • 00:22:38
    treatment out okay it's the best cancer
  • 00:22:40
    treatment that there is it's the you
  • 00:22:41
    know gold star for cancer treatment and
  • 00:22:43
    we want to research whether groups are
  • 00:22:45
    receiving that treatment in a
  • 00:22:47
    proportionate way okay is there a
  • 00:22:49
    disparity in who is receiving that
  • 00:22:51
    treatment okay so let's say we look at
  • 00:22:53
    everybody who has who gets cancer
  • 00:22:55
    treatment in a year and we find that 13%
  • 00:22:58
    of all those people who got the best
  • 00:23:00
    cancer treatment were black citizens
  • 00:23:02
    okay we're black patients so we look and
  • 00:23:07
    we compare that to the thirteen percent
  • 00:23:09
    in the u.s. population right by that
  • 00:23:12
    metric or by that benchmark we would say
  • 00:23:15
    that there is not a disparity right
  • 00:23:17
    things look great here okay black
  • 00:23:19
    citizens are 13% of the population and
  • 00:23:21
    they're also 13% of those receiving the
  • 00:23:24
    top treatment everything looks fine okay
  • 00:23:26
    so by that benchmark we would say that
  • 00:23:28
    the the world of cancer treatment does
  • 00:23:31
    not show any disparities any racial
  • 00:23:33
    disparities I'm imagine that we found
  • 00:23:37
    out for whatever reason might be the
  • 00:23:39
    case we don't need to know the reason
  • 00:23:41
    right now but imagine that we found out
  • 00:23:42
    that actually although black citizens
  • 00:23:45
    make up 13% of the US population
  • 00:23:48
    they actually make up 50% of cancer
  • 00:23:50
    patients now at this point we would I
  • 00:23:54
    think rightly conclude that there was a
  • 00:23:56
    racial disparity here okay that black
  • 00:23:58
    patients were not getting the top
  • 00:24:00
    treatment in accordance with what is the
  • 00:24:03
    appropriate benchmark the people who
  • 00:24:05
    have cancer okay
  • 00:24:06
    so in this case it's quite clear that we
  • 00:24:09
    would want to compare the proportion who
  • 00:24:11
    got cancer treatment to the proportion
  • 00:24:12
    who actually have cancer not to the
  • 00:24:14
    general US population the general US
  • 00:24:16
    population is irrelevant in this case
  • 00:24:18
    okay because the outcome isn't needed
  • 00:24:21
    for everybody right in this case there's
  • 00:24:24
    a very clear way in which that's true
  • 00:24:25
    which is that people generally don't
  • 00:24:27
    elect to have cancer treatment if they
  • 00:24:28
    don't have cancer okay so so we have a
  • 00:24:31
    clearer sense of who the appropriate
  • 00:24:33
    pool is and it's not the total US
  • 00:24:35
    population
  • 00:24:37
    let's take another example which is
  • 00:24:39
    disparities sucks disparities in stem
  • 00:24:42
    okay we can look at physics majors in
  • 00:24:45
    undergraduate populations okay let's
  • 00:24:47
    look and see as is the case that females
  • 00:24:51
    represent about 20% of physics majors
  • 00:24:53
    okay so we look at all physics majors if
  • 00:24:56
    we consider that to be the outcome
  • 00:24:57
    you know enrolling as a physics major we
  • 00:24:59
    find that women are only 20% of physics
  • 00:25:02
    majors and we look to the u.s.
  • 00:25:04
    population women are about 51 percent of
  • 00:25:07
    the u.s. population alright that's a
  • 00:25:09
    disparity we have a clear disparity here
  • 00:25:11
    there's a 30% gap in terms of what we
  • 00:25:14
    would expect to be true right we should
  • 00:25:17
    expect a 51% enrollment of women in
  • 00:25:20
    physics
  • 00:25:20
    whereas physics majors but in this case
  • 00:25:24
    actually the disparity is much worse
  • 00:25:27
    than that okay and that's because women
  • 00:25:30
    make up 70% of the undergraduate
  • 00:25:32
    population ok so again the disparity is
  • 00:25:37
    not a 30% disparity the disparity is
  • 00:25:39
    actually a 50% disparity then as we saw
  • 00:25:42
    in the previous example the relevant
  • 00:25:44
    pool of individuals is not the u.s.
  • 00:25:46
    population it's people enrolled in
  • 00:25:48
    college okay because you have to be
  • 00:25:50
    enrolled in college to be a physics
  • 00:25:51
    major right so the u.s. population
  • 00:25:54
    benchmark as in the prior example can be
  • 00:25:57
    misleading right if the outcome is not
  • 00:26:01
    equally available to everybody or
  • 00:26:03
    equally relevant to everybody then it's
  • 00:26:05
    not always the case that the u.s.
