42 citations · 54 across the 12 of their papers we have counts for
13 papers
Position: Evaluations of AI Moral Reasoning Still Miss Half of the Picture
Aidan Kierans, Ritam Dutt, Kaley Rittichier +2
Recent work on evaluating the moral competence of large language models (LLMs) has focused primarily on what we call the moral value problem, i.e., whether model outputs align with…
AI-Powered Detection of Inappropriate Language in Medical School Curricula
Chiman Salavati, Shannon Song, Scott A. Hale +3
The use of inappropriate language -- such as outdated, exclusionary, or non-patient-centered terms -- medical instructional materials can significantly influence clinical training,…
A Scaling Law for Token Efficiency in LLM Fine-Tuning Under Fixed Compute Budgets
Ryan Lagasse, Aidan Kierans, Avijit Ghosh +1
We introduce a scaling law for fine-tuning large language models (LLMs) under fixed compute budgets that explicitly accounts for data composition. Conventional approaches measure t…
Stop treating `AGI' as the north-star goal of AI research
Borhane Blili-Hamelin, Christopher Graziul, Leif Hancox-Li +13
The AI research community plays a vital role in shaping the scientific, engineering, and societal goals of AI research. In this position paper, we argue that focusing on the highly…
Towards Fairer Health Recommendations: finding informative unbiased samples via Word Sense Disambiguation
Gavin Butts, Pegah Emdad, Jethro Lee +5
There have been growing concerns around high-stake applications that rely on models trained with biased data, which consequently produce biased predictions, often harming the most…
ClaimCompare: A Data Pipeline for Evaluation of Novelty Destroying Patent Pairs
Arav Parikh, Shiri Dori-Hacohen
A fundamental step in the patent application process is the determination of whether there exist prior patents that are novelty destroying. This step is routinely performed by both…
Reducing Biases towards Minoritized Populations in Medical Curricular Content via Artificial Intelligence for Fairer Health Outcomes
Chiman Salavati, Shannon Song, Willmar Sosa Diaz +4
Biased information (recently termed bisinformation) continues to be taught in medical curricula, often long after having been debunked. In this paper, we introduce BRICC, a firstin…
Quantifying Misalignment Between Agents: Towards a Sociotechnical Understanding of Alignment
Aidan Kierans, Avijit Ghosh, Hananel Hazan +1
Existing work on the alignment problem has focused mainly on (1) qualitative descriptions of the alignment problem; (2) attempting to align AI actions with human interests by focus…
SynDy: Synthetic Dynamic Dataset Generation Framework for Misinformation Tasks
Michael Shliselberg, Ashkan Kazemi, Scott A. Hale +1
Diaspora communities are disproportionately impacted by off-the-radar misinformation and often neglected by mainstream fact-checking efforts, creating a critical need to scale-up e…
Current and Near-Term AI as a Potential Existential Risk Factor
Benjamin S. Bucknall, Shiri Dori-Hacohen
There is a substantial and ever-growing corpus of evidence and literature exploring the impacts of Artificial intelligence (AI) technologies on society, politics, and humanity as a…
Fairness via AI: Bias Reduction in Medical Information
Shiri Dori-Hacohen, Roberto Montenegro, Fabricio Murai +4
Most Fairness in AI research focuses on exposing biases in AI systems. A broader lens on fairness reveals that AI can serve a greater aspiration: rooting out societal inequities fr…
Report on the First HIPstIR Workshop on the Future of Information Retrieval
Laura Dietz, Bhaskar Mitra, Jeremy Pickens +21
The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR. The first ite…
Is Climate Change Controversial? Modeling Controversy as Contention Within Populations
Shiri Dori-Hacohen, Myungha Jang, James Allan
A growing body of research focuses on computationally detecting controversial topics and understanding the stances people hold on them. Yet gaps remain in our theoretical and pract…