[go: up one dir, main page]

    Calendario de lanzamientosTop 250 películasPelículas más popularesBuscar películas por géneroTaquilla superiorHorarios y entradasNoticias sobre películasPelículas de la India destacadas
    Programas de televisión y streamingLas 250 mejores seriesSeries más popularesBuscar series por géneroNoticias de TV
    Qué verÚltimos trailersTítulos originales de IMDbSelecciones de IMDbDestacado de IMDbGuía de entretenimiento familiarPodcasts de IMDb
    EmmysSuperheroes GuideSan Diego Comic-ConSummer Watch GuideBest Of 2025 So FarDisability Pride MonthPremios STARmeterInformación sobre premiosInformación sobre festivalesTodos los eventos
    Nacidos un día como hoyCelebridades más popularesNoticias sobre celebridades
    Centro de ayudaZona de colaboradoresEncuestas
Para profesionales de la industria
  • Idioma
  • Totalmente compatible
  • English (United States)
    Parcialmente compatible
  • Français (Canada)
  • Français (France)
  • Deutsch (Deutschland)
  • हिंदी (भारत)
  • Italiano (Italia)
  • Português (Brasil)
  • Español (España)
  • Español (México)
Lista de visualización
Iniciar sesión
  • Totalmente compatible
  • English (United States)
    Parcialmente compatible
  • Français (Canada)
  • Français (France)
  • Deutsch (Deutschland)
  • हिंदी (भारत)
  • Italiano (Italia)
  • Português (Brasil)
  • Español (España)
  • Español (México)
Usar app
  • Elenco y equipo
  • Opiniones de usuarios
  • Preguntas Frecuentes
IMDbPro

Coded Bias

  • 2020
  • TV-MA
  • 1h 26min
CALIFICACIÓN DE IMDb
6.8/10
2.9 k
TU CALIFICACIÓN
Shalini Kantayya in Coded Bias (2020)
An exploration of the fallout of MIT Media Lab researcher Joy Buolamwini's startling discovery of racial bias in facial recognition algorithms.
Reproducir trailer2:28
1 video
2 fotos
Documentary

Agrega una trama en tu idiomaWhen MIT Media Lab researcher Joy Buolamwini discovers that facial recognition does not see dark-skinned faces accurately, she embarks on a journey to push for the first-ever U.S. legislatio... Leer todoWhen MIT Media Lab researcher Joy Buolamwini discovers that facial recognition does not see dark-skinned faces accurately, she embarks on a journey to push for the first-ever U.S. legislation against bias in algorithms that impact us all.When MIT Media Lab researcher Joy Buolamwini discovers that facial recognition does not see dark-skinned faces accurately, she embarks on a journey to push for the first-ever U.S. legislation against bias in algorithms that impact us all.

  • Dirección
    • Shalini Kantayya
  • Guionistas
    • Christopher Seward
    • Paul Rachman
    • Kurt Engfehr
  • Elenco
    • Joy Buolamwini
    • Meredith Broussard
    • Cathy O'Neil
  • Ver la información de producción en IMDbPro
  • CALIFICACIÓN DE IMDb
    6.8/10
    2.9 k
    TU CALIFICACIÓN
    • Dirección
      • Shalini Kantayya
    • Guionistas
      • Christopher Seward
      • Paul Rachman
      • Kurt Engfehr
    • Elenco
      • Joy Buolamwini
      • Meredith Broussard
      • Cathy O'Neil
    • 50Opiniones de los usuarios
    • 34Opiniones de los críticos
    • 73Metascore
  • Ver la información de producción en IMDbPro
    • Premios
      • 3 premios ganados y 6 nominaciones en total

