2024
Supporting Industry Computing Researchers in Assessing, Articulating, and Addressing the Potential Negative Societal Impact of Their Work.
CoRR, 2024
The Legal Duty to Search for Less Discriminatory Algorithms.
CoRR, 2024
Measuring machine learning harms from stereotypes: requires understanding who is being harmed by which errors in what ways.
CoRR, 2024
On the Actionability of Outcome Prediction.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
Variance, Self-Consistency, and Arbitrariness in Fair Classification.
CoRR, 2023
Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023
Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023
Informational Diversity and Affinity Bias in Team Growth Dynamics.
Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, 2023
Taxonomizing and Measuring Representational Harms: A Look at Image Tagging.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023
2022
On modeling human perceptions of allocation policies with uncertain outcomes.
SIGecom Exch., July, 2022
An Uncommon Task: Participatory Design in Legal AI.
Proc. ACM Hum. Comput. Interact., 2022
Measuring Representational Harms in Image Captioning.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022
REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022
Model Multiplicity: Opportunities, Concerns, and Solutions.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022
Disentangling the Components of Ethical Research in Machine Learning.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022
Mimetic Models: Ethical Implications of AI that Acts Like You.
Proceedings of the AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19, 2022
2021
Responsible computing during COVID-19 and beyond.
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Commun. ACM, 2021
Better Together?: How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness.
Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021
Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies.
Proceedings of the EAAMO 2021: ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, Virtual Event, USA, October 5, 2021
Computer Vision and Conflicting Values: Describing People with Automated Alt Text.
Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs.
Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021
2020
Mitigating bias in algorithmic hiring: evaluating claims and practices.
Proceedings of the FAT* '20: Conference on Fairness, 2020
The meaning and measurement of bias: lessons from natural language processing.
Proceedings of the FAT* '20: Conference on Fairness, 2020
The hidden assumptions behind counterfactual explanations and principal reasons.
Proceedings of the FAT* '20: Conference on Fairness, 2020
When not to design, build, or deploy.
Proceedings of the FAT* '20: Conference on Fairness, 2020
Roles for computing in social change.
Proceedings of the FAT* '20: Conference on Fairness, 2020
Language (Technology) is Power: A Critical Survey of "Bias" in NLP.
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020
2019
Mitigating Bias in Algorithmic Employment Screening: Evaluating Claims and Practices.
CoRR, 2019
Problem Formulation and Fairness.
Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019
2018
Debiasing Desire: Addressing Bias & Discrimination on Intimate Platforms.
Proc. ACM Hum. Comput. Interact., 2018
2017
Ten simple rules for responsible big data research.
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PLoS Comput. Biol., 2017
Big Data, Data Science, and Civil Rights.
CoRR, 2017
Engaging the ethics of data science in practice.
Commun. ACM, 2017
Social and Technical Trade-Offs in Data Science.
Big Data, 2017
2016
WSDM 2016 Workshop on the Ethics of Online Experimentation.
Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, 2016
2014
Big data's end run around procedural privacy protections.
Commun. ACM, 2014
2012
A Critical Look at Decentralized Personal Data Architectures
CoRR, 2012
The price of precision: voter microtargeting and its potential harms to the democratic process.
Proceedings of the first edition workshop on Politics, elections and data, 2012
2010
Adnostic: Privacy Preserving Targeted Advertising.
Proceedings of the Network and Distributed System Security Symposium, 2010