  • 00:26:07
    population level is the right benchmark
  • 00:26:09
    okay here it's the undergraduate
  • 00:26:11
    population okay both of these cases
  • 00:26:15
    illustrate some potential pitfalls with
  • 00:26:18
    choosing the US population as the right
  • 00:26:21
    benchmark okay and they have a major
  • 00:26:24
    assumption that's built in there right
  • 00:26:26
    and that assumption is that members of
  • 00:26:29
    different groups have equal
  • 00:26:30
    opportunities for experiencing that
  • 00:26:32
    outcome okay in the case of cancer
  • 00:26:35
    treatment it's obviously it's obvious
  • 00:26:37
    that that's not true okay because only
  • 00:26:39
    people with cancer elect to have cancer
  • 00:26:41
    treatment okay in this physics major the
  • 00:26:44
    same is true because not everybody in
  • 00:26:46
    the US population is becoming a physics
  • 00:26:48
    major you have to be enrolled as an
  • 00:26:49
    undergraduate okay
  • 00:26:51
    so that the assumption there when we use
  • 00:26:54
    that population benchmark is that
  • 00:26:56
    members of different groups have that
  • 00:26:58
    same opportunity and if we apply this
  • 00:27:02
    logic to the case of fatal police
  • 00:27:05
    shootings when we benchmark shooting
  • 00:27:08
    percentages or raw shooting numbers
  • 00:27:11
    against the US population what we are
  • 00:27:15
    saying in fact what's required for us to
  • 00:27:17
    do that is to say that members of
  • 00:27:20
    different racial groups are in those
  • 00:27:22
    situations where deadly force is
  • 00:27:24
    relevant at rates that are proportionate
  • 00:27:27
    to their overall population just as in
  • 00:27:31
    the cancer and the stem example what
  • 00:27:34
    that calculation requires is that if
  • 00:27:37
    black citizens are 13% of the US
  • 00:27:39
    population and if white citizens are 70
  • 00:27:41
    percent of the population then it must
  • 00:27:43
    be the case that if we look at all of
  • 00:27:46
    those decisions all those situations
  • 00:27:47
    where deadly force is relevant for the
  • 00:27:50
    officer okay where the officer is in the
  • 00:27:53
    case of making a shooter no shoot
  • 00:27:54
    decision that 13 percent of those
  • 00:27:57
    individuals are black citizens and 70
  • 00:27:59
    percent are white that has to be the
  • 00:28:03
    case if we're going to look to the
  • 00:28:05
    population proportions I'm so
  • 00:28:09
    benchmarking any type of you benchmark
  • 00:28:11
    some number against the US population
  • 00:28:13
    you are requiring that the experience of
  • 00:28:17
    that or the opportunity for that event
  • 00:28:19
    to occur happens in proportion to that
  • 00:28:22
    groups overall representation okay if it
  • 00:28:25
    doesn't as with the stem in the cancer
  • 00:28:27
    examples you're gonna get a misleading
  • 00:28:29
    understanding of what the world is like
  • 00:28:31
    and if it's the case that different
  • 00:28:36
    groups occupy deadly force relevant
  • 00:28:40
    decisions at different rates and then
  • 00:28:43
    the more appropriate benchmark is not
  • 00:28:46
    the number of citizens in the general US
  • 00:28:49
    population okay it's the number of
  • 00:28:51
    citizens from each group who are in
  • 00:28:53
    those deadly force situations okay
  • 00:28:56
    that's the appropriate benchmark if we
  • 00:28:59
    want to know something about racial bias
  • 00:29:01
    on the part of the police officer and
  • 00:29:03
    I'll give some examples here
  • 00:29:04
    in a second of why that's actually
  • 00:29:07
    important and if said differently
  • 00:29:09
    you can't experience a policing outcome
  • 00:29:12
    without encountering the police and so
  • 00:29:15
    if members from different groups
  • 00:29:17
    encounter the police at different rates
  • 00:29:19
    we have to take that into account right
  • 00:29:21
    we can't just look at their overall
  • 00:29:23
    population values otherwise it's a
  • 00:29:25
    misleading quantity okay given this what
  • 00:29:33
    we can do is ask whether police are
  • 00:29:35
    likely to use deadly force in every
  • 00:29:36
    single situation okay if it's the case
  • 00:29:39
    that police are likely to use deadly
  • 00:29:40
    force as likely in one situation as
  • 00:29:43
    another then actually we're okay with
  • 00:29:45
    using the population values right if
  • 00:29:48
    it's the case that police are more
  • 00:29:49
    likely to use deadly force in some
  • 00:29:51
    situations than others then that
  • 00:29:53
    population value might be misleading
  • 00:29:55
    okay and so we want to know are there
  • 00:29:58
    some situations for which deadly force
  • 00:30:00
    is relevant for an officer and some for
  • 00:30:03
    which it is not okay and it's very clear
  • 00:30:05
    and I'll show you the data on this okay
  • 00:30:07
    that deadly force is not relevant for
  • 00:30:10
    every single experience that a police
  • 00:30:12
    officer has or every single kind of
  • 00:30:14
    policing scenario for an officer okay
  • 00:30:16
    and that's because deadly force
  • 00:30:18
    scenarios are situations are strongly
  • 00:30:21
    strongly tied to violent crime
  • 00:30:23
    situations okay
  • 00:30:25
    so as a qualitative way of looking at
  • 00:30:27
    this we can actually look I just started
  • 00:30:30
    at 2015 at the database that we have now
  • 00:30:32
    from the Guardian and went through the
  • 00:30:34
    first ten shootings of 2015 okay so
  • 00:30:37
    let's take a look I gave you give you a
  • 00:30:39
    quick summary of what those first ten
