Author Profiling Using Corpus Statistics, Lexicons and Stylistic Features Notebook for PAN at CLEF-2013. - Maria De-Arteaga; Sergio Jiménez 001; George Dueñas; Sergio Mancera; Julia Baquero (CLEF (Working Notes), 2013)

MindLab-UNAL: Comparing Metamap and T-mapper for Medical Concept Extraction in SemEval 2014 Task 7. - Alejandro Riveros; Maria De-Arteaga; Fabio A. González 001; Sergio Jiménez 001; Henning Müller (SemEval@COLING, 2014)

Canonical Autocorrelation Analysis. - Maria De-Arteaga; Artur Dubrawski; Peter Huggins (CoRR, 2015)

Comparing image search behaviour in the ARRS GoldMiner search engine and a clinical PACS/RIS. - Maria De-Arteaga; Ivan Eggel; Bao H. Do; Daniel L. Rubin; Charles E. Kahn Jr.; Henning Müller (J. Biomed. Informatics, 2015)

Analyzing Medical Image Search Behavior: Semantics and Prediction of Query Results. - Maria De-Arteaga; Ivan Eggel; Charles E. Kahn Jr.; Henning Müller (J. Digit. Imaging, 2015)

Learning under selective labels in the presence of expert consistency. - Maria De-Arteaga; Artur Dubrawski; Alexandra Chouldechova (CoRR, 2018)

Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact. - Maria De-Arteaga; Amanda Coston; William Herlands (CoRR, 2018)

Machine Learning for the Developing World. - Maria De-Arteaga; William Herlands; Daniel B. Neill; Artur Dubrawski (ACM Trans. Manag. Inf. Syst., 2018)

What are the Biases in My Word Embedding? - Nathaniel Swinger; Maria De-Arteaga; Neil Thomas Heffernan IV; Mark D. M. Leiserson; Adam Tauman Kalai (AIES, 2019)

Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. - Maria De-Arteaga; Alexey Romanov; Hanna M. Wallach; Jennifer T. Chayes; Christian Borgs; Alexandra Chouldechova; Sahin Cem Geyik; Krishnaram Kenthapadi; Adam Tauman Kalai (FAT, 2019)

What's in a Name? Reducing Bias in Bios without Access to Protected Attributes. - Alexey Romanov; Maria De-Arteaga; Hanna M. Wallach; Jennifer T. Chayes; Christian Borgs; Alexandra Chouldechova; Sahin Cem Geyik; Krishnaram Kenthapadi; Anna Rumshisky; Adam Kalai (NAACL-HLT (1), 2019)

Killings of social leaders in the Colombian post-conflict: Data analysis for investigative journalism. - Maria De-Arteaga; Benedikt Boecking (CoRR, 2019)

A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores. - Maria De-Arteaga; Riccardo Fogliato; Alexandra Chouldechova (CHI, 2020)

Proceedings of NeurIPS 2019 Workshop on Machine Learning for the Developing World: Challenges and Risks of ML4D. - Maria De-Arteaga; Tejumade Afonja; Amanda Coston (CoRR, 2020)

The effect of differential victim crime reporting on predictive policing systems. - Nil-Jana Akpinar; Maria De-Arteaga; Alexandra Chouldechova (FAccT, 2021)

Human-AI Collaboration with Bandit Feedback. - Ruijiang Gao; Maytal Saar-Tsechansky; Maria De-Arteaga; Ligong Han; Min Kyung Lee; Matthew Lease (IJCAI, 2021)

Leveraging Expert Consistency to Improve Algorithmic Decision Support. - Maria De-Arteaga; Artur Dubrawski; Alexandra Chouldechova (CoRR, 2021)

Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms. - Terrence Neumann; Maria De-Arteaga; Sina Fazelpour (FAccT, 2022)

When More Data Lead Us Astray: Active Data Acquisition in the Presence of Label Bias. - Yunyi Li; Maria De-Arteaga; Maytal Saar-Tsechansky (HCOMP, 2022)

Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness. - Vincent Jeanselme; Maria De-Arteaga; Zhe Zhang; Jessica K. Barrett; Brian D. M. Tom (ML4H@NeurIPS, 2022)

On the Relationship Between Explanations, Fairness Perceptions, and Decisions. - Jakob Schoeffer; Maria De-Arteaga; Niklas Kühl 001 (CoRR, 2022)

