Finspector: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models

2023/05/26 by Bum Chul Kwon, Kwon, Bum Chul, Nandana Mihindukulasooriya +1 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Text Readability and Simplification

paper · pdf · doi:10.48550/arxiv.2305.16937

openalex publication_date 2023/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Pre-trained transformer-based language models are becoming increasingly popular due to their exceptional performance on various benchmarks. However, concerns persist regarding the presence of hidden biases within these models, which can lead to discriminatory outcomes and reinforce harmful stereotypes. To address this issue, we propose Finspector, a human-centered visual inspection tool designed to detect biases in different categories through log-likelihood scores generated by language models. The goal of the tool is to enable researchers to easily identify potential biases using visual analytics, ultimately contributing to a fairer and more just deployment of these models in both academic and industrial settings. Finspector is available at https://github.com/IBM/finspector.

Cited by

• Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safety

Related

• Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Heads
• Fine-Grained Bias Detection in LLM: Enhancing detection mechanisms for nuanced biases
• Detecting Natural Language Biases with Prompt-based Learning
• A Comprehensive Study of Implicit and Explicit Biases in Large Language Models
• Biases in the Blind Spot: Detecting What LLMs Fail to Mention