Tootfinder

Classical AI vs. LLMs for Decision-Maker Alignment in Health Insurance Choices
Mallika Mainali, Harsha Sureshbabu, Anik Sen, Christopher B. Rauch, Noah D. Reifsnyder, John Meyer, J. T. Turner, Michael W. Floyd, Matthew Molineaux, Rosina O. Weber
https://arxiv.org/abs/2510.06093

Classical AI vs. LLMs for Decision-Maker Alignment in Health Insurance Choices
As algorithmic decision-makers are increasingly applied to high-stakes domains, AI alignment research has evolved from a focus on universal value alignment to context-specific approaches that account for decision-maker attributes. Prior work on Decision-Maker Alignment (DMA) has explored two primary strategies: (1) classical AI methods integrating case-based reasoning, Bayesian reasoning, and naturalistic decision-making, and (2) large language model (LLM)-based methods leveraging prompt engine…

Strengthening legal protection against discrimination by algorithms and artificial intelligence
Frederik J. Zuiderveen Borgesius
https://arxiv.org/abs/2510.02859 https://…

Strengthening legal protection against discrimination by algorithms and artificial intelligence
Algorithmic decision-making and other types of artificial intelligence (AI) can be used to predict who will commit crime, who will be a good employee, who will default on a loan, etc. However, algorithmic decision-making can also threaten human rights, such as the right to non-discrimination. The paper evaluates current legal protection in Europe against discriminatory algorithmic decisions. The paper shows that non-discrimination law, in particular through the concept of indirect discriminatio…

Who is a Better Matchmaker? Human vs. Algorithmic Judge Assignment in a High-Stakes Startup Competition
Sarina Xi, Orelia Pi, Miaomiao Zhang, Becca Xiong, Jacqueline Ng Lane, Nihar B. Shah
https://arxiv.org/abs/2510.12692

Who is a Better Matchmaker? Human vs. Algorithmic Judge Assignment in a High-Stakes Startup Competition
There is growing interest in applying artificial intelligence (AI) to automate and support complex decision-making tasks. However, it remains unclear how algorithms compare to human judgment in contexts requiring semantic understanding and domain expertise. We examine this in the context of the judge assignment problem, matching submissions to suitably qualified judges. Specifically, we tackled this problem at the Harvard President's Innovation Challenge, the university's premier venture compet…