69 citations · 121 across the 6 of their papers we have counts for
7 papers
Perils of Label Indeterminacy: A Case Study on Prediction of Neurological Recovery After Cardiac Arrest
Jakob Schoeffer, Maria De-Arteaga, Jonathan Elmer
The design of AI systems to assist human decision-making typically requires the availability of labels to train and evaluate supervised models. Frequently, however, these labels ar…
A Critical Survey on Fairness Benefits of Explainable AI
Luca Deck, Jakob Schoeffer, Maria De-Arteaga +1
In this critical survey, we analyze typical claims on the relationship between explainable AI (XAI) and fairness to disentangle the multidimensional relationship between these two…
Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making
Jakob Schoeffer, Maria De-Arteaga, Niklas Kuehl
In this work, we study the effects of feature-based explanations on distributive fairness of AI-assisted decisions, specifically focusing on the task of predicting occupations from…
Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness
Vincent Jeanselme, Maria De-Arteaga, Zhe Zhang +2
Machine learning risks reinforcing biases present in data and, as we argue in this work, in what is absent from data. In healthcare, societal and decision biases shape patterns in…
Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables
Kenneth Holstein, Maria De-Arteaga, Lakshmi Tumati +1
In many real world contexts, successful human-AI collaboration requires humans to productively integrate complementary sources of information into AI-informed decisions. However, i…
On the Relationship Between Explanations, Fairness Perceptions, and Decisions
Jakob Schoeffer, Maria De-Arteaga, Niklas Kuehl
It is known that recommendations of AI-based systems can be incorrect or unfair. Hence, it is often proposed that a human be the final decision-maker. Prior work has argued that ex…
Diversity in Sociotechnical Machine Learning Systems
Sina Fazelpour, Maria De-Arteaga
There has been a surge of recent interest in sociocultural diversity in machine learning (ML) research, with researchers (i) examining the benefits of diversity as an organizationa…