  • 00:30:41
    shootings are the first shooting there
  • 00:30:45
    was a 911 call about a domestic
  • 00:30:47
    disturbance so that's non-discretionary
  • 00:30:48
    the police have to show up the police
  • 00:30:50
    show up to this house the man here
  • 00:30:53
    emerged from his house pointing his gun
  • 00:30:55
    at police officers and police officer
  • 00:30:58
    shot and killed the police officer who
  • 00:30:59
    showed up shot and killed him and second
  • 00:31:02
    we don't have a picture of this
  • 00:31:03
    individual again a 9-1-1 call came in
  • 00:31:06
    there was an armed man police showed up
  • 00:31:09
    the man threatened to shoot the officers
  • 00:31:11
    pointed his gun at them refused to drop
  • 00:31:13
    his gun and the officers shot and killed
  • 00:31:15
    him
  • 00:31:16
    okay third case police respond to 9-1-1
  • 00:31:20
    call okay there was a violent dispute a
  • 00:31:22
    violent domestic dispute they showed up
  • 00:31:24
    they the man was tased okay he wasn't
  • 00:31:27
    complying with the police orders so he
  • 00:31:29
    was tased he reached for his waistband
  • 00:31:31
    and was shot in this case he didn't have
  • 00:31:33
    a gun on him the officer thought he was
  • 00:31:36
    reaching for a gun when he went for his
  • 00:31:37
    waistband this one is a still actually
  • 00:31:43
    from the dashcam video of this event man
  • 00:31:46
    was pulled over in a traffic stop at
  • 00:31:48
    night the officer thought he was acting
  • 00:31:50
    really nervously he got him out of the
  • 00:31:52
    car did a pat down as he was patting him
  • 00:31:54
    down discovered a gun the moment the
  • 00:31:56
    officer discovered the gun the guys
  • 00:31:57
    became combative and tried to grab his
  • 00:31:59
    gun and the officer shot him while he
  • 00:32:02
    was reaching for his gun
  • 00:32:05
    this one again there's no no picture
  • 00:32:07
    this individual a man pulled up to a
  • 00:32:08
    restricted police lot waited for the
  • 00:32:10
    police to come to his car when he did he
  • 00:32:12
    pulled out a gun and only aimed it at
  • 00:32:14
    the officers and the officer shot him
  • 00:32:15
    this was a tragic case also of suicide
  • 00:32:19
    by cop we know this because he left a
  • 00:32:21
    note in his car for the police officer
  • 00:32:23
    who was going to eventually killed him
  • 00:32:25
    saying I'm sorry I used you I just want
  • 00:32:28
    to die and so on okay we'll talk we
  • 00:32:30
    could talk about that also there's a
  • 00:32:31
    surprisingly high number of those cases
  • 00:32:33
    in the US I think the number six man
  • 00:32:37
    identified by the police as a robbery
  • 00:32:39
    suspect he was driving a stolen vehicle
  • 00:32:41
    they stopped him they ID'd him as a
  • 00:32:43
    robbery suspect he then opened his door
  • 00:32:45
    and started firing his gun at the police
  • 00:32:46
    officers officers killed him and police
  • 00:32:49
    respond to a 911 call of a man armed
  • 00:32:51
    with a gun threatening bar patrons and
  • 00:32:53
    assaulting his girlfriend he was shot
  • 00:32:56
    when he refused to drop his weapon again
  • 00:33:00
    9-1-1 call man threatening his son the
  • 00:33:02
    police show up the man took out his gun
  • 00:33:04
    and pointed it at officers was shot and
  • 00:33:06
    killed him police were trying to carry
  • 00:33:09
    out a warrant for this man he was wanted
  • 00:33:10
    for sexual assault of a teenage girl he
  • 00:33:13
    aimed his gun at the police officers and
  • 00:33:15
    was shot okay finally again 9-1-1 call
  • 00:33:18
    officers respond the man here had a
  • 00:33:21
    knife he stabbed the police dog when
  • 00:33:23
    they when they showed up with the dog
  • 00:33:24
    and then he raised his knife over his
  • 00:33:26
    head and approached the officers in the
  • 00:33:28
    officers shot
  • 00:33:29
    and the point about going through each
  • 00:33:33
    of these cases is to illustrate that
  • 00:33:35
    this is not a random sample of
  • 00:33:37
    situations that everybody in the u.s.
  • 00:33:39
    finds themselves in okay and it's not a
  • 00:33:42
    random sample of what is relevant for
  • 00:33:44
    everybody in the US okay these
  • 00:33:46
    situations are by and large violent
  • 00:33:50
    crime okay they involve violent crime
  • 00:33:52
    right and police training as well as the
  • 00:33:54
    legal justification for police to use
  • 00:33:56
    deadly force is tied to threats okay
  • 00:33:59
    they're tied to threats to the officers
  • 00:34:01
    life or another citizens life that's
  • 00:34:04
    when police are justified in using
  • 00:34:05
    deadly threat or a deadly force and so
  • 00:34:10
    the question is not our police or our
  • 00:34:13
    black or white citizens more likely to
  • 00:34:15
    be shot given their overall population
  • 00:34:18
    proportions it's our black and white
  • 00:34:19
    citizens more likely to be shot given
  • 00:34:21
    their encounters with the police in
  • 00:34:23
    these decision relevant situations okay
  • 00:34:26
    and the quantitative data backed this up
  • 00:34:28
    as well so if you look at the data on
  • 00:34:30
    again that now we can analyze that we
  • 00:34:33
    have these large you know nearly
  • 00:34:35
    complete databases and what you see is
  • 00:34:38
    that deadly force use again is strongly
  • 00:34:40
    tied to physical threats from citizens
  • 00:34:44
    so somewhere between 85 and 90 percent
  • 00:34:46
    of citizens who are fatally shot are
  • 00:34:48
    actually armed at the time of being shot
  • 00:34:52
    out of the roughly eight hundred black