    Videos1

    Official Trailer
    Trailer 2:28
    Official Trailer

    Fotos1

    Ver el cartel

    Elenco principal67

    Editar
    Joy Buolamwini
    Joy Buolamwini
    • Self - Ph.D. Candidate, MIT Media Lab
    Meredith Broussard
    Meredith Broussard
    • Self - Author, Artificial Unintelligence
    Cathy O'Neil
    Cathy O'Neil
    • Self - Author, Weapons of Math Destruction
    • (as Cathy O'Neil Ph.D.)
    Silkie Carlo
    Silkie Carlo
    • Self - Director, Big Brother Watch UK
    Zeynep Tüfekçi
    Zeynep Tüfekçi
    • Self - Author, Twitter and Tear Gas
    • (as Zeynep Tufekci Ph.D.)
    Amy Webb
    Amy Webb
    • Self - Futurist…
    Tranae Moran
    Tranae Moran
    • Self - Brooklyn Tenant
    Virginia Eubanks
    Virginia Eubanks
    • Self - Author, Automating Inequality
    • (as Virginia Eubanks Ph.D.)
    Icemae Downes
    Icemae Downes
    • Self - Brooklyn Tenant
    Ravi Naik
    Ravi Naik
    • Self - UK Human Rights Lawyer
    Deborah Raji
    Deborah Raji
    • Self - Research Fellow, Partnership on A.I.
    Timnit Gebru
    Timnit Gebru
    • Self - Technical Co-Lead, Ethical A.I. Team at Google
    • (as Timnit Gebru Ph.D.)
    Safiya Umoja Noble
    Safiya Umoja Noble
    • Self - Author, Algorithms of Oppression
    • (as Safiya Umoja Noble Ph.D.)
    Wolfie O'Neil
    Wolfie O'Neil
    • Self - Cathy's Son
    Kiri Soares
    Kiri Soares
    • Self - School Principal
    Daniel Santos
    Daniel Santos
    • Self - Middle School Teacher
    LaTonya Myers
    LaTonya Myers
    • Self - Criminal Justice Activist
    Mark Houldin
    Mark Houldin
    • Self - Lawyer
    • Dirección
      • Shalini Kantayya
    • Guionistas
      • Christopher Seward
      • Paul Rachman
      • Kurt Engfehr
    • Todo el elenco y el equipo
    • Producción, taquilla y más en IMDbPro

    Opiniones de usuarios50

    6.82.8K
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10

    Opiniones destacadas

    6keikoyoshikawa

    Is It A Conspiracy or Incompetence?

    Algorithms are not magical systems. At their core they are simply data. Feed your programs with rubbish or incomplete data sets, and you'll get rubbish or inaccurate outputs. What surprises me is not that this is happening with programs like facial recognition software; what surprises me is that the basics taught in any beginning programming class are somehow forgotten.

    That said, this documentary feels incomplete. It seems to be one-sided, with lots of interviews with people who are against the use of AI.

    But while the film-makers do an ok job of highlighting the dangers and inadequacies of AI systems such as facial recognition software, they failed to show what really is behind these glaring.blunders - was it some kind of knowing omission meant to create more biases, was it a case of software engineers creating something that they themselves don't understand and thus making a mess of things, or was it simply incompetence.on the part of many involved?

    Who knows. And that's the problem with this film.
    7cherold

    informative

    I've heard a lot about the various algorithmic failures around race. This is a good overview of the discussion that talks about some things I haven't seen before. When you see it put all together in one place it's pretty shocking.

    At the same time, purely as a documentary this is kind of weak. It's sometimes a little muddled, and it sometimes stretches a point a bit too far. Some of the things it tries to fold into the narrative are less examples of technological racism and more examples of actual criminal behavior. There's a difference between slippery tech and actions that resulted in people going to jail.

    Still, it's a compelling film.
    random-70778

    Does it matter that the assertions in this documentary have been debunked?

    Does it matter to any of the reviewers here that the claims made in this documentary have been debunked?

    Fact: The darker ones complexation is the LESS likely that there is usable video or photos for investigators or prosecutors.

    The makers of this film claim the opposite is the case, they claim there is a bias against persons with darker complexions -- when in fact that is not at all what the peer reviewed research shows.
    8schaeh

    Educational despite what the whiney 1 star reviewers say

    This was a good documentary making points we should all keep in mind.
    5MeadtheMan

    Powerful Messages. Poor Execution.

    The general messages conveyed are powerful, and there's no denying that we urgently need to regulate a technology that has encroached into every facet of our lives - it's like letting people drive without introducing any traffic laws.

    The execution of this documentary, however, is very underwhelming, to say the least. There are the usuals: catchy montages, TED-style interviews, news soundbites, and the most annoying of all - artificially created (pun intended) graphics of AI scanning data in a stereotypical digital font paired with silly sound effects which, unless the primary audience of this documentary is fifth graders, I don't understand why it's necessary to incessantly rehash them. And then there's the unimaginative 'robotic voice.' It's just puerile.

    Maybe the producers are wary that people still won't get the danger of unregulated AI without these gimmicks. But I'd argue that people would be more alarmed to learn how AI has been infiltrating and affecting our lives in the least expected ways. If the documentary can clearly point out the potential harms as a consequence, I think people will naturally find the lack of regulation disturbing, no silly visuals and sound effects are needed. Sometimes I think they actually undermine the severity of potential danger at hand. For example, the scene where a teenager is mistakenly stopped by plainclothes police, instead of being accompanied with yet another piece of cheesy soundtrack meant to suggest danger, it would be so much more powerful if everything is just eerily silent.

