78 citations · 85 across the 7 of their papers we have counts for
9 papers
Large Language Models Exhibit Human-Like Bayesian Hypocrisy
Nykko Vitali, Mahzarin R. Banaji
Given recent achievements of large language models (LLMs), frontier models are expected to perform well on Bayesian reasoning tasks, at least as well as humans. Furthermore, there…
Language Models Embody and Amplify Human Cognitive Distortions: What Is to Be Done?
Arnau Marin-Llobet, Steven A. Lehr, Mahzarin R. Banaji
Human judgment is fundamentally prone to error. A promise of AI is that it will rid decisions of bias and ensure a fairer and safer world for all. Yet research unequivocally demons…
Vision-Language Models Suppress Female Representations Under Ambiguous Input
Arnau Marin-Llobet, Simon Henniger, Mahzarin R. Banaji
Alignment teaches vision-language models (VLMs) to avoid expressing demographic biases, and when gender is clearly visible they largely succeed. Far less is known about ambiguous i…
Extreme Self-Preference in Language Models
Steven A. Lehr, Mary Cipperman, Mahzarin R. Banaji
Self-preference is a fundamental feature of biological organisms. Since large language models (LLMs) lack sentience, they might be expected to avoid such distortions. Yet, across 7…
Kernels of Selfhood: GPT-4o shows humanlike patterns of cognitive consistency moderated by free choice
Steven A. Lehr, Ketan S. Saichandran, Eddie Harmon-Jones +2
Large Language Models (LLMs) show emergent patterns that mimic human cognition. We explore whether they also mirror other, less deliberative human psychological processes. Drawing…
ChatGPT as Research Scientist: Probing GPT's Capabilities as a Research Librarian, Research Ethicist, Data Generator and Data Predictor
Steven A. Lehr, Aylin Caliskan, Suneragiri Liyanage +1
How good a research scientist is ChatGPT? We systematically probed the capabilities of GPT-3.5 and GPT-4 across four central components of the scientific process: as a Research Lib…
Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics
Aylin Caliskan, Pimparkar Parth Ajay, Tessa Charlesworth +2
The statistical regularities in language corpora encode well-known social biases into word embeddings. Here, we focus on gender to provide a comprehensive analysis of group-based b…
Evidence for Hypodescent in Visual Semantic AI
Robert Wolfe, Mahzarin R. Banaji, Aylin Caliskan
We examine the state-of-the-art multimodal "visual semantic" model CLIP ("Contrastive Language Image Pretraining") for the rule of hypodescent, or one-drop rule, whereby multiracia…
Learning Representations by Humans, for Humans
Sophie Hilgard, Nir Rosenfeld, Mahzarin R. Banaji +2
When machine predictors can achieve higher performance than the human decision-makers they support, improving the performance of human decision-makers is often conflated with impro…