Prompt and Prejudice
Lorenzo Berlincioni, Luca Cultrera, Federico Becattini, Marco Bertini,, Alberto Del Bimbo

TL;DR
This paper examines demographic biases in LLMs and VLMs by appending first names to ethical scenarios, introduces a new bias benchmark, and emphasizes the importance of responsible AI practices.
Contribution
It proposes a novel method to reveal biases using demographic names, and introduces the Pratical Scenarios Benchmark for assessing biases in real-world decision-making.
Findings
Biases vary across different demographic names.
The benchmark enables comprehensive comparison of model biases.
Models show significant biases in practical decision scenarios.
Abstract
This paper investigates the impact of using first names in Large Language Models (LLMs) and Vision Language Models (VLMs), particularly when prompted with ethical decision-making tasks. We propose an approach that appends first names to ethically annotated text scenarios to reveal demographic biases in model outputs. Our study involves a curated list of more than 300 names representing diverse genders and ethnic backgrounds, tested across thousands of moral scenarios. Following the auditing methodologies from social sciences we propose a detailed analysis involving popular LLMs/VLMs to contribute to the field of responsible AI by emphasizing the importance of recognizing and mitigating biases in these systems. Furthermore, we introduce a novel benchmark, the Pratical Scenarios Benchmark (PSB), designed to assess the presence of biases involving gender or demographic prejudices in…
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Taxonomy
TopicsNames, Identity, and Discrimination Research · Authorship Attribution and Profiling · Ethics and Social Impacts of AI