  • 00:34:56
    or white citizens so again about a
  • 00:34:57
    thousand are shot every citizens are
  • 00:34:59
    shot every year there were eleven
  • 00:35:00
    hundred and twenty fifteen of those
  • 00:35:02
    eight hundred are black or white
  • 00:35:04
    citizens if you look at those black or
  • 00:35:07
    white citizens in the US who are fatally
  • 00:35:09
    shot only about 45 or so per year could
  • 00:35:13
    be described or classified as being both
  • 00:35:16
    unarmed and not aggressing against an
  • 00:35:19
    officer at the time of being shot okay
  • 00:35:21
    so in the phalangeal Casteel case for
  • 00:35:24
    example that would be one of those cases
  • 00:35:27
    in some of the smaller data sets that
  • 00:35:30
    were analyzed prior to the the 2015
  • 00:35:34
    year's 2015 2016 the those research were
  • 00:35:37
    finding the same sorts of things roughly
  • 00:35:40
    90% of fatal shootings involved what
  • 00:35:43
    defined as a clear deadly threat again
  • 00:35:45
    someone has a gun they're currently
  • 00:35:47
    shooting at someone so on and so forth
  • 00:35:50
    and so it is a case that when you
  • 00:35:53
    analyze fatal police shootings you do
  • 00:35:54
    see that officers are overwhelmingly
  • 00:35:56
    shocked in crime related situations okay
  • 00:36:00
    and almost always shoot the person who
  • 00:36:02
    is involved in criminal activity
  • 00:36:05
    mistakes do happen and we'll talk about
  • 00:36:07
    those but innocent bystanders being shot
  • 00:36:09
    by the police is really quite rare okay
  • 00:36:15
    so if police are more likely to use
  • 00:36:18
    deadly force in violent crime related
  • 00:36:22
    situations okay where violent crime is
  • 00:36:25
    currently happening or where they've
  • 00:36:27
    showed up to a scene where it's
  • 00:36:28
    suspected to have been happening okay if
  • 00:36:31
    that's the case then the question that
  • 00:36:34
    we want to know okay if we're going to
  • 00:36:38
    use the population proportion as a
  • 00:36:39
    benchmark is to know whether citizens
  • 00:36:42
    from different groups are in those
  • 00:36:44
    situations at the same rates okay so is
  • 00:36:47
    it the case that black and white
  • 00:36:48
    citizens and when again we could look at
  • 00:36:50
    the full range of data we have data on
  • 00:36:52
    Hispanic and Asian citizens as well okay
  • 00:36:54
    but what we want to know again for this
  • 00:36:56
    for the purposes of what we're analyzing
  • 00:36:57
    is it the case that black and white
  • 00:37:00
    citizens are involved in violent crime
  • 00:37:02
    situations to the same degree okay and
  • 00:37:06
    this is a very complicated let's just
  • 00:37:09
    appreciate the complicated nature of why
  • 00:37:12
    this is true okay nobody's saying that
  • 00:37:14
    there's a simple answer for why this is
  • 00:37:15
    true or a simple solution for it okay
  • 00:37:18
    but it is very well-known that people
  • 00:37:20
    from different racial groups don't
  • 00:37:22
    occupy violent crime situations at equal
  • 00:37:25
    rates okay and so the data are just
  • 00:37:28
    enormous ly clear on this there's no
  • 00:37:30
    question at all about it if you look to
  • 00:37:32
    the data and we'll talk about police
  • 00:37:34
    bias in arrests and reporting's also in
  • 00:37:37
    a little bit okay
  • 00:37:38
    but as just one example you could look
  • 00:37:40
    at the Centers for Disease Control data
  • 00:37:43
    Centers for Disease Control have nothing
  • 00:37:45
    to do with police data the police don't
  • 00:37:47
    report the data to them these are
  • 00:37:48
    autopsy reports okay and what we can do
  • 00:37:51
    is look because we know that over 90% of
  • 00:37:54
    any death
  • 00:37:56
    if it's a if it's a murder and not
  • 00:37:58
    negligent manslaughter is within race we
  • 00:38:01
    can get a pretty good understanding of
  • 00:38:02
    who suspects are based on who victims
  • 00:38:05
    are okay and in 2015 for example if you
  • 00:38:07
    just look at all assaults and so these
  • 00:38:10
    are raw numbers in terms of all assaults
  • 00:38:12
    white citizens dying again almost always
  • 00:38:15
    at the hands of another white citizen
  • 00:38:17
    there were 5000 cases for black citizens
  • 00:38:20
    again dying at the hands of black
  • 00:38:21
    citizens was almost 9,000 cases okay
  • 00:38:25
    in 2020 15 if you look at death by
  • 00:38:28
    firearm discharge specifically so all
  • 00:38:31
    assaults is any kind of assault if you
  • 00:38:33
    look at just firearm deaths those
  • 00:38:34
    numbers are even more dramatically
  • 00:38:36
    different
  • 00:38:37
    okay 3,000 versus 7,500 and again those
  • 00:38:40
    are raw numbers so when you actually
  • 00:38:42
    look at a per capita rate the difference
  • 00:38:45
    in in violent crime experience is
  • 00:38:47
    massive for black and white citizens
  • 00:38:49
    okay there's a very strong difference
  • 00:38:51
    again this is victimization in terms of
  • 00:38:53
    them being victimized but because we
  • 00:38:55
    know that almost all of the are about 90
  • 00:38:57
    percent or so of the suspects in those
  • 00:39:00
    cases are the same race we can get a
  • 00:39:02
    good sense of what the suspects are also
  • 00:39:05
    okay to just be clear this is this a
  • 00:39:09
    really important point
  • 00:39:10
    before we move on also that the number
  • 00:39:13
    of people who do things like commit
  • 00:39:15
    murder is unbelievably low okay and so
  • 00:39:19
    it's useful when we talk about crime and
