Geiger, R. Stuart, Kevin Yu, Yanlai Yang, Mindy Dai, Jie Qiu, Rebekah Tang, and Jenny Huang. Garbage in, garbage out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From? In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* ’20), 2020. [PDF]
Andrus, McKane and Thomas Krendl Gilbert. 2019. Towards a Just Theory of Measurement: A Principled Social Measurement Assurance Program for Machine Learning. In proceedings of the AAAI/ACM conference on Artificial Intelligence, Ethics and Society. Honolulu, HI. [PDF]
Burrell, Jenna, Zoe Kahn, Anne Jonas, and Daniel Griffin. 2019. When Users Control the Algorithms: Values Expressed in Practices on Twitter. In proceedings of the ACM Computer-Supported Cooperative Work and Social Computing (CSCW) conference. Austin, TX. [PDF]
Jonas, Anne and Jenna Burrell. 2019. Friction, Snake Oil, and Weird Countries: Cybersecurity Systems Could Deepen Global Inequality through Regional Blocking. Big Data & Society, 6, 1. Jan 2019. [PDF]
Kluttz, Daniel and Deirdre K. Mulligan. 2019. Automated Decision Support Technologies and the Legal Profession. Berkeley Technology Law Journal.
Mulligan, K. Deirdre., Joshua A. Kroll, Nitin Kohli, and Richmond Y. Wong. 2019. This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology. In proceedings of the ACM Computer-Supported Cooperative Work and Social Computing (CSCW) conference. Austin, TX. [PDF]
Mulligan, K. Deirdre, Daniel Kluttz, and Nitin Kohli. 2019. Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the Professions. Draft available at SSRN: http://dx.doi.org/10.2139/ssrn.3311894
Wu, Eva Yiwei, Emily Pedersen, Niloufar Salehi. 2019. Agent, Gatekeeper, Drug Dealer: How Content Creators Craft Algorithmic Personas. In proceedings of the ACM Computer-Supported Cooperative Work and Social Computing (CSCW) conference. Austin, TX. [PDF]
Dobbe, Roel, Sarah Dean, Thomas Gilbert, and Nitin Kohli (2018) A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics. Presented at the 2018 Workshop on Fairness, Accountability and Transparency in Machine Learning during ICML 2018. Stockholm, Sweden.
2020 Projects co-sponsored with the Center for Technology Society and Policy (CTSP)
Algorithmic Fairness in Mediated Markets
Online marketplaces, where firms like Uber and Amazon control the terms of economic interaction, exert an increasing influence on economic life. Algorithms on these platforms are drawing greater scrutiny, whether in how different price and quality characteristics are determined for different users, the end outcomes algorithms optimize for, and ultimately, how surplus created by these networks is allocated between buyers, sellers, and the platform. This project undertakes a systematic survey of perceptions on fairness among riders and drivers in ride-sharing marketplaces. We seek to carefully catalogue different notions of fairness among different groups, examining where they might cohere and where they might be in tension. We explore obligations platform firms might have as custodians of market information and arbiters of market choice and structure, to contribute to developing pubic debate on what a “just” algorithmic regime might resemble for online marketplaces.
An alternate lexicon for AI
This project joins the “second wave” of AI scholars in examining structural questions around what constitutes the field of social concerns within current AI and Social Impact research. Under this project, we will map the ethical and social landscape of current AI research and its limits by conducting a critical and comparative content analysis of how social/ethical concerns have been represented over time at leading AI/ML conferences. Based on our findings, we will also develop a draft syllabus on ‘Global and Critical AI’ and will convene a one-day workshop to build vocabulary for such AI thinking and writing. With this project we aim to join the growing community at UC Berkeley and beyond in identifying the dominant techno-imaginaries of AI and Social Impact research, and 2) critically and tactically expanding that field to bring diverse experiential, social, cultural, and political realities beyond the Silicon Valley to bear upon AI thinking. Morgan Ames is also collaborating on this project.
Environmental conservation in the age of algorithms: from data to decisions
While human impacts on the rest of nature accelerate, our techniques of observing those impacts are rapidly outstripping our ability to react to them. Artificial Intelligence (AI) techniques are quickly being adopted in the environmental sphere not only to inform decisions through providing more useful datasets, but also to facilitate more robust decisions about complex natural resource and conservation problems. The onset of decision-making algorithms requires us to urgently ask the question: Whose values are shaping AI decision making systems in natural resource management? In the shadow of this problem, our project seeks to understand the expansion of privately developed but publicly available environmental data and algorithms through a critical study of algorithmic governance. It aims to facilitate an analysis of how governments and nongovernmental entities deploy techniques of algorithmic conservation to aid in collective judgments about our complex and troubled relation to our natural environments. Carl Boettiger is also a collaborator on the project.
State-Firm Coproduction of China’s Social Credit System
Fellows: Shazeda Ahmed
This qualitative dissertation project investigates how the Chinese government and domestic technology companies are collaboratively constructing the country’s social credit system. Through interviews with government officials, tech industry representatives, lawyers, and academics, I argue that China’s government and tech firms rely on and influence one another in their efforts to engineer social trust through incentives of punishment and reward.
2019 Projects co-sponsored with the Center for Technology Society and Policy (CTSP)
Affect & Facial Recognition in Hiring
Affective computing is the study and development of systems that can recognize, interpret, process, and simulate human emotion. Powered by artificial intelligence, emerging applications of affect recognition in the workplace raise pressing ethical and regulatory questions: what happens when an automated understanding of human affect enters the real world, in the form of systems that have life-altering consequences? This is particularly pertinent in the realm of workplace surveillance, with no clear answers about how to address privacy, bias, and discrimination problems. As the underlying technologies are generally proprietary and therefore opaque, their impact can only be assessed with a deeper look into how they are designed and implemented. In collaboration with Coworker.org, a nonprofit that helps people organize for improvements in their jobs and workplace, we thus aim to evaluate applications of affect recognition and the potential risks and implications of these technologies.
Algorithmic Intermediation and Workplace Surveillance – Emerging Threats to the Democracy of Work
Advanced analytical software is changing the dynamics between workers and their employers, exacerbating the existing power asymmetry. Combined with AI, technologies like facial recognition, email monitoring, and audio recordings can all be analyzed to infer workers’ emotions and behavior to determine facets of worker productivity or whether an employee is, for example, “threatening.” This technology often reinforces racial and gender bias, and little is known about how the results of these analyses affect managerial decisions like promotions and terminations. Not only is this surveillance a huge loss of privacy for employees, but it may also have a negative impact on their stress levels or ability to perform in the workplace. Our project will investigate the different workplace surveillance technologies on the market and their effects on workers, and then provide potential policy responses to these issues.
Coordinated Entry System Research and Development for a Continuum of Care in Northern California (co-sponsored with CLTC)