130 citations · 306 across the 10 of their papers we have counts for
10 papers
Just-In-Time Objectives: A General Approach for Specialized AI Interactions
Michelle S. Lam, Omar Shaikh, Hallie Xu +5
Large language models promise a broad set of functions, but when not given a specific objective, they default to generic results. We demonstrate that inferring the user's in-the-mo…
Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM
Michelle S. Lam, Janice Teoh, James Landay +2
Data analysts have long sought to turn unstructured text data into meaningful concepts. Though common, topic modeling and clustering focus on lower-level keywords and require signi…
Clarify: Improving Model Robustness With Natural Language Corrections
Yoonho Lee, Michelle S. Lam, Helena Vasconcelos +2
The standard way to teach models is by feeding them lots of data. However, this approach often teaches models incorrect ideas because they pick up on misleading signals in the data…
Sociotechnical Audits: Broadening the Algorithm Auditing Lens to Investigate Targeted Advertising
Michelle S. Lam, Ayush Pandit, Colin H. Kalicki +3
Algorithm audits are powerful tools for studying black-box systems. While very effective in examining technical components, the method stops short of a sociotechnical frame, which…
Embedding Democratic Values into Social Media AIs via Societal Objective Functions
Chenyan Jia, Michelle S. Lam, Minh Chau Mai +2
Can we design artificial intelligence (AI) systems that rank our social media feeds to consider democratic values such as mitigating partisan animosity as part of their objective f…
Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design
Michelle S. Lam, Zixian Ma, Anne Li +4
Machine learning practitioners often end up tunneling on low-level technical details like model architectures and performance metrics. Could early model development instead focus o…
Jury Learning: Integrating Dissenting Voices into Machine Learning Models
Mitchell L. Gordon, Michelle S. Lam, Joon Sung Park +4
Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks ranging from online comment toxicity to misinformation detection to medical diagnosis, differen…
Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems
Chloé Bakalar, Renata Barreto, Stevie Bergman +13
Many technical approaches have been proposed for ensuring that decisions made by machine learning systems are fair, but few of these proposals have been stress-tested in real-world…
Power Networks: A Novel Neural Architecture to Predict Power Relations
Michelle Lam, Catherina Xu, Angela Kong +1
Can language analysis reveal the underlying social power relations that exist between participants of an interaction? Prior work within NLP has shown promise in this area, but the…
Atelier: Repurposing Expert Crowdsourcing Tasks as Micro-internships
Ryo Suzuki, Niloufar Salehi, Michelle S. Lam +2
Expert crowdsourcing marketplaces have untapped potential to empower workers' career and skill development. Currently, many workers cannot afford to invest the time and sacrifice t…