Towards Understanding and Mitigating Social Biases in Language Models

Abstract: As machine learning methods are deployed in real-world settings such as healthcare, legal systems, and social science, it is crucial to recognize how they shape social biases and stereotypes in these sensitive decision-making processes. Among such real-world deployments are large-scale pretrained LMs that can be potentially dangerous in manifesting undesirable representational biases - harmful biases resulting from stereotyping that propagate negative generalizations involving gender, race, religion, and other social constructs. As a step towards improving the fairness of LMs, we carefully define several sources of representational biases before proposing new benchmarks and metrics to measure them. With these tools, we propose steps towards mitigating social biases during text generation. Our empirical results and human evaluation demonstrate effectiveness in mitigating bias while retaining crucial contextual information for high-fidelity text generation, thereby pushing forward the performance-fairness Pareto frontier.

• The paper introduces a robust framework for identifying and mitigating token-level and sentence-level social biases in language models using novel metrics and debiasing strategies.
• It employs fine-grained f-divergences and sentiment classifiers to benchmark representational biases on real-world datasets.
• Empirical results on GPT-2 show that the proposed A-INLP method effectively lowers stereotype scores while maintaining text generation quality.

Towards Understanding and Mitigating Social Biases in LLMs

The paper "Towards Understanding and Mitigating Social Biases in LLMs" provides a formal and systematic exploration of inherent biases in LMs, emphasizing the potential impact of these biases in crucial domains such as healthcare and legal systems. The authors, Liang, Wu, Morency, and Salakhutdinov from Carnegie Mellon University, propose a structured methodology to both quantify and mitigate these biases, contributing valuable tools and novel methodologies to NLP.

Core Contributions

1. Defining Sources of Biases: The research carefully delineates two main sources of representational biases in LMs:
   • Fine-grained Local Biases: These are biases appearing at the token level during generation, such as an LM being more likely to associate certain words with specific demographics, e.g., "doctor" with "male".
   • High-level Global Biases: These biases span across entire generated sentences and phrases, often reflecting stereotypes or representing social groups inaccurately.
2. Measurement Tools: The authors propose benchmarks and metrics designed to measure these defined representational biases. These include f-divergences for fine-grained biases and sentiment/regard classifiers for global biases. The paper suggests innovative ways to use diverse, real-world context datasets to evaluate these biases effectively, moving beyond template-driven evaluations common in prior studies.
3. Mitigation Strategies: A significant technical contribution is the development of Autoregressive INLP (A-INLP) — an adaptive method for post-hoc debiasing. This method extends Iterative Nullspace Projection (INLP) by adapting it for token-level debiasing in autoregressive generation. This approach includes dynamically finding bias-sensitive tokens and adjusting debiasing strength across token generation steps.

Empirical Findings

The study offers comprehensive empirical evaluations using the proposed techniques on GPT-2, demonstrating their effectiveness in bias reduction while maintaining text generation quality. The outlined methods achieve measurable improvements over existing approaches like INLP and show a balanced trade