Timezone: »

 
Tutorial
Common Pitfalls for Studying the Human Side of Machine Learning
Deirdre Mulligan · Nitin Kohli · Joshua Kroll

Mon Dec 03 08:00 AM -- 10:00 AM (PST) @ Room 220 E

As machine learning becomes increasingly important in everyday life, researchers have examined its relationship to people and society to answer calls for more responsible uses of data-driven technologies. Much work has focused on fairness, accountability, and transparency as well as on explanation and interpretability. However, these terms have resisted definition by computer scientists: while many definitions of each have been put forward, several capturing natural intuitions, these definitions do not capture everything that is meant by associated concept, causing friction with other disciplines and the public. Worse, sometimes different properties conflict explicitly or cannot be satisfied simultaneously. Drawing on our research on the meanings of these terms and the concepts they refer to across different disciplines (e.g., computer science, statistics, public policy, law, social sciences, philosophy, humanities, and others), we present common misconceptions machine learning researchers and practitioners hold when thinking about these topics. For example, it is often axiomatic that producing machine learning explanations automatically makes the outputs of a model more understandable, but this is hardly if ever the case. Similarly, defining fairness as a statistical property of the distribution of model outputs ignores the many procedural requirements supporting fairness in policymaking and the operation of the law. We describe how to integrate the rich meanings of these concepts into machine learning research and practice, enabling attendees to engage with disparate communities of research and practice and to recognize when terms are being overloaded, thereby avoiding speaking to people from other disciplines at cross purposes.

Author Information

Deirdre Mulligan (School of Information, UC Berkeley)

Deirdre K. Mulligan is an Associate Professor in the School of Information at UC Berkeley, a faculty Director of the Berkeley Center for Law & Technology, and a PI on the new Hewlett funded Berkeley Center for Long-Term Cybersecurity. Mulligan’s research explores legal and technical means of protecting values such as privacy, freedom of expression, and fairness in emerging technical systems. Her book, Privacy on the Ground: Driving Corporate Behavior in the United States and Europe, a study of privacy practices in large corporations in five countries, conducted with UC Berkeley Law Prof. Kenneth Bamberger was recently published by MIT Press. Mulligan and Bamberger received the 2016 International Association of Privacy Professionals Leadership Award for their research contributions to the field of privacy protection. The past year, Mulligan chaired a series of interdisciplinary visioning workshops on Privacy by Design with the Computing Community Consortium to develop a research agenda. She is a member of the National Academy of Science Forum on Cyber Resilience. She is Chair of the Board of Directors of the Center for Democracy and Technology, a leading advocacy organization protecting global online civil liberties and human rights; a founding member of the standing committee for the AI 100 project, a 100-year effort to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play; and a founding member of the Global Network Initiative, a multi-stakeholder initiative to protect and advance freedom of expression and privacy in the ICT sector, and in particular to resist government efforts to use the ICT sector to engage in censorship and surveillance in violation of international human rights standards. She is a Commissioner on the Oakland Privacy Advisory Commission. Prior to joining the School of Information. she was a Clinical Professor of Law, founding Director of the Samuelson Law, Technology & Public Policy Clinic, and Director of Clinical Programs at the UC Berkeley School of Law. Mulligan was the Policy lead for the NSF-funded TRUST Science and Technology Center, which brought together researchers at U.C. Berkeley, Carnegie-Mellon University, Cornell University, Stanford University, and Vanderbilt University; and a PI on the multi-institution NSF funded ACCURATE center. In 2007 she was a member of an expert team charged by the California Secretary of State to conduct a top-to-bottom review of the voting systems certified for use in California elections. This review investigated the security, accuracy, reliability and accessibility of electronic voting systems used in California. She was a member of the National Academy of Sciences Committee on Authentication Technology and Its Privacy Implications; the Federal Trade Commission's Federal Advisory Committee on Online Access and Security, and the National Task Force on Privacy, Technology, and Criminal Justice Information. She was a vice-chair of the California Bipartisan Commission on Internet Political Practices and chaired the Computers, Freedom, and Privacy (CFP) Conference in 2004. She co-chaired Microsoft's Trustworthy Computing Academic Advisory Board with Fred B. Schneider, from 2003-2014. Prior to Berkeley, she served as staff counsel at the Center for Democracy & Technology in Washington, D.C.

Nitin Kohli (Berkeley)

Nitin Kohli is a PhD student at the UC Berkeley School of Information, researching topics that span privacy, security, and fairness. Utilizing techniques from game theory, cryptography, and statistics, I develop theory and tools that safeguard sensitive populations by constructing algorithmic mechanisms with provable guarantees over the outcomes of their use. Additionally, he also explores legal and policy mechanisms to protect these values by analyzing the incentive structures, power dynamics, and adversarial opportunities that govern these environments. Prior to his PhD, Nitin worked both as a data scientist in industry and as an academic. Within industry, Nitin developed machine learning and natural language processing algorithms to identify occurrences and locations of future risk in healthcare settings. Within academia, Nitin worked as an adjunct instructor and lecturer at UC Berkeley, teaching both introductory and advanced courses in elementary mathematics, probability, statistics, and game theory. Nitin holds a Master’s Degree in Information and Data Science from Berkeley’s School of Information, and a Bachelor’s Degree in Mathematics and Statistics, where he received departmental honors in statistics for his work in stochastic modeling and game theory.

Joshua Kroll (Princeton University)