[Colloquium] [CDAC] March 1: Deirdre Mulligan, UC Berkeley - "Procurement as Policy: Administrative Process for Machine Learning"

[Rob Mitchum]

*Deirdre Mulligan*
*Professor, School of Information*
*University of California, Berkeley*

*Monday, March 1st*
*3:00 p.m. - 4:00 p.m.*
*Zoom (RSVP for login
<https://www.eventbrite.com/e/cdac-distinguished-speaker-series-deirdre-mulligan-uc-berkeley-tickets-129949936759>)
or YouTube <https://youtu.be/1JqhyZVvUGE> (no registration required)*


*Procurement as Policy: Administrative Process for Machine Learning*
At every level of government, officials contract for technical systems that
employ machine learning—systems that perform tasks without using explicit
instructions, relying on patterns and inference instead. These systems
frequently displace discretion previously exercised by policymakers or
individual front-end government employees with an opaque logic that bears
no resemblance to the reasoning processes of agency personnel. However,
because agencies acquire these systems through government procurement
processes, they, and the public, have little input into—or even knowledge
about—their design, or how well that design aligns with public goals and
values.

In this talk I explore specific ways that design decisions inherent in
machine-learning systems are substantive policy decisions, and how the
procurement processes, which today dominates their adoption, limits their
full consideration. Specifically, these embedded policies receive little or
no agency or outside expertise beyond that provided by the vendor: no
public participation, no reasoned deliberation, and no factual record.
Design decisions are left to private third-party developers: Government
responsibility for policymaking is abdicated. I argue that, when policy
decisions are made through system design processes suitable for substantive
administrative determinations should be used: processes that demand
reasoned deliberation reflecting both technocratic concerns about the
informed application of expertise, and democratic concerns about political
accountability. Finally, I sketch ways that agencies might garner relevant
technical expertise and overcome problems of system opacity, satisfying
administrative law’s technocratic demand for reasoned expert deliberation;
and institutional and engineering design solutions to the challenge of
policy making opacity, offering both process paradigms to ensure the
“political visibility” required for public input and political oversight,
and proposing the importance of using “contestable design”—design that
exposes value-laden features and parameters, and provides for iterative
human involvement in system evolution and deployment. Together, these
institutional and design approaches further both administrative law’s
technocratic, and democratic, mandates.

*Bio*: Deirdre K. Mulligan is a Professor in the School of Information at
UC Berkeley, a faculty Director of the Berkeley Center for Law &
Technology, a co-organizer of the Algorithmic Fairness & Opacity Working
Group, an affiliated faculty on the Hewlett funded Berkeley Center for
Long-Term Cybersecurity, and a faculty advisor to the Center for
Technology, Society & Policy. 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. She is a
member of the Defense Advanced Research Projects Agency’s Information
Science and Technology study group (ISAT); and, a member of the National
Academy of Science Forum on Cyber Resilience. She is past-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; an initial board member of the Partnership on AI; a founding member
of the standing committee for the AI 100 project; 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 recently served as a Commissioner on the Oakland Privacy
Advisory Commission and helped to develop a local ordinance providing
oversight of surveillance technology. Mulligan chaired a series of
interdisciplinary visioning workshops on Privacy by Design with the
Computing Community Consortium to develop a shared interdisciplinary
research agenda. 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.


*Part of the CDAC Winter 2021 Distinguished Speaker Series:*
*Bias Correction: Solutions for Socially Responsible Data Science
<https://cdac.uchicago.edu/news/announcing-the-cdac-winter-2021-distinguished-speaker-series/>*

Security, privacy and bias in the context of machine learning are often
treated as binary issues, where an algorithm is either biased or fair,
ethical or unjust. In reality, there is a tradeoff between using technology
and opening up new privacy and security risks. Researchers are developing
innovative tools that navigate these tradeoffs by applying advances in
machine learning to societal issues without exacerbating bias or
endangering privacy and security. The CDAC Winter 2021 Distinguished
Speaker Series will host interdisciplinary researchers and thinkers
exploring methods and applications that protect user privacy, prevent
malicious use, and avoid deepening societal inequities — while diving into
the human values and decisions that underpin these approaches.



-- 
*Rob Mitchum*

*Associate Director of Communications for Data Science and Computing*
*University of Chicago*
*rmitchum at uchicago.edu <rmitchum at ci.uchicago.edu>*
*773-484-9890*
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