Government Procurement, Design, and Responsible Use of Emerging Technologies

The adoption of artificial intelligence and emerging technologies across government operations has created urgent questions about procurement practices, risk assessment frameworks, and responsible deployment strategies. California agencies at the state and local levels are experimenting with AI in a range of internal and public facing use cases and evaluating whether and how it can improve service delivery and operational efficiency while safeguarding equity and transparency, and building public trust. 
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To support agencies’ responsible AI experimentation, UC Berkeley and the California Department of Technology convened a workshop bringing together government technology leaders, former federal and international technology leaders, nonprofit leaders, and academic researchers to share learnings from AI pilots and AI governance, including procurement, processes. The meeting followed the Chatham House Rule. The workshop featured: 

• agency case studies highlighting the variety of AI use case pilots and accompanying innovative deployment, evaluation, and procurement practices; 
• research and practice insights from former senior technology policy leaders—UC Berkeley Fellows in Applied Technology Policy and UC Berkeley Tech Policy Fellows—on AI procurement and governance policy approaches across different countries; and,
• working group discussions to identify policy, infrastructure, and capacity gaps across the AI lifecycle and opportunities to address them through collaborations across government and academia.

In June of 2018, the Algorithmic Fairness and Opacity Working Group (AFOG) held a summer workshop with the theme “Algorithms are Opaque and Unfair: Now What?.” The event was organized by Berkeley I School Professors (and AFOG co-directors) Jenna Burrell and Deirdre Mulligan and postdoc Daniel Kluttz, and Allison Woodruff and Jen Gennai from Google. Our working group is generously sponsored by Google Trust and Safety and hosted at the UC Berkeley School of Information.

Inspired by questions that came up at our biweekly working group meetings during the 2017-2018 academic year, we organized four panels for the workshop. The panel topics raised issues that we felt required deeper consideration and debate. To make progress we brought together a diverse, interdisciplinary group of experts from academia, industry, and civil society in a workshop-style environment. In panel discussions, we considered potential ways of acting on algorithmic (un)fairness and opacity. We sought to consider the fullest possible range of ‘solutions,’ including technical implementations (algorithms, user-interface designs), law and policy, standard-setting, incentive programs, new organizational processes, labor organizing, and direct action.

Key outcomes include: 

• a commitment to creating more opportunities for shared learning across state and city agencies and academic researchers;
• the identification of four investments that could bring the knowledge and expertise within agencies and academia together to support California’s leadership in using technologies to serve all Californians and setting standards for responsible emerging technology use across the nation:

• A shared infrastructure for independent testing and evaluation of AI systems
• A shared set of methods and tools for testing and evaluating AI use case deployments
• Standardized acceptance criteria for AI procurements; and,
• Equity assessment tools and methods to support all of the above.

In June of 2018, the Algorithmic Fairness and Opacity Working Group (AFOG) held a summer workshop with the theme “Algorithms are Opaque and Unfair: Now What?.” The event was organized by Berkeley I School Professors (and AFOG co-directors) Jenna Burrell and Deirdre Mulligan and postdoc Daniel Kluttz, and Allison Woodruff and Jen Gennai from Google. Our working group is generously sponsored by Google Trust and Safety and hosted at the UC Berkeley School of Information.

Inspired by questions that came up at our biweekly working group meetings during the 2017-2018 academic year, we organized four panels for the workshop. The panel topics raised issues that we felt required deeper consideration and debate. To make progress we brought together a diverse, interdisciplinary group of experts from academia, industry, and civil society in a workshop-style environment. In panel discussions, we considered potential ways of acting on algorithmic (un)fairness and opacity. We sought to consider the fullest possible range of ‘solutions,’ including technical implementations (algorithms, user-interface designs), law and policy, standard-setting, incentive programs, new organizational processes, labor organizing, and direct action.

