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Maria De-Arteaga
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2020 – today
- 2024
- [c15]Jakob Schoeffer
, Maria De-Arteaga
Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making. CHI 2024: 836:1-836:18 - [c14]Luca Deck
A Critical Survey on Fairness Benefits of Explainable AI. FAccT 2024: 1579-1595 - [i29]Terrence Neumann, Sooyong Lee, Maria De-Arteaga, Sina Fazelpour, Matthew Lease:
Diverse, but Divisive: LLMs Can Exaggerate Gender Differences in Opinion Related to Harms of Misinformation. CoRR abs/2401.16558 (2024) - [i28]Soumyajit Gupta, Venelin Kovatchev, Maria De-Arteaga, Matthew Lease:
Fairly Accurate: Optimizing Accuracy Parity in Fair Target-Group Detection. CoRR abs/2407.11933 (2024) - 2023
- [j6]Myra Cheng
Social norm bias: residual harms of fairness-aware algorithms. Data Min. Knowl. Discov. 37(5): 1858-1884 (2023) - [j5]Kenneth Holstein
Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables. Proc. ACM Hum. Comput. Interact. 7(CSCW1): 1-20 (2023) - [c13]Mohammad Tahaei
Human-Centered Responsible Artificial Intelligence: Current & Future Trends. CHI Extended Abstracts 2023: 515:1-515:4 - [c12]Soumyajit Gupta
Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection. WWW 2023: 3689-3700 - [i27]Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Wei Sun, Min Kyung Lee, Matthew Lease:
Learning Complementary Policies for Human-AI Teams. CoRR abs/2302.02944 (2023) - [i26]Soumyajit Gupta, Sooyong Lee, Maria De-Arteaga, Matthew Lease:
Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection. CoRR abs/2302.07372 (2023) - [i25]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, Ilana Golbin Blumenfeld, Maria De-Arteaga, Jessica Vitak
Human-Centered Responsible Artificial Intelligence: Current & Future Trends. CoRR abs/2302.08157 (2023) - [i24]Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky:
Mitigating Label Bias via Decoupled Confident Learning. CoRR abs/2307.08945 (2023) - [i23]Luca Deck, Jakob Schoeffer, Maria De-Arteaga, Niklas Kühl:
A Critical Survey on Fairness Benefits of XAI. CoRR abs/2310.13007 (2023) - 2022
- [j4]Sina Fazelpour
Diversity in sociotechnical machine learning systems. Big Data Soc. 9(1): 205395172210820 (2022) - [c11]Terrence Neumann, Maria De-Arteaga, Sina Fazelpour:
Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms. FAccT 2022: 1504-1515 - [c10]Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
When More Data Lead Us Astray: Active Data Acquisition in the Presence of Label Bias. HCOMP 2022: 133-146 - [c9]Vincent Jeanselme, Maria De-Arteaga, Zhe Zhang, Jessica K. Barrett, Brian D. M. Tom:
Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness. ML4H@NeurIPS 2022: 12-34 - [i22]Jakob Schoeffer, Maria De-Arteaga, Niklas Kühl:
On the Relationship Between Explanations, Fairness Perceptions, and Decisions. CoRR abs/2204.13156 (2022) - [i21]Terrence Neumann, Maria De-Arteaga, Sina Fazelpour:
Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms. CoRR abs/2204.13568 (2022) - [i20]Maria De-Arteaga, Alexandra Chouldechova, Artur Dubrawski:
Doubting AI Predictions: Influence-Driven Second Opinion Recommendation. CoRR abs/2205.00072 (2022) - [i19]Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky:
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias. CoRR abs/2207.07723 (2022) - [i18]Maria De-Arteaga, Stefan Feuerriegel, Maytal Saar-Tsechansky:
Algorithmic Fairness in Business Analytics: Directions for Research and Practice. CoRR abs/2207.10991 (2022) - [i17]Kenneth Holstein, Maria De-Arteaga, Lakshmi Tumati, Yanghuidi Cheng:
Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables. CoRR abs/2207.13834 (2022) - [i16]Vincent Jeanselme, Maria De-Arteaga, Zhe Zhang, Jessica K. Barrett, Brian D. M. Tom:
Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness. CoRR abs/2208.06648 (2022) - [i15]Jakob Schoeffer, Maria De-Arteaga, Niklas Kühl:
On Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making. CoRR abs/2209.11812 (2022) - 2021
- [c8]Nil-Jana Akpinar, Maria De-Arteaga, Alexandra Chouldechova:
The effect of differential victim crime reporting on predictive policing systems. FAccT 2021: 838-849 - [c7]Ruijiang Gao, Maytal Saar-Tsechansky
