{"id":58819,"date":"2019-05-13T08:00:59","date_gmt":"2019-05-13T12:00:59","guid":{"rendered":"http:\/\/www.bu.edu\/law\/?p=58819"},"modified":"2025-07-14T12:10:48","modified_gmt":"2025-07-14T16:10:48","slug":"bu-law-faculty-and-students-take-on-algorithmic-bias","status":"publish","type":"bu-article","link":"https:\/\/www.bu.edu\/law\/record\/articles\/2019\/bu-law-faculty-and-students-take-on-algorithmic-bias\/","title":{"rendered":"BU Law Faculty and Students Take on Algorithmic Bias"},"content":{"rendered":"

Combating such bias is at the heart of the Technology Law Clinic\u2019s work and a central aspect of several other courses and initiatives at BU Law.<\/h2>\n
\"Technology
Technology Law Clinic client Joy Buolamwini used a white mask to demonstrate the shortcomings of artificial intelligence-powered gender classification products.<\/figcaption><\/figure>\n

The white mask<\/a> that Technology Law Clinic<\/a> client Joy Buolamwini wore as part of her inquiry into artificial intelligence-powered gender classification products will soon be on display at the Barbican Centre<\/a> in London, but Boston University School of Law students got an up-close-and-personal look last fall.<\/p>\n

That\u2019s because clinic students, under the guidance of Director Andrew Sellars<\/strong>, represented Buolamwini in her study of the facial analysis technology, called Gender Shades<\/a>. They helped her make sure her research didn\u2019t run afoul of the Computer Fraud and Abuse Act, and they helped notify the relevant companies of the results in advance to give them a chance to address the products\u2019 shortcomings, including poor recognition of people of color and those with traditionally feminine facial features.<\/p>\n

The study is a stark example of the algorithmic bias that has taken root in so much of the technology that runs through our daily lives, directing the online ads we see for products or jobs or housing, determining the care we receive from government agencies, and even shaping how law enforcement agencies allocate crime-prevention resources.<\/p>\n

Combating such bias is at the heart of the Technology Law Clinic\u2019s work with Buolamwini and other clients and a central aspect of several other courses and initiatives at BU Law. The law school has a longstanding collaboration with the Hariri Institute for Computing and Computational Science and Engineering\u2014the BU Cyber Security, Law and Society Alliance<\/a>\u2014in which law professors, computer science researchers, and social scientists engage on critical questions involving technology and ethics. Last fall, two BU Law professors\u2014Stacey Dogan<\/a> and Daniela Caruso<\/a>\u2014teamed up with computer science Professor Ran Canetti<\/a> and faculty from Harvard, Columbia, and the University of California at Berkeley to launch a new online course called Law for Algorithms<\/a>. And this summer, Danielle Keats Citron<\/a>, an internationally recognized information privacy expert and a leading scholar on algorithmic bias, is joining the BU Law faculty.<\/p>\n

Algorithmic bias is a product of machine learning, which \u201cat its simplest,\u201d Sellars says, involves developing an algorithm \u201cthat is adaptive to data you feed it.\u201d<\/p>\n

\u201cIf you feed it large quantities of data over time, it can, for lack of a better word, learn,\u201d he says.<\/p>\n

The problem, of course, is that these algorithms are learning from humans who come with all kinds of implicit and explicit biases. Discrimination is hard enough to prove when a human is to blame; now civil rights advocates and regulators are grappling with how to prove discrimination by ever-changing algorithms. In March, the US Department of Housing and Urban Development sued Facebook<\/a>, claiming its algorithms allow advertisers to discriminate by only allowing certain types of people to see ads. In 2018, an Arkansas judge ordered<\/a> that state\u2019s Department of Human Services to stop using an algorithm to determine the number of at-home care hours people with disabilities receive because so many patients\u2019 hours had decreased dramatically. And the ACLU and other organizations have raised<\/a> questions about the use of algorithms by law enforcement agencies trying to predict where crimes will occur.<\/p>\n

More than a decade ago, Citron raised the idea of \u201ctechnological due process<\/a>\u201d\u2014the ability to have notice of and challenge decisions made by non-human arbiters. She has argued<\/a> that one way to ensure such due process would be to have routine algorithmic auditing by the US Federal Trade Commission, which protects consumers against unfair business practices. More recently, Citron has made the case<\/a> for abandoning punitive algorithmic decision-making in favor of more \u201cpro-social\u201d uses of the technology, such as offering translation for non-English speakers interacting with government agencies.<\/p>\n

\u201cWe are in a moment of deep uncertainty, and, often when it comes to tech, we adopt first, ask questions later,\u201d Citron says. \u201cWe are doing that in ways that are having great consequences on people\u2019s lives and opportunities.\u201d<\/p>\n

Dogan agrees.<\/p>\n

\u201cThe question of how the law should handle this bias is extremely difficult,\u201d she says. \u201cPart of the problem is the lack of appreciation among legal scholars and policy makers of just how complex machine learning is. Technologists, on the other hand, don’t always appreciate the complexity of the legal and regulatory framework in which they work. We are at a stage in which we really need to learn from one another and develop a sophisticated understanding from the legal perspective of what is happening with the technology, and vice versa. That\u2019s why we started this course and what we are trying to achieve in our collaboration with the Hariri Institute.\u201d<\/p>\n

The approach seems to be working. Julia Schur<\/a> (\u201920), who wants to work in technology law, took the Law for Algorithms class last fall.<\/p>\n

\u201cI didn\u2019t go into that class looking for answers, but looking for which questions to ask,\u201d she explains. Schur also spent her second year helping clients in the Technology Law Clinic, including on issues of algorithmic bias, and will be a research assistant for Citron next year.<\/p>\n

\u201cTo best advise computer scientists, you need to understand their language, and they need to understand yours,\u201d she says. \u201cI think that\u2019s what we\u2019re doing very successfully by having these classes: We\u2019re teaching future generations of lawyers to not fear novelty but to tackle it head on.\u201d<\/p>\n

Reported by Rebecca Beyer<\/h6>\n

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