  • 00:39:21
    race to remind ourselves that almost
  • 00:39:24
    everybody from every group is exactly
  • 00:39:26
    the same okay it doesn't matter what
  • 00:39:28
    your racial group is you're not going to
  • 00:39:30
    commit murder right and so if you look
  • 00:39:31
    at this this is arrest rates for black
  • 00:39:33
    and white citizens all right point zero
  • 00:39:36
    zero two and point zero one percent of
  • 00:39:40
    black and white citizens are arrested
  • 00:39:43
    for murder and nine others remain sorry
  • 00:39:45
    you can't see it but this is actually a
  • 00:39:46
    pie chart with that sliver cut out of it
  • 00:39:49
    okay that's how small of a group that is
  • 00:39:52
    right the number of people who would
  • 00:39:54
    commit murder is unbelievably low black
  • 00:39:58
    and white citizens are the same 99 you
  • 00:40:01
    know above 99 percent of them okay we're
  • 00:40:04
    all the same right but there is a
  • 00:40:06
    difference there is a probabilistic
  • 00:40:08
    difference
  • 00:40:09
    and in that difference is bigger when
  • 00:40:11
    you know there's a larger slice of the
  • 00:40:13
    pie when you look at violent crime in
  • 00:40:14
    general rather than just murder but
  • 00:40:16
    there is an important difference there
  • 00:40:21
    okay well would skip this slide and move
  • 00:40:25
    on to this okay so as we said then
  • 00:40:27
    because black and white citizens are not
  • 00:40:30
    involved in violent crime at the same
  • 00:40:33
    rates okay in terms of being in line
  • 00:40:35
    with their population levels this is
  • 00:40:37
    gonna make the population benchmark
  • 00:40:39
    misleading okay it's like looking at all
  • 00:40:42
    people instead of cancer patients right
  • 00:40:44
    we want the group of people for whom the
  • 00:40:46
    outcome is actually relevant okay and so
  • 00:40:49
    the more informative question is
  • 00:40:50
    actually our black or white citizens
  • 00:40:53
    over-represented and in fatal police
  • 00:40:56
    shootings given each groups involvement
  • 00:40:58
    in violent crime because that's when
  • 00:41:00
    fatal police shootings happen okay or
  • 00:41:02
    said differently given the rates at
  • 00:41:05
    which black and white citizens are
  • 00:41:07
    exposed to the police okay
  • 00:41:09
    in those situations because remember you
  • 00:41:11
    can't experience a policing outcome
  • 00:41:13
    without being exposed to the police and
  • 00:41:16
    then is it the case that officers are
  • 00:41:18
    more likely to shoot black citizens okay
  • 00:41:21
    now the problem with this of course is
  • 00:41:24
    we don't know any groups racial or any
  • 00:41:27
    groups crime rate in in reality the true
  • 00:41:30
    crime rate is unknown of any group okay
  • 00:41:33
    what we can do is try to look at as many
  • 00:41:35
    different pieces of data as possible and
  • 00:41:37
    then see whether they're all telling us
  • 00:41:39
    the same answer and and so what I'm
  • 00:41:43
    gonna show you is some first some data
  • 00:41:46
    there are more data here and I'll
  • 00:41:47
    explain them as we get it but what we
  • 00:41:49
    can do to look at exposure first is we
  • 00:41:51
    can look at the FBI's two different FBI
  • 00:41:54
    sources and again we'll look at non
  • 00:41:55
    policing sources in a moment okay but we
  • 00:41:58
    can look at the FBI's summary report
  • 00:42:00
    system data okay on murder non-negligent
  • 00:42:03
    manslaughter rates alright our numbers
  • 00:42:06
    for both arrests and reports of those
  • 00:42:08
    events violent crime arrests violent
  • 00:42:11
    crime includes murder non-negligent
  • 00:42:13
    manslaughter rape robbery and assault
  • 00:42:16
    okay
  • 00:42:16
    and also weapons violations ok who's
  • 00:42:19
    carrying a gun illegally
  • 00:42:22
    and again this is being arrested for
  • 00:42:24
    that right we can also look at the
  • 00:42:26
    National Institute and that's our
  • 00:42:28
    National Incident based reporting system
  • 00:42:30
    also from the FBI and look at again
  • 00:42:32
    murder non-negligent manslaughter
  • 00:42:33
    violent crime and weapons violations so
  • 00:42:35
    these are overlapping but somewhat
  • 00:42:37
    distinct datasets of crime okay and what
  • 00:42:40
    we can do then is ask what are the odds
  • 00:42:42
    that a black or white citizen is shot
  • 00:42:44
    given their rates of violent crime of
  • 00:42:48
    that group okay
  • 00:42:49
    so again instead of saying given the
  • 00:42:50
    population proportion what do we know
  • 00:42:53
    given the rate at which different
  • 00:42:55
    members of different groups would
  • 00:42:56
    encounter the police in those situations
  • 00:42:59
    where the police might use deadly force
  • 00:43:01
    okay or for which deadly force would be
  • 00:43:03
    relevant okay and so these are all just
  • 00:43:05
    listed here the first panel is
  • 00:43:07
    benchmarking off of homicide data okay
  • 00:43:11
    what you're gonna see is on the y-axis
  • 00:43:12
    is the odds ratio sorry the clickers and
  • 00:43:15
    other pointers not working and I'm not
  • 00:43:17
    tall the odds ratio one would mean that
  • 00:43:20
    there's an equal likelihood of a black
  • 00:43:22
    or a white citizen being shot okay
  • 00:43:24
    numbers below mean black citizens would
  • 00:43:26
    be more likely numbers above would mean
  • 00:43:28
    white citizens would be more likely okay
  • 00:43:30
    so again where we saw before on the
  • 00:43:32
    population level a two point five odds
  • 00:43:34
