Hugging Face Trending Papers
Are AI Decision Models Biased? A Case Study of Jev
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arXiv AI
Sep 23
Biased AI improves human performance but reduces perceived helpfulness
The study tests deliberately biased AI assistants and finds that such bias improves human performance on tasks like misinformation evaluation, financial investment, and graduate education compared to neutral AI. However, participants undervalue biased AI and overvalue neutral AI, even when performance is similar. When two AI biases flank a participant’s perspective, performance gains are maintained while reducing the perceived cost and one‑sided influence.
arXiv AI
Jul 14
Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling
arXiv:2607. 11632v1 Announce Type: new Abstract: Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality.
arXiv Computation and Language
2d ago
S1CogBench: What System One Decision Models Can and Cannot Do on Cognitive Experiments
S1CogBench is a new benchmark that evaluates System One decision models—typed decision models like Jev and Laya—on 24 cognitive tasks. It tests whether these models can use evidence, respond to feedback, handle memory load, and anticipate future opportunities, while keeping conditions and scoring separate from any disclosed adaptations. The study finds wide variability in reliability among decision models, shows that good performance does not guarantee human-like predictions, and reveals that speed largely stems from generating decisions without text output.
arXiv AI
Aug 17
BiasTrace: Linking Reasoning Behaviours to Biased Outputs in LLMs
arXiv:2608. 14161v1 Announce Type: new Abstract: LLMs exhibit social biases that can produce inaccurate and discriminatory inferences, posing risks in high-stakes applications.
arXiv Computation and Language
2d ago
Swap, Replicate, Read the Mass: A Protocol for Auditing Demographic Bias in System One Decision Models
The paper introduces a comprehensive audit protocol for System One decision models, which provide typed, probability‑scored answers without human oversight. Using three adapted benchmarks—BBQ, CrowS‑Pairs, and SilverSpoon—it evaluates twelve models plus a no‑thinking baseline across 1,638,000 decisions, revealing widespread stereotype alignment, order‑dependent responses, and confidence instability. The study demonstrates that swapping answer options and reversing question order significantly alters model outputs, underscoring the need for order‑invariant scoring in procurement.
Hugging Face Trending Papers
Jul 13
Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling
Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality. Cumulative prospect theory (CPT) has been widely recognized as an effective framework for characterizing such behavioral patterns.