Quinn Anex-Ries, Senior Policy Analyst, Center for Democracy & Technology
Yelda Bartlett, Notetaker, Goldman School of Public Policy, University of California, Berkeley
Tony Batalla, Director of Information Technology & CIO, City of Oakland
Judy Brewer, Executive Fellow in Applied Tech Policy, University of California, Berkeley
Charlotte A. Burrows, Former Chair, U.S. Equal Employment Opportunity Commission
Kathryn Camp, Notetaker, Goldman School of Public Policy, University of California, Berkeley
Alan Davidson, Head of Government Affairs, Databricks
Anil Dewan, Applied AI Product Leader
Leila Doty, Privacy & AI Analyst, City of San José
Chris Edley III, Executive Director, AI and Justice Consortium
Nel Escher, Bellwether Postdoc, University of California, Berkeley School of Information
Marcela Escobar-Alava, Executive Fellow in Applied Tech Policy, University of California, Berkeley
Avi Feller, Associate Professor of Public Policy and Statistics, University of California, Berkeley
Alexia Gallon, Research Apprentice, Berkeley Risk and Security Lab
Maya De Ganyar, Undergraduate, University of California, Berkeley
Sabine Gerdon, Former UK Office of Artificial Intelligence
Chris Given, Deputy Secretary for Technology and Innovation, California GovOps Agency
Jane Gong, Director of Emerging Technologies, City and County of San Francisco
Tyler Haas, Senior Data Scientist, California Office of Data and Innovation
Eric Hysen, Executive Fellow in Applied Tech Policy, University of California, Berkeley
Angela Jin, PhD candidate, Computer Science, University of California, Berkeley
Tania Jogesh, Lead AI Engineer, City and County of San Francisco
Melanie Kolbe-Guyot, Head of Data Governance, Statistical Office, Canton of Basel-City, Switzerland
Prakash Krishnan, Research Assistant, University of California, Berkeley
Ivy Teng Lei, Chief Digital Strategy Officer, California Department of Technology
Ianina Lipara, Administrative Director, Criminal Law & Justice Center, University of California, Berkeley
Siobhan McDonough, Senior Data Policy Analyst, California Office of Data and Innovation
Michelle Moskowitz, Director, Advocacy and Institutional Relations, University of California, Berkeley
Deirdre K. Mulligan, Professor, University of California, Berkeley School of Information
Justin Norman, PhD Candidate, University of California, Berkeley School of Information
Viviana Padelli, AI Policy Principal, City and County of San Francisco
Isabelle A. Qian, Research Assistant, University of California, Berkeley School of Information
Inioluwa Deborah Raji, PhD candidate, EECS, University of California, Berkeley
Katie Regan, State Data Services Manager, California Department of Technology
Denice W. Ross, Executive Fellow in Applied Tech Policy, University of California, Berkeley
Esa Sferra-Bonistalli, Senior Advisor & Director of AI Governance, US Department of Homeland Security
Lea A. Shanley, Director and CEO, International Computer Science Institute
Florence Simon, Director, Mayor’s Office of Innovation, City and County of San Francisco
Kaye Sklar, Senior Manager for Content and Insights, Open Contracting Partnership
Caroline Kyungae Smith, Chief Transformation Officer, Office of Critical Services, California Department of Technology
Genevieve M. Smith, Founding Director, BAIR Responsible AI Initiative 
Jenny Toomey, Executive Fellow in Applied Tech Policy, University of California, Berkeley
Merici Vinton, Executive Fellow in Applied Tech Policy, University of California, Berkeley
Samit Wangnoo, Procurement Manager, California Department of Technology
Jenny R. Yang, Executive Fellow in Applied Tech Policy, University of California, Berkeley
Meg Young, Senior Researcher, Data & Society
Vera Zakem, State Chief Technology Innovation Officer at the California Department of Technology; Executive Fellow in Applied Tech Policy, University of California, Berkeley
Victor Zhenyi Wang, PhD candidate, University of California, Berkeley School of Information
Matt Zhou, Assistant State Chief Data Officer, California Office of Data and Innovation
Dan Zhukov, Program Coordinator, Executive Fellowship in Applied Technology Policy 

In June of 2018, the Algorithmic Fairness and Opacity Working Group (AFOG) held a summer workshop with the theme “Algorithms are Opaque and Unfair: Now What?.” The event was organized by Berkeley I School Professors (and AFOG co-directors) Jenna Burrell and Deirdre Mulligan and postdoc Daniel Kluttz, and Allison Woodruff and Jen Gennai from Google. Our working group is generously sponsored by Google Trust and Safety and hosted at the UC Berkeley School of Information.

Inspired by questions that came up at our biweekly working group meetings during the 2017-2018 academic year, we organized four panels for the workshop. The panel topics raised issues that we felt required deeper consideration and debate. To make progress we brought together a diverse, interdisciplinary group of experts from academia, industry, and civil society in a workshop-style environment. In panel discussions, we considered potential ways of acting on algorithmic (un)fairness and opacity. We sought to consider the fullest possible range of ‘solutions,’ including technical implementations (algorithms, user-interface designs), law and policy, standard-setting, incentive programs, new organizational processes, labor organizing, and direct action.