Michiel A. Bakker

Large-scale public deliberations generate thousands of free-form contributions that must be synthesized into representative and neutral summaries for policy use. While LLMs have been shown as a promising tool to generate summaries for large-scale deliberations, they also risk underrepresenting minority perspectives, raising fairness concerns in high-stakes contexts. Studying and fixing these issues requires a comprehensive evaluation at a large scale, yet current practice often relies on LLMs as judges, which show weak alignment with human judgments. We introduce DeliberationBank, a large-scale, human-grounded benchmark for deliberation summarization that contains (1) 3,000 participant-generated opinions across ten deliberation questions and (2) 4,500 human annotations evaluating summaries along four dimensions: representativeness, informativeness, neutrality, and policy approval. Using this benchmark, we train DeliberationJudge, a domain-aligned evaluator that provides more reliable and efficient assessments than general-purpose LLM judges. With this evaluation framework, we benchmark 18 LLM summarizers and uncover consistent weaknesses, including systematic underrepresentation of minority viewpoints. Our benchmark and evaluator offer a scalable and reliable foundation for assessing deliberation summarization systems, supporting the development of more representative, equitable, and policy-relevant AI tools.

People often optimize for *long-term* goals in collaboration: A mentor or companion doesn't just answer questions, but also scaffolds learning, tracks progress, and prioritizes the other person's growth over immediate results. In contrast, current AI systems are fundamentally *short-sighted* collaborators -- optimized for providing instant and complete responses, without ever saying no (unless for safety reasons). What are the consequences of this dynamic? Here, through a series of randomized controlled trials on human-AI interactions (N=1,222), we provide *causal* evidence for two key consequences of AI assistance: reduced persistence and impairment of unassisted performance. Across a variety of tasks, including mathematical reasoning and reading comprehension, we find that although AI assistance improves performance in the short-term, people perform significantly worse without AI and are less likely to attempt the tasks. Notably, these effects emerge after only brief interactions with AI ($\sim$10 minutes). These findings are particularly concerning because persistence is foundational to skill acquisition and is one of the strongest predictors of long-term learning. We posit that persistence is reduced because AI conditions people to expect immediate answers, thereby denying them the experience of working through challenges on their own. These results suggest the need for AI model development to prioritize scaffolding long-term competence alongside immediate task completion.