Human-AI Collaboration with Bandit Feedback. IJCAI 2021: 1722-1728 - [i14]Maria De-Arteaga, Artur Dubrawski, Alexandra Chouldechova:
Leveraging Expert Consistency to Improve Algorithmic Decision Support. CoRR abs/2101.09648 (2021) - [i13]Nil-Jana Akpinar, Maria De-Arteaga, Alexandra Chouldechova:
The effect of differential victim crime reporting on predictive policing systems. CoRR abs/2102.00128 (2021) - [i12]Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee, Matthew Lease:
Human-AI Collaboration with Bandit Feedback. CoRR abs/2105.10614 (2021) - [i11]Sina Fazelpour, Maria De-Arteaga:
Diversity in Sociotechnical Machine Learning Systems. CoRR abs/2107.09163 (2021) - [i10]Myra Cheng, Maria De-Arteaga, Lester Mackey, Adam Tauman Kalai:
Social Norm Bias: Residual Harms of Fairness-Aware Algorithms. CoRR abs/2108.11056 (2021) - 2020
- [c6]Maria De-Arteaga, Riccardo Fogliato, Alexandra Chouldechova:
A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores. CHI 2020: 1-12 - [i9]Maria De-Arteaga, Tejumade Afonja, Amanda Coston:
Proceedings of NeurIPS 2019 Workshop on Machine Learning for the Developing World: Challenges and Risks of ML4D. CoRR abs/2001.00249 (2020) - [i8]Maria De-Arteaga, Riccardo Fogliato, Alexandra Chouldechova:
A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores. CoRR abs/2002.08035 (2020)
2010 – 2019
- 2019
- [c5]Nathaniel Swinger
What are the Biases in My Word Embedding? AIES 2019: 305-311 - [c4]Maria De-Arteaga, Alexey Romanov, Hanna M. Wallach, Jennifer T. Chayes
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. FAT 2019: 120-128 - [c3]Alexey Romanov, Maria De-Arteaga, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Kalai
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes. NAACL-HLT (1) 2019: 4187-4195 - [i7]Maria De-Arteaga, Alexey Romanov, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Adam Tauman Kalai:
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. CoRR abs/1901.09451 (2019) - [i6]Alexey Romanov, Maria De-Arteaga, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Tauman Kalai:
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes. CoRR abs/1904.05233 (2019) - [i5]Maria De-Arteaga, Benedikt Boecking:
Killings of social leaders in the Colombian post-conflict: Data analysis for investigative journalism. CoRR abs/1906.08206 (2019) - 2018
- [j3]Maria De-Arteaga, William Herlands, Daniel B. Neill, Artur Dubrawski:
Machine Learning for the Developing World. ACM Trans. Manag. Inf. Syst. 9(2): 9:1-9:14 (2018) - [i4]Maria De-Arteaga, Artur Dubrawski, Alexandra Chouldechova:
Learning under selective labels in the presence of expert consistency. CoRR abs/1807.00905 (2018) - [i3]Nathaniel Swinger, Maria De-Arteaga, Neil Thomas Heffernan IV, Mark D. M. Leiserson, Adam Tauman Kalai:
What are the biases in my word embedding? CoRR abs/1812.08769 (2018) - [i2]Maria De-Arteaga, Amanda Coston, William Herlands:
Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact. CoRR abs/1812.10398 (2018) - 2015
- [j2]Maria De-Arteaga, Ivan Eggel, Bao H. Do, Daniel L. Rubin, Charles E. Kahn Jr.
Comparing image search behaviour in the ARRS GoldMiner search engine and a clinical PACS/RIS. J. Biomed. Informatics 56: 57-64 (2015) - [j1]Maria De-Arteaga, Ivan Eggel, Charles E. Kahn Jr.
Analyzing Medical Image Search Behavior: Semantics and Prediction of Query Results. J. Digit. Imaging 28(5): 537-546 (2015) - [i1]Maria De-Arteaga, Artur Dubrawski, Peter Huggins:
Canonical Autocorrelation Analysis. CoRR abs/1511.06419 (2015) - 2014
- [c2]Alejandro Riveros, Maria De-Arteaga, Fabio A. González, Sergio Jiménez, Henning Müller:
MindLab-UNAL: Comparing Metamap and T-mapper for Medical Concept Extraction in SemEval 2014 Task 7. SemEval@COLING 2014: 424-427 - 2013
- [c1]Maria De-Arteaga, Sergio Jiménez, George Dueñas, Sergio Mancera, Julia Baquero:
Author Profiling Using Corpus Statistics, Lexicons and Stylistic Features Notebook for PAN at CLEF-2013. CLEF (Working Notes) 2013
Coauthor Index
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last updated on 2024-10-07 21:15 CEST by the dblp team
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