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The Algorithmic Auditing Trap

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This op-ed was written by Mona Sloane, a sociologist and senior research scientist at the NYU Center for Responsible A.I. and a fellow at the NYU Institute for Public Knowledge. Her work focuses on design and inequality in the context of algorithms and artificial intelligence. We have a new A.I. race on our hands: the race to define and steer what it means to audit algorithms. Governing bodies know that they must come up with solutions to the disproportionate harm algorithms can inflict. This technology has disproportionate impacts on racial minorities, the economically disadvantaged, womxn, and people with disabilities, with applications ranging from health care to welfare, hiring, and education.


Bias in facial recognition isn't hard to discover, but it's hard to get rid of

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Joy Buolamwini is a researcher at the MIT Media Lab who pioneered research into bias that's built into artificial intelligence and facial recognition. And the way she came to this work is almost a little too on the nose. As a graduate student at MIT, she created a mirror that would project aspirational images onto her face, like a lion or tennis star Serena Williams. But the facial-recognition software she installed wouldn't work on her Black face, until she literally put on a white mask. Buolamwini is featured in a documentary called "Coded Bias," airing tonight on PBS.


We need to hold algorithms accountable--here's how to do it

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Algorithms are now used throughout the public and private sectors, informing decisions on everything from education and employment to criminal justice. But despite the potential for efficiency gains, algorithms fed by big data can also amplify structural discrimination, produce errors that deny services to individuals, or even seduce an electorate into a false sense of security. Indeed, there is growing awareness that the public should be wary of the societal risks posed by over-reliance on these systems and work to hold themaccountable. Various industry efforts, including a consortium of Silicon Valley behemoths, are beginning to grapple with the ethics of deploying algorithms that can have unanticipated effects on society. Algorithm developers and product managers need new ways to think about, design, and implement algorithmic systems in publicly accountable ways.


We need to hold algorithms accountable--here's how to do it

#artificialintelligence

Algorithms are now used throughout the public and private sectors, informing decisions on everything from education and employment to criminal justice. But despite the potential for efficiency gains, algorithms fed by big data can also amplify structural discrimination, produce errors that deny services to individuals, or even seduce an electorate into a false sense of security. Indeed, there is growing awareness that the public should be wary of the societal risks posed by over-reliance on these systems and work to hold themaccountable. Various industry efforts, including a consortium of Silicon Valley behemoths, are beginning to grapple with the ethics of deploying algorithms that can have unanticipated effects on society. Algorithm developers and product managers need new ways to think about, design, and implement algorithmic systems in publicly accountable ways.


We Need Bug Bounties for Bad Algorithms

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Amit Elazari Bar On is a doctoral law candidate (J.S.D.) at UC Berkeley School of Law and a CLTC (Center for Long-Term Cybersecurity) Grantee, Berkeley School of Information, as well as a member of AFOG, Algorithmic Fairness and Opacity Working Group at Berkeley. On 2017, Amit was a CTSP Fellow. We are told opaque algorithms and black-boxes are going to control our world, shaping every aspect of our life. They warn us that without accountability and transparency, and generally without better laws, humanity is doomed to a future of machine-generated bias and deception. From calls to open-the-black box to the limitations of explanations of inscrutable machine-learning models, the regulation of algorithms is one of the most pressing policy concerns in today's digital society.