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Automating Inequality, with Virginia Eubanks

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Conteúdo fornecido por University at Albany, State University of New York and UAlbany Media Relations. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por University at Albany, State University of New York and UAlbany Media Relations ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

Virginia Eubanks is an associate professor of political science at UAlbany's Rockefeller College of Public Affairs & Policy. On this episode of the UAlbany News Podcast, Eubanks shares about her book, 'Automating Inequality: How High-Tech Tools Profile, Police and Punish the Poor.'

In the book, she details three examples of technology failing to streamline welfare programs:

• an effort to automate eligibility processes for public assistance programs in Indiana
•an electronic registry of the homeless in California
•a statistical model in Pennsylvania that attempts to predict child maltreatment

These automated public service systems are designed to serve some of the country’s most vulnerable populations, such as those living in poverty or contending with poor health, while at the same time saving the government time and money. But these technologies can leave poor families feeling tracked, targeted and trapped.

Eubanks explains how these systems fail to remove human bias, exacerbate inequality and perpetuate a "Digital Poorhouse" for working-class people in America.

The UAlbany News Podcast is hosted and produced by Sarah O'Carroll, a Communications Specialist at the University at Albany, State University of New York, with production assistance by Patrick Dodson and Scott Freedman.

Have a comment or question about one of our episodes? You can email us at mediarelations@albany.edu, and you can find us on Twitter @UAlbanyNews.

  continue reading

40 episódios

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iconCompartilhar
 
Manage episode 230629514 series 2447553
Conteúdo fornecido por University at Albany, State University of New York and UAlbany Media Relations. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por University at Albany, State University of New York and UAlbany Media Relations ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

Virginia Eubanks is an associate professor of political science at UAlbany's Rockefeller College of Public Affairs & Policy. On this episode of the UAlbany News Podcast, Eubanks shares about her book, 'Automating Inequality: How High-Tech Tools Profile, Police and Punish the Poor.'

In the book, she details three examples of technology failing to streamline welfare programs:

• an effort to automate eligibility processes for public assistance programs in Indiana
•an electronic registry of the homeless in California
•a statistical model in Pennsylvania that attempts to predict child maltreatment

These automated public service systems are designed to serve some of the country’s most vulnerable populations, such as those living in poverty or contending with poor health, while at the same time saving the government time and money. But these technologies can leave poor families feeling tracked, targeted and trapped.

Eubanks explains how these systems fail to remove human bias, exacerbate inequality and perpetuate a "Digital Poorhouse" for working-class people in America.

The UAlbany News Podcast is hosted and produced by Sarah O'Carroll, a Communications Specialist at the University at Albany, State University of New York, with production assistance by Patrick Dodson and Scott Freedman.

Have a comment or question about one of our episodes? You can email us at mediarelations@albany.edu, and you can find us on Twitter @UAlbanyNews.

  continue reading

40 episódios

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