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.
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.
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