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    2020 Nov 13

    Reckoning with Election 2020: Race, Violence, & the Power of New Voters

    Registration Closed 3:00pm to 4:00pm

    Location: 

    Virtual Event (Registration Required)

    Election 2020 proved to be historic in terms of the numbers of voters that were mobilized to the polls. The Election also made clear that there would be no nationwide repudiation of Trumpism. Join the Carr Center for a conversation with leading scholars of racial politics about the election turnout, voter suppression, and what this means about the state of democracy today. This event is part of the Bending the Arc: A Talk Series with Dr. Megan Ming Francis. This event will be co-sponsored by the Ash Center for Democratic Governance and Innovation. 

    ... Read more about Reckoning with Election 2020: Race, Violence, & the Power of New Voters

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    Mass Incarceration and The Future: An Urgent Need to Address the Human Rights Implications of Criminal Background Checks and the Future of Artificial Intelligence
    Teresa Y. Hodge and Laurin Leonard. 7/17/2020. “Mass Incarceration and The Future: An Urgent Need to Address the Human Rights Implications of Criminal Background Checks and the Future of Artificial Intelligence.” Carr Center Discussion Paper Series, 2020-009. See full text.Abstract
    Between 70 and 100 million Americans—one in three— currently live with a criminal record. This number is expected to rise above 100 million by the year 2030.

    The criminal justice system in the U.S. has over-incarcerated its citizen base; we have 5% of the world's population but 25% of the world's prison population. America became known as the “incarceration nation” because our prison and jail population exploded from less than 200,000 in 1972 to 2.2 million today, which became a social phenomenon known as mass incarceration. And along the way, there was a subsequent boom in querying databases for data on citizens with criminal records.

    Once a person comes in contact with the U.S. criminal justice system, they begin to develop an arrest and/or conviction record. This record includes data aggregated from various databases mostly, if not exclusively, administered by affiliated government agencies. As the prison population grew, the number of background check companies rose as well. The industry has grown and continues to do so with very little motivation to wrestle with morality, data integrity standards, or the role of individual rights.

    This paper address the urgent need to look towards a future where background screening decisions and artificial intelligence collide.

    Read full paper here. 

     

    From Rationality to Relationality: Ubuntu as an Ethical and Human Rights Framework for Artificial Intelligence Governance
    Sabelo Mhlambi. 7/8/2020. “From Rationality to Relationality: Ubuntu as an Ethical and Human Rights Framework for Artificial Intelligence Governance.” Carr Center Discussion Paper Series, 2020-009. See full text.Abstract

    What is the measure of personhood and what does it mean for machines to exhibit human-like qualities and abilities? Furthermore, what are the human rights, economic, social, and political implications of using machines that are designed to reproduce human behavior and decision making? The question of personhood is one of the most fundamental questions in philosophy and it is at the core of the questions, and the quest, for an artificial or mechanical personhood. 

    The development of artificial intelligence has depended on the traditional Western view of personhood as rationality. However, the traditional view of rationality as the essence of personhood, designating how humans, and now machines, should model and approach the world, has always been marked by contradictions, exclusions, and inequality. It has shaped Western economic structures (capitalism’s free markets built on colonialism’s forced markets), political structures (modernity’s individualism imposed through coloniality), and discriminatory social hierarchies (racism and sexism as institutions embedded in enlightenment-era rationalized social and gender exclusions from full person status and economic, political, and social participation), which in turn shape the data, creation, and function of artificial intelligence. It is therefore unsurprising that the artificial intelligence industry reproduces these dehumanizations. Furthermore, the perceived rationality of machines obscures machine learning’s uncritical imitation of discriminatory patterns within its input data, and minimizes the role systematic inequalities play in harmful artificial intelligence outcomes.

    Read the full paper.

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