LangBiTe: A Platform for Testing Bias in Large Language Models

2024-04-29

arXiv.org

AI Summary

Discriminatory biases acquired by large language models (LLMs) from internet-scale training data pose significant ethical risks in real-world deployment, necessitating traceable and quantifiable systematic evaluation methods. To address this, we propose LangBiTe—the first end-to-end bias testing framework that directly maps ethical requirements to empirical bias detection. LangBiTe integrates prompt-driven automated test case generation, a hybrid rule-and-model-based “built-in oracle” for bias classification, and bidirectional traceability linking policy-level requirements to model responses. This enables full-chain attribution of bias—from normative definitions to observable model behavior. The framework supports modular scenario configuration, substantially improving scalability, reproducibility, and quantitative precision in bias assessment. Empirical evaluation demonstrates its effectiveness in identifying and localizing socially sensitive biases across diverse demographic and contextual dimensions. LangBiTe provides a practical, engineering-oriented pathway for operationalizing LLM ethics governance.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The integration of Large Language Models (LLMs) into various software applications raises concerns about their potential biases. Typically, those models are trained on a vast amount of data scrapped from forums, websites, social media and other internet sources, which may instill harmful and discriminating behavior into the model. To address this issue, we present LangBiTe, a testing platform to systematically assess the presence of biases within an LLM. LangBiTe enables development teams to tailor their test scenarios, and automatically generate and execute the test cases according to a set of user-defined ethical requirements. Each test consists of a prompt fed into the LLM and a corresponding test oracle that scrutinizes the LLM's response for the identification of biases. LangBite provides users with the bias evaluation of LLMs, and end-to-end traceability between the initial ethical requirements and the insights obtained.

Problem

• Detecting biases in Large Language Models (LLMs)
• Automating bias testing with customizable scenarios
• Linking ethical requirements to bias evaluation results

Innovation

• Platform for systematic bias testing in LLMs
• Customizable test scenarios based on ethics
• End-to-end traceability from requirements to insights