2 citations · 2 across the 10 of their papers we have counts for
10 papers
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…
ONION: A Multi-Layered Framework for Participatory ER Design
Viktoriia Makovska, George Fletcher, Julia Stoyanovich
We present ONION, a multi-layered framework for participatory Entity-Relationship (ER) modeling that integrates insights from design justice, participatory AI, and conceptual model…
We Are AI: Taking Control of Technology
Julia Stoyanovich, Armanda Lewis, Eric Corbett +3
Responsible AI (RAI) is the science and practice of ensuring the design, development, use, and oversight of AI are socially sustainable--benefiting diverse stakeholders while contr…
Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data
Shlomi Hod, Lucas Rosenblatt, Julia Stoyanovich
Differentially private (DP) machine learning often relies on the availability of public data for tasks like privacy-utility trade-off estimation, hyperparameter tuning, and pretrai…
"Would You Want an AI Tutor?" Understanding Stakeholder Perceptions of LLM-based Systems in the Classroom
Caterina Fuligni, Daniel Dominguez Figaredo, Armanda Lewis +1
Large Language Models (LLMs) have gained traction in educational settings, often framed as virtual tutors or teaching assistants. Following early skepticism and bans, many schools…
CREDAL: Close Reading of Data Models
George Fletcher, Olha Nahurna, Matvii Prytula +1
Data models are necessary for the birth of data and of any data-driven system. Indeed, every algorithm, every machine learning model, every statistical model, and every database ha…
Using Case Studies to Teach Responsible AI to Industry Practitioners
Julia Stoyanovich, Rodrigo Kreis de Paula, Armanda Lewis +1
Responsible AI (RAI) encompasses the science and practice of ensuring that AI design, development, and use are socially sustainable -- maximizing the benefits of technology while m…