2 citations · 3 across the 11 of their papers we have counts for
13 papers
Algorithmic Impact Reveals the Hidden Social Choice Structure of Alignment
Zachary Wojtowicz, Michelle Si, Finale Doshi-Velez +1
When an AI algorithm makes decisions that affect more than one person, aligning it becomes a problem of social choice: how should people's divergent preferences about system behavi…
How RLHF Amplifies Sycophancy
Itai Shapira, Gerdus Benade, Ariel D. Procaccia
Large language models often exhibit increased sycophantic behavior after preference-based post-training, showing a stronger tendency to affirm a user's stated or implied belief eve…
Question the Questions: Auditing Representation in Online Deliberative Processes
Soham De, Lodewijk Gelauff, Ashish Goel +3
A central feature of many deliberative processes, such as citizens' assemblies and deliberative polls, is the opportunity for participants to engage directly with experts. While pa…
Incentives in Federated Learning with Heterogeneous Agents
Ariel D. Procaccia, Han Shao, Itai Shapira
Federated learning promises significant sample-efficiency gains by pooling data across multiple agents, yet incentive misalignment is an obstacle: each update is costly to the cont…
Metritocracy: Representative Metrics for Lite Benchmarks
Ariel Procaccia, Benjamin Schiffer, Serena Wang +1
A common problem in LLM evaluation is how to choose a subset of metrics from a full suite of possible metrics. Subset selection is usually done for efficiency or interpretability r…
Alternates, Assemble! Selecting Optimal Alternates for Citizens' Assemblies
Angelos Assos, Carmel Baharav, Bailey Flanigan +1
Citizens' assemblies are an increasingly influential form of deliberative democracy, where randomly selected people discuss policy questions. The legitimacy of these assemblies hin…
Pairwise Calibrated Rewards for Pluralistic Alignment
Daniel Halpern, Evi Micha, Ariel D. Procaccia +1
Current alignment pipelines presume a single, universal notion of desirable behavior. However, human preferences often diverge across users, contexts, and cultures. As a result, di…
The Proportional Veto Principle for Approval Ballots
Daniel Halpern, Ariel D. Procaccia, Warut Suksompong
The proportional veto principle, which captures the idea that a candidate vetoed by a large group of voters should not be chosen, has been studied for ranked ballots in single-winn…
Strategic Classification With Externalities
Safwan Hossain, Evi Micha, Yiling Chen +1
We propose a new variant of the strategic classification problem: a principal reveals a classifier, and agents report their (possibly manipulated) features to be classified. Mo…
Federated Assemblies
Daniel Halpern, Ariel D. Procaccia, Ehud Shapiro +1
A citizens' assembly is a group of people who are randomly selected to represent a larger population in a deliberation. While this approach has successfully strengthened democracy,…
Learning Social Welfare Functions
Kanad Shrikar Pardeshi, Itai Shapira, Ariel D. Procaccia +1
Is it possible to understand or imitate a policy maker's rationale by looking at past decisions they made? We formalize this question as the problem of learning social welfare func…
Bias Detection Via Signaling
Yiling Chen, Tao Lin, Ariel D. Procaccia +2
We introduce and study the problem of detecting whether an agent is updating their prior beliefs given new evidence in an optimal way that is Bayesian, or whether they are biased t…
Axioms for AI Alignment from Human Feedback
Luise Ge, Daniel Halpern, Evi Micha +4
In the context of reinforcement learning from human feedback (RLHF), the reward function is generally derived from maximum likelihood estimation of a random utility model based on…