    And the interviews and info - yes, AI is like a black box even to the programmers, but can you explain it in layman's terms so that people get it? - could be a lot more insightful. Even some short Vox-style Youtube clips have explored these issues in greater depth.

    The themes explored are a bit all over the place too. I get it this domain is relatively new, so the vocabulary and focus aren't that streamlined yet, still... Sometimes the documentary brings up issues of obvious biases, which is consistent with the title, but sometimes we don't even know what the problem is, it's simply an issue of things being completely nontransparent and/or unverified by a third party. The China parts are also a little disjointed from the rest of the documentary and the country itself is painted in broad strokes - it's as if we can't do good until we can identify the bad guy to feel good about ourselves.

    Más como esto

    Órbita 9
    5.9
    Órbita 9
    Layla M.
    6.6
    Layla M.
    Nada es privado
    7.0
    Nada es privado
    Do You Trust This Computer?
    7.3
    Do You Trust This Computer?
    El dilema de las redes sociales
    7.6
    El dilema de las redes sociales
    The Edge of All We Know
    6.6
    The Edge of All We Know
    Bigger Than Africa
    6.9
    Bigger Than Africa
    City of Ghosts
    7.4
    City of Ghosts
    The Internet's Own Boy: The Story of Aaron Swartz
    8.0
    The Internet's Own Boy: The Story of Aaron Swartz
    ¿Y ahora qué? El futuro según Bill Gates
    6.2
    ¿Y ahora qué? El futuro según Bill Gates
    Recursos inhumanos
    7.2
    Recursos inhumanos
    Terms and Conditions May Apply
    7.3
    Terms and Conditions May Apply

    Argumento

    Editar

    ¿Sabías que…?

    Editar
    • Citas

      Self - Author, Weapons of Math Destruction: On internet advertising as data scientists, we are competing for eyeballs on one hand, but really we're competing for eyeballs of rich people. And then, the poor people, who's competing for their eyeballs? Predatory industries. So payday lenders, or for-profit colleges, or Caesars Palace. Like, really predatory crap.

    • Conexiones
      Featured in Jeremy Vine: Episode #4.95 (2021)

    Selecciones populares

    Inicia sesión para calificar y agrega a la lista de videos para obtener recomendaciones personalizadas
    Iniciar sesión

    Preguntas Frecuentes

    • How long is Coded Bias?
      Con tecnología de Alexa

    Detalles

    Editar
    • Fecha de lanzamiento
      • 11 de noviembre de 2020 (Estados Unidos)
    • Países de origen
      • Estados Unidos
      • China
      • Reino Unido
    • Sitios oficiales
      • Facebook
      • Instagram
    • Idioma
      • Inglés
    • También se conoce como
      • Kodlanmış Önyargı
    • Locaciones de filmación
      • Brooklyn, Nueva York, Nueva York, Estados Unidos
    • Productoras
      • 7th Empire Media
      • Chicken And Egg Pictures
      • Ford Foundation - Just Films
    • Ver más créditos de la compañía en IMDbPro

    Taquilla

    Editar
    • Total en EE. UU. y Canadá
      • USD 10,236
    • Fin de semana de estreno en EE. UU. y Canadá
      • USD 10,236
      • 15 nov 2020
    • Total a nivel mundial
      • USD 10,236
    Ver la información detallada de la taquilla en IMDbPro

    Especificaciones técnicas

    Editar
    • Tiempo de ejecución
      1 hora 26 minutos
    • Color
      • Color

    Contribuir a esta página

    Sugiere una edición o agrega el contenido que falta
    Shalini Kantayya in Coded Bias (2020)
    Principales brechas de datos
    What is the Spanish language plot outline for Coded Bias (2020)?
    Responda
    • Ver más datos faltantes
    • Obtén más información acerca de cómo contribuir
    Editar página

    Más para explorar

    Visto recientemente

    Habilita las cookies del navegador para usar esta función. Más información.
    Obtener la aplicación de IMDb
    Inicia sesión para obtener más accesoInicia sesión para obtener más acceso
    Sigue a IMDb en las redes sociales
    Obtener la aplicación de IMDb
    Para Android e iOS
    Obtener la aplicación de IMDb
    • Ayuda
    • Índice del sitio
    • IMDbPro
    • Box Office Mojo
    • Licencia de datos de IMDb
    • Sala de prensa
    • Publicidad
    • Trabaja con nosotros
    • Condiciones de uso
    • Política de privacidad
    • Your Ads Privacy Choices
    IMDb, una compañía de Amazon

    © 1990-2025 by IMDb.com, Inc.