Doubting AI Predictions: Influence-Driven Second Opinion Recommendation. - Maria De-Arteaga; Alexandra Chouldechova; Artur Dubrawski (CoRR, 2022)

More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias. - Yunyi Li; Maria De-Arteaga; Maytal Saar-Tsechansky (CoRR, 2022)

Algorithmic Fairness in Business Analytics: Directions for Research and Practice. - Maria De-Arteaga; Stefan Feuerriegel; Maytal Saar-Tsechansky (CoRR, 2022)

Diversity in sociotechnical machine learning systems. - Sina Fazelpour; Maria De-Arteaga (Big Data Soc., 2022)

Human-Centered Responsible Artificial Intelligence: Current & Future Trends. - Mohammad Tahaei; Marios Constantinides; Daniele Quercia; Sean Kennedy; Michael J. Muller; Simone Stumpf; Q. Vera Liao; Ricardo Baeza-Yates; Lora Aroyo; Jess Holbrook; Ewa Luger; Michael Madaio; et al. (CHI Extended Abstracts, 2023)

Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection. - Soumyajit Gupta; Sooyong Lee; Maria De-Arteaga; Matthew Lease (WWW, 2023)

Learning Complementary Policies for Human-AI Teams. - Ruijiang Gao; Maytal Saar-Tsechansky; Maria De-Arteaga; Ligong Han; Wei Sun 031; Min Kyung Lee; Matthew Lease (CoRR, 2023)

Mitigating Label Bias via Decoupled Confident Learning. - Yunyi Li; Maria De-Arteaga; Maytal Saar-Tsechansky (CoRR, 2023)

A Critical Survey on Fairness Benefits of XAI. - Luca Deck; Jakob Schoeffer; Maria De-Arteaga; Niklas Kühl 001 (CoRR, 2023)

Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables. - Kenneth Holstein; Maria De-Arteaga; Lakshmi Tumati; Yanghuidi Cheng (Proc. ACM Hum. Comput. Interact., 2023)

Social norm bias: residual harms of fairness-aware algorithms. - Myra Cheng; Maria De-Arteaga; Lester Mackey; Adam Tauman Kalai (Data Min. Knowl. Discov., 2023)

Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making. - Jakob Schoeffer; Maria De-Arteaga; Niklas Kühl 001 (CHI, 2024)

A Critical Survey on Fairness Benefits of Explainable AI. - Luca Deck; Jakob Schoeffer; Maria De-Arteaga; Niklas Kühl 001 (FAccT, 2024)

Diverse, but Divisive: LLMs Can Exaggerate Gender Differences in Opinion Related to Harms of Misinformation. - Terrence Neumann; Sooyong Lee; Maria De-Arteaga; Sina Fazelpour; Matthew Lease (CoRR, 2024)

Fairly Accurate: Optimizing Accuracy Parity in Fair Target-Group Detection. - Soumyajit Gupta; Venelin Kovatchev; Maria De-Arteaga; Matthew Lease (CoRR, 2024)

Fighting Bias with Bias: A Machine Learning Approach to Assess Human Bias. - Wanxue Dong; Maria De-Arteaga; Maytal Saar-Tsechansky (CoRR, 2024)

Perils of Label Indeterminacy: A Case Study on Prediction of Neurological Recovery After Cardiac Arrest. - Jakob Schoeffer; Maria De-Arteaga; Jonathan Elmer (FAccT, 2025)

Finding Pareto trade-offs in fair and accurate detection of toxic speech. - Soumyajit Gupta; Venelin Kovatchev; Anubrata Das 001; Maria De-Arteaga; Matthew Lease (Inf. Res., 2025)

More of the Same: Persistent Representational Harms Under Increased Representation. - Jennifer Mickel; Maria De-Arteaga; Leqi Liu; Kevin Tian (CoRR, 2025)

Should you use LLMs to simulate opinions? Quality checks for early-stage deliberation. - Terrence Neumann; Maria De-Arteaga; Sina Fazelpour (CoRR, 2025)

Bias-Aware Mislabeling Detection via Decoupled Confident Learning. - Yunyi Li; Maria De-Arteaga; Maytal Saar-Tsechansky (CoRR, 2025)