2 citations · 2 across the 10 of their papers we have counts for
4 papers · 1 filter
Be Fair! Can Machine Learning Engineering Agents Adhere to Fairness Constraints?
Anna Richter, Julia Stoyanovich, Sebastian Schelter
Machine learning engineering (MLE) agents promise to automate end-to-end ML pipeline development from raw data and natural language instructions, potentially making ML accessible t…
SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems
Rima Hazra, Bikram Ghuku, Ilona Marchenko +5
Large language models are rapidly being deployed as AI tutors, yet current evaluation paradigms assess problem-solving accuracy and generic safety in isolation, failing to capture…
Memory Undone: Between Knowing and Not Knowing in Data Systems
Viktoriia Makovska, George Fletcher, Julia Stoyanovich +1
Machine learning and data systems increasingly function as infrastructures of memory: they ingest, store, and operationalize traces of personal, political, and cultural life. Yet c…
Explanation Multiplicity in SHAP: Characterization and Assessment
Hyunseung Hwang, Seungeun Lee, Lucas Rosenblatt +2
SHAP explanations are widely used in high-stakes settings to justify decisions, yet they can differ substantially across repeated runs, even when the model, the input instance, and…