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The Growing Dilemma Within AI – Unconscious Bias


Level: Advanced
Runtime: 113 minutes
Recorded Date: February 03, 2020
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Agenda


  • Machine Learning
  • Unconscious Bias
  • Ethics of using Predictive Analysis and AI
  • Policy & Liability
  • Mitigation
Runtime: 1 hour, 53 minutes
Recorded: February 3, 2020

Description

As AI technology continues to grow and increasingly become part of our everyday lives, businesses, and legal system; the lack of safeguards and morality within AI has seen a rise in unconscious bias leading to injustices.

This program was recorded as part of ALM's Legalweek Conference on February 3rd, 2020.

Provided By

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Panelists

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Miriam Vogel

Executive Director
EqualAI

Miriam is the Executive Director of EqualAI and an adjunct professor at Georgetown Law where she teaches Technology Law & Policy. Previously, Miriam served in U.S. government leadership for over two decades, including positions in the three branches of federal government. Most recently, she served as Associate Deputy Attorney General, where she advised the Attorney General and the Deputy Attorney General (DAG) and developed and implemented priority policies and programs on a broad range of legal, policy and operational issues. Miriam spearheaded the Department’s Intellectual Property (IP) efforts to identify and dismantle IP theft domestically and internationally and worked with the DAG to develop, manage and monitor divisions’ multibillion-dollar budgets, resolve high-level challenges, and represent the Department in briefings for White House, Congressional and GAO staff on policy initiatives and oversight matters.

Miriam served in the White House in two Administrations, most recently as the Acting Director of Justice and Regulatory Affairs. She advised White House leadership on regulatory, women, economic, food safety and LGBT policy and a wide array of criminal justice-related matters.

Prior to serving in the Obama administration, Miriam was Associate General Counsel at Dana-Farber Cancer Institute and practiced entertainment/corporate transactional law at Sheppard Mullin in Los Angeles. Miriam began her legal career as a federal clerk in Denver, Colorado.

She has traveled extensively in the Middle East, including volunteering in the Israeli army; and studied French language and civilization in Paris. Miriam graduated with Honors from University of Michigan and Georgetown University Law Center.

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Kush Varshney

Principal Research Staff Member & Manager
IBM Research AI

Kush R. Varshney was born in Syracuse, New York in 1982. He received the B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University, Ithaca, New York, in 2004. He received the S.M. degree in 2006 and the Ph.D. degree in 2010, both in electrical engineering and computer science at the Massachusetts Institute of Technology (MIT), Cambridge.

He is a research staff member and manager in IBM Research AI, working from the Thomas J. Watson Research Center, Yorktown Heights, New York. He co-directs the IBM Science for Social Good initiative. He is also a data ambassador with DataKind, New York, New York. While at MIT, he was a research assistant with the Stochastic Systems Group in the Laboratory for Information and Decision Systems and a National Science Foundation Graduate Research Fellow. He has been a visiting student at Laboratoire de Mathématiques Appliquées aux Systèmes at École Centrale, Paris, and an intern at the Systems and Decision Sciences Section, Lawrence Livermore National Laboratory, Livermore, California, at Sun Microsystems, Burlington, Massachusetts, and at Sensis Corporation, DeWitt, New York. His research interests include statistical signal processing, machine learning, data mining, and image processing.

Dr. Varshney is a member of Eta Kappa Nu and Tau Beta Pi, and a senior member of IEEE. He received a Best Student Paper Travel Award at the 2009 International Conference on Information Fusion, the Best Paper Award at the 2013 IEEE International Conference on Service Operations and Logistics, and Informatics, the Best Social Good Paper Award at the 2014 ACM SIGKDD Conference on Knowledge Discovery and Data Mining, a Best Research Paper Honorable Mention at the 2015 SIAM International Conference on Data Mining, and several IBM awards for contributions to business analytics projects. He is a member of the IEEE Signal Processing Society's Signal Processing Theory and Methods Technical Committee.

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Kim Nayyer

Edward Cornell Law Librarian & Associate Dean for Library Services
Cornell Law School

Associate Dean Nayyer oversees the operations of the Cornell Law School library and its services to the law school community. As Edward Cornell Law Librarian, she also represents the Law School Library within the Cornell University Library system.

Before joining Cornell in May 2019, she was Associate University Librarian for Law and Adjunct Associate Professor at University of Victoria in Canada. There, her administrative responsibilities included direction of the law library, and participation in the administration of the university’s copyright office. Within that law school, she taught advanced and introductory legal research and writing in the Canadian context, along with a program in comparative legal research. She planned that law library’s role in the world’s first JD/JID dual degree program, which combines education in Indigenous legal orders and Canadian common law.

Associate Dean Nayyer contributes time to various legal education and legal information professional communities including the Association of American Law Schools (Vice-Chair, North American Cooperation Section); American Association of Law Libraries (member, Spectrum Editorial Board); the Canadian Association of Law Libraries/L’Association Canadienne des Bibliothèques de Droit (Vice-President; founder, Diversity, Inclusion, and Decolonization Committee; past co-chair, Copyright Committee); and the Canadian Federation of Library Associations—Fédération Canadienne des Associations des Bibliothèques (member, Copyright Committee).

Her research interests span legal studies and information studies. They include the ethics of big data and artificial intelligence in legal information; critical information literacy in machine-manipulated environments of legal information, non-legal information, and the media; digital citizenship, privacy, and data ownership issues and their regulation in intersections with legal and non-legal public online participation; technological innovations in legal education, pedagogy, and practice; access to direct and legal information and machine-manipulated legal information and access to justice; comparative US and Canadian legal information topics and research methods; comparative copyright and other intellectual property law and their intersections with Indigenous knowledge; comparative copyright and licensing issues and solutions in libraries and of primary legal information; and inclusive library, legal, and educational leadership in environments of diversity and accelerated change.

Associate Dean Nayyer holds a BSc from the University of Alberta, an LLB from York University’s Osgoode Hall Law School, and an MLIS from the University of Alberta. She clerked for the late Associate Chief Justice Jerome at the Federal Court of Canada/Cour Fédérale du Canada in a bilingual position and is called to the bars of Alberta and Ontario. In her law practice career she worked as a litigation and research attorney for large and small law firms in Calgary, Edmonton, and Toronto and she served as legal counsel for the Alberta Court of Appeal in Edmonton and Calgary.

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Dean Harvey

Partner and Co-Chair, Artificial Intelligence, Machine Learning & Robotics
Perkins Coie LLP

Dean Harvey is the co-chair of Perkins Coie's Artificial Intelligence, Machine Learning & Robotics practice. He counsels FORTUNE 100 clients, mid-tier clients and start-ups on AI and machine learning, including product compliance, development acquisition of machine learning solutions, inadvertent bias, data acquisition and use, and data privacy and security. He has represented both providers and buyers in establishing templates for negotiating machine learning and AI transactions. He has advised clients in AI projects involving issues such as fraud detection, disease risk prediction, disease condition prediction, autonomous vehicles, frictionless checkout solutions, image recognition and identification, pricing optimization, claims editing, RPA and e-commerce site optimization. He began his technical career in AI and machine learning developing expert systems. Mr. Harvey received a B.S. in computer science from West Virginia University and a J.D. from the University of Texas School of Law.


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