    of black citizens being 2.5 times more
  • 00:43:37
    likely to be shot that would be down
  • 00:43:39
    here the bar would be low to a below one
  • 00:43:42
    okay well when we benchmark and set it
  • 00:43:45
    on homicide data actually that effect
  • 00:43:48
    goes away okay and in indeed it flips to
  • 00:43:51
    the other side where black citizens are
  • 00:43:53
    not more likely to be shot than white
  • 00:43:55
    citizens given each groups involvement
  • 00:43:57
    in homicide okay we can do the same
  • 00:44:00
    thing benchmarking on violent crime in
  • 00:44:02
    general and there's a number of
  • 00:44:03
    different calculations of violent crime
  • 00:44:05
    that you can do they're all up there so
  • 00:44:07
    you can see them what you could see is
  • 00:44:08
    in no case do you see again evidence of
  • 00:44:11
    anti-black disparity in police shootings
  • 00:44:13
    once you account for violent crime rates
  • 00:44:16
    then and again the same is true when we
  • 00:44:18
    look at weapons violation data so again
  • 00:44:21
    this is asking given the rates at which
  • 00:44:24
    black and white citizens are arrested
  • 00:44:27
    for or reported carrying illegal weapons
  • 00:44:30
    okay we again don't see anti black
  • 00:44:33
    disparity in
  • 00:44:34
    fatal police shootings now an obvious
  • 00:44:38
    problem here is if the data themselves
  • 00:44:41
    are biased okay then that might mask any
  • 00:44:44
    actual bias on the part of the police so
  • 00:44:47
    if it's just the case that police are
  • 00:44:48
    more likely to let's say pat-down and
  • 00:44:51
    arrest black citizens relative to white
  • 00:44:53
    citizens then the whole thing is wrong
  • 00:44:55
    because there it's gonna be bias already
  • 00:44:57
    built into the data okay and and that
  • 00:44:59
    would mask anti black disparities on the
  • 00:45:03
    part of the police making deadly force
  • 00:45:05
    decisions what we can do to account for
  • 00:45:07
    that is to look at two additional
  • 00:45:09
    sources of data okay one as I said is
  • 00:45:12
    the CDC death data okay so these are
  • 00:45:15
    data from the Centers for Disease
  • 00:45:16
    Control on whether people have died in
  • 00:45:19
    to death by assault those are not
  • 00:45:22
    subject to police bias right so we know
  • 00:45:24
    that these are as much as we can a quote
  • 00:45:27
    pure count of homicide rates for
  • 00:45:30
    different from black and white citizens
  • 00:45:32
    we can also look at the National the
  • 00:45:36
    National victimization survey sorry
  • 00:45:38
    blanked on what the National
  • 00:45:41
    victimization survey okay
  • 00:45:43
    is a nationwide representative survey
  • 00:45:46
    it's a telephone survey people are
  • 00:45:48
    called up every year and asked were you
  • 00:45:50
    victimized by somebody in the last year
  • 00:45:52
    if so tell us about it
  • 00:45:53
    okay so again that's a self-report if
  • 00:45:56
    you were robbed you would tell the
  • 00:45:58
    person on the phone yeah I was robbed by
  • 00:45:59
    a you know male in the middle of the
  • 00:46:01
    night and so on so again those are these
  • 00:46:04
    are not contaminated by police data or
  • 00:46:07
    police bias and what we see when we do
  • 00:46:10
    that again is little to no evidence for
  • 00:46:12
    anti black disparities and being shocked
  • 00:46:15
    by the police the CDC death data look
  • 00:46:18
    like all the other homicide data the
  • 00:46:20
    National Crime Victimization survey data
  • 00:46:22
    are right there on no bias which is
  • 00:46:25
    again the odds ratio of one and same
  • 00:46:27
    with the reports of having a weapon used
  • 00:46:29
    against you when we benchmark on that
  • 00:46:32
    again it looks at one
  • 00:46:34
    now these are as you can see at the top
  • 00:46:37
    there it says all fatal shootings these
  • 00:46:40
    are all cases where anyone was fatally
  • 00:46:42
    shot by the police right what we want to
  • 00:46:45
    do though is ask is it the case that
  • 00:46:47
    maybe a police aren't biased in all
  • 00:46:49
    shootings okay maybe they're biased when
  • 00:46:52
    it comes to things like the
  • 00:46:54
    misidentification of a wallet for a gun
  • 00:46:57
    okay or in cases where it's more
  • 00:46:59
    ambiguous right perhaps when someone has
  • 00:47:02
    a gun out and they're firing it at the
  • 00:47:04
    officer they're gonna get shot no matter
  • 00:47:06
    what the race is but in those ambiguous
  • 00:47:08
    cases maybe that's where we see bias and
  • 00:47:11
    and so we can do that first by looking
  • 00:47:13
    at cases where someone is killed when
  • 00:47:18
    they're unarmed and they're not
  • 00:47:20
    physically attacking an officer those
  • 00:47:22
    would be more ambiguous cases there's a
  • 00:47:24
    hundred of those across the two-year
  • 00:47:26
    period and now what you see okay is that
  • 00:47:28
    there's a little bit more evidence for
  • 00:47:30
    anti-black bias
  • 00:47:31
    okay anti-black disparities some of
  • 00:47:33
    those numbers are starting to go below
  • 00:47:34
    one and what you also see is those bars
  • 00:47:37
    the sorry the lines above and below the
  • 00:47:40
    the odds ratios get very big okay and
  • 00:47:43
    we'll talk about that in in just a
  • 00:47:45
    second when we look at those cases where
  • 00:47:48
    someone is killed while they are holding
  • 00:47:50
    or reaching for an object they're
  • 00:47:52
    reaching for their waistband or they
  • 00:47:53
    take out their wallet and it's mistaken
  • 00:47:55
