Alexandra Chouldechova

Orcid: 0000-0002-2337-9610

According to our database1, Alexandra Chouldechova authored at least 37 papers between 2013 and 2024.

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Bibliography

2024
A structured regression approach for evaluating model performance across intersectional subgroups.
CoRR, 2024

The Impact of Differential Feature Under-reporting on Algorithmic Fairness.
CoRR, 2024

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

Examining risks of racial biases in NLP tools for child protective services.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Overcoming Algorithm Aversion: A Comparison between Process and Outcome Control.
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

2022
Heterogeneity in Algorithm-Assisted Decision-Making: A Case Study in Child Abuse Hotline Screening.
Proc. ACM Hum. Comput. Interact., 2022

Doubting AI Predictions: Influence-Driven Second Opinion Recommendation.
CoRR, 2022

Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Unsupervised and Semi-supervised Bias Benchmarking in Face Recognition.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies.
Proc. ACM Hum. Comput. Interact., 2021

Leveraging Expert Consistency to Improve Algorithmic Decision Support.
CoRR, 2021

Using a Machine Learning Tool to Support High-Stakes Decisions in Child Protection.
AI Mag., 2021

Characterizing Fairness Over the Set of Good Models Under Selective Labels.
Proceedings of the 38th International Conference on Machine Learning, 2021

Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds.
Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy.
Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

The effect of differential victim crime reporting on predictive policing systems.
Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems.
Proceedings of the CHI '21: CHI Conference on Human Factors in Computing Systems, 2021

On the Validity of Arrest as a Proxy for Offense: Race and the Likelihood of Arrest for Violent Crimes.
Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020
A snapshot of the frontiers of fairness in machine learning.
Commun. ACM, 2020

Counterfactual Predictions under Runtime Confounding.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Counterfactual risk assessments, evaluation, and fairness.
Proceedings of the FAT* '20: Conference on Fairness, 2020

A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores.
Proceedings of the CHI '20: CHI Conference on Human Factors in Computing Systems, 2020

Fairness Evaluation in Presence of Biased Noisy Labels.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Counterfactual Risk Assessments, Evaluation, and Fairness.
CoRR, 2019

What's in a Name? Reducing Bias in Bios without Access to Protected Attributes.
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting.
Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

Toward Algorithmic Accountability in Public Services: A Qualitative Study of Affected Community Perspectives on Algorithmic Decision-making in Child Welfare Services.
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019

2018
The Frontiers of Fairness in Machine Learning.
CoRR, 2018

Learning under selective labels in the presence of expert consistency.
CoRR, 2018

Does mitigating ML's impact disparity require treatment disparity?
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions.
Proceedings of the Conference on Fairness, Accountability and Transparency, 2018

2017
Does mitigating ML's disparate impact require disparate treatment?
CoRR, 2017

Fairer and more accurate, but for whom?
CoRR, 2017

Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments.
Big Data, 2017

2013
Differences in search engine evaluations between query owners and non-owners.
Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, 2013


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