    for gun again those are 45 over the
  • 00:47:57
    two-year period that we analyzed here
  • 00:48:00
    again you start to see a little bit of
  • 00:48:02
    potential anti-black disparity but now
  • 00:48:04
    those measures of uncertainty are huge
  • 00:48:06
    okay and what that's going to do is
  • 00:48:09
    that's going to reflect the fact that
  • 00:48:10
    once we start to ask about these more
  • 00:48:12
    specific questions there just isn't a
  • 00:48:14
    lot of data there okay we really don't
  • 00:48:17
    know the answer of whether police are
  • 00:48:19
    more racially biased when it comes to
  • 00:48:22
    identifying a gun in a mistaken they
  • 00:48:24
    mistake a wallet for a gun because there
  • 00:48:26
    are just so few cases of that so it then
  • 00:48:30
    at this level we have to just be in a
  • 00:48:32
    place of uncertainty where we say we
  • 00:48:35
    don't really know whether there's racial
  • 00:48:36
    bias on the part of the police it might
  • 00:48:38
    be the case it looks like there might be
  • 00:48:40
    some evidence for that but there's just
  • 00:48:42
    so much uncertainty in what we can know
  • 00:48:44
    and that's not true for all the
  • 00:48:46
    shootings when we look at all the
  • 00:48:47
    shootings
  • 00:48:48
    a lot of data there we know what the
  • 00:48:50
    answer is it doesn't look like there's
  • 00:48:51
    racial disparities a racial bias on the
  • 00:48:54
    part of police okay once we get to this
  • 00:48:56
    level we're just at a place of
  • 00:48:58
    uncertainty so if we summarize that we
  • 00:49:04
    asked our black or white citizens
  • 00:49:06
    over-represented in fatal police
  • 00:49:07
    shootings not given their population
  • 00:49:09
    levels but given their and vile
  • 00:49:11
    involvement in violent crime overall and
  • 00:49:14
    by overall I mean when we look at all
  • 00:49:16
    the shootings from 2015 and 2016 okay
  • 00:49:20
    which is about 1,500 shootings total it
  • 00:49:22
    does not look like there's anti black
  • 00:49:24
    disparity in those shootings okay what
  • 00:49:28
    this means actually is that even if it
  • 00:49:31
    was the case that officers were
  • 00:49:33
    completely blind to citizen race okay
  • 00:49:36
    even if they somehow couldn't know what
  • 00:49:39
    a citizen's race was we are still gonna
  • 00:49:42
    see a population level disparity I am
  • 00:49:45
    they're still going to be a population
  • 00:49:47
    level disparity with black citizens
  • 00:49:49
    being more likely to be shot given their
  • 00:49:51
    population values because of the greater
  • 00:49:54
    exposure into crime once we get into
  • 00:49:59
    specific types of shootings shootings of
  • 00:50:02
    unarmed citizens the data are just too
  • 00:50:04
    uncertain okay we just don't have an
  • 00:50:06
    answer to that question right now and I
  • 00:50:08
    know generally people don't like
  • 00:50:10
    uncertainty and don't like to be left
  • 00:50:11
    with uncertainty but that really is the
  • 00:50:13
    state of where those data are when we
  • 00:50:15
    look at how and uncertain the estimates
  • 00:50:18
    are of those odds
  • 00:50:21
    because the data are consistent when we
  • 00:50:24
    look at the CDC data for example it's
  • 00:50:27
    unlikely that initial contact bias has a
  • 00:50:31
    lot to do with those numbers okay so we
  • 00:50:35
    are doing another analysis right now
  • 00:50:36
    where we're separating all the shootings
  • 00:50:38
    by those that start with a discretionary
  • 00:50:40
    decision by the officer pull somebody
  • 00:50:43
    over for a traffic violation versus
  • 00:50:45
    those that don't
  • 00:50:46
    with like 911 response calls and we'll
  • 00:50:49
    see what the data look like but most of
  • 00:50:51
    the police shootings are these kinds of
  • 00:50:53
    non-discretionary mandatory responses on
  • 00:50:56
    the part of officers and the final thing
  • 00:51:02
    we can cut out here we is now about an
  • 00:51:05
    appropriate time to stop or what how it
  • 00:51:07
    is the time like so two three okay I
  • 00:51:10
    think we'll do one more thing then what
  • 00:51:14
    this also tells us is that the shootings
  • 00:51:16
    that we have of what we call unarmed non
  • 00:51:19
    aggressive citizens the phalangeal
  • 00:51:21
    Casteel case the amadou diallo case and
  • 00:51:23
    so on are exceptionally tragic but
  • 00:51:26
    they're also very rare okay they are not
  • 00:51:29
    the modal or the most common type of
  • 00:51:32
    police shooting that we that we have
  • 00:51:34
    then and then I'll skip all this just to
  • 00:51:38
    get into one last finding that we have
  • 00:51:42
    which we've looked at recently and
  • 00:51:44
    that's data on the question of officer
  • 00:51:47
    race okay so one thing that people have
  • 00:51:50
    wondered some people have speculated
  • 00:51:52
    that when it comes to shooting minority
  • 00:51:55
    citizens particularly black citizens
  • 00:51:57
    that white officers might be more likely
  • 00:51:59
    to shoot minority citizens than black
  • 00:52:02
    officers okay lots of cases of this in
  • 00:52:05
    the media for example the race of the
  • 00:52:08
    officer is often mentioned if the
  • 00:52:09
    officer is white okay on the other hand
  • 00:52:12
    people have argued that it isn't about
  • 00:52:14
    the race of the officer it's about sort
  • 00:52:16
    of the institution as a whole and that
  • 00:52:18
    officers you know we don't distinguish
  • 00:52:19
    between black or white officers when it
  • 00:52:21
    comes to these questions okay well up
  • 00:52:24
    until last year we actually were not
  • 00:52:28
    able to answer the question of whether
  • 00:52:29
    white officers are more likely than
  • 00:52:31
    black officers for example to shoot
  • 00:52:33
    minority citizens because again there's
  • 00:52:36
    no there are no data bases on this right
  • 00:52:39
    so even when there is a fatal shooting
  • 00:52:40
    the the local police departments are not
  • 00:52:43
    required to report the race of the
  • 00:52:45
    officer to the federal government right
  • 00:52:48
    so we just simply had no data actually
  • 00:52:50
    on the race of officers involved in
  • 00:52:52
    these kinds of shootings yeah so what we
  • 00:52:55
    did was we spent two years trying to
  • 00:52:58
    collect all of these data right we
  • 00:53:01
    looked at every shooting from 2019 so
  • 00:53:04
    again that's about a thousand or so
  • 00:53:05
    about 1,100 total shootings and we
  • 00:53:08
    contacted every Police Department that
  • 00:53:10
    was that had a fatal shooting in that
  • 00:53:12
    year and we got from those police
  • 00:53:14
    departments after
  • 00:53:15
    many many tries some police departments
  • 00:53:18
    were happy to give us the information
  • 00:53:19
    others it took a lot of effort to get
  • 00:53:21
    from them okay but we actually got the
  • 00:53:24
    officers race
  • 00:53:25
    the officers sex and the officers years
  • 00:53:28
    of experience for every officer that was
  • 00:53:30
    involved in a fatal police shooting from
  • 00:53:33
    that department okay and we have we got
  • 00:53:35
    about eighty per se to five percent of
  • 00:53:37
    all officer information which for these
  • 00:53:39
    kinds of things is actually very high to
  • 00:53:41
    be able to get 85% of data from police
  • 00:53:44
    departments that doesn't always happen
  • 00:53:45
    okay and what we were able to do is ask
  • 00:53:48
    okay we have this database of people who
  • 00:53:51
    were fatally shot right we know the race
  • 00:53:53
    of the people who were fatally shot and
  • 00:53:55
    then we could ask what among those
  • 00:53:58
    individuals who were fatally shot okay
  • 00:54:00
    what predicted or related to that
  • 00:54:03
    person's race so citizen one who was
  • 00:54:05
    shot let's say they were white okay was
  • 00:54:08
    it more likely that there was a white or
  • 00:54:10
    black or Hispanic officer on the scene
  • 00:54:12
    who shot them citizen two was black
  • 00:54:14
    wasn't more likely that they were would
  • 00:54:16
    that there was a black or white or
  • 00:54:17
    Hispanic officer on the scene who shot
  • 00:54:19
    them in so on okay we can skip that
  • 00:54:23
    question what we found actually when we
  • 00:54:26
    looked at that was that the race of the
  • 00:54:28
    police officer actually did not relate
  • 00:54:30
    to the race of the citizen shot okay so
  • 00:54:34
    it was not the case that white officers
  • 00:54:36
    were more likely to shoot black citizens
  • 00:54:38
    to fatally shoot black citizens and
  • 00:54:40
    black officers were in fact at just a
  • 00:54:43
    pure relationship level it was actually
  • 00:54:46
    the case that black officers were more
  • 00:54:47
    likely to have shot black citizens but
  • 00:54:49
    that was simply due to the demographics
  • 00:54:51
    of a County okay so in a County with a
  • 00:54:53
    lot of black citizens you're gonna have
  • 00:54:55
    a county with a lot of black officers
  • 00:54:56
    okay because officers are drawn from the
  • 00:54:58
    communities that they serve okay once
  • 00:55:01
    you control for that there's no
  • 00:55:02
    relationship between the race of the
  • 00:55:04
    police officer and the race of the
  • 00:55:06
    citizen who is shot in contrast what we
  • 00:55:10
    did find and again this is consistent
  • 00:55:12
    with what we saw in the in the other
  • 00:55:14
    work that I just presented is that local
  • 00:55:17
    or county level crime rates predicted
  • 00:55:20
    the race of a person shot in that County
  • 00:55:23
    okay and in predictive it quite strongly
  • 00:55:25
    so the greater number of crimes for
  • 00:55:28
    instance who were commit that we're
  • 00:55:29
    made by whites in a County made it more
  • 00:55:32
    likely that a person who is fatally shot
  • 00:55:34
    by the police was white okay
  • 00:55:36
    the greater number of crimes committed
  • 00:55:38
    by black citizens in a County made it
  • 00:55:40
    more likely that a black citizen was
  • 00:55:42
    shot and again in this data set we
  • 00:55:44
    expanded it to Hispanic citizens as well
  • 00:55:46
    the greater number of crimes committed
  • 00:55:48
    by Hispanics in a county the more likely
  • 00:55:50
    it was that a person was Hispanic was
  • 00:55:51
    shot or the person who shot was was
  • 00:55:53
    Hispanic okay
  • 00:55:55
    so again this gives further support to
  • 00:55:57
    the idea that violent crime is really
  • 00:55:59
    the context of fatal shootings and that
  • 00:56:02
    that's the only thing that we have to
  • 00:56:03
    consider when we're looking at policing
  • 00:56:05
    behavior and then I actually will stop
  • 00:56:09
    it there and I'm happy to answer any
  • 00:56:11
    more any any questions that people have
  • 00:56:13
    about the work or anything that we
  • 00:56:15
    covered so thank you very much
  • 00:56:19
    [Applause]
标签
  • tiroteos policiales
  • sesgo racial
  • Estados Unidos
  • crimen violento
  • bases de datos
  • disparidades raciales
  • investigación
  • exposición policial
  • contexto criminal
  • evidencia
  • percepción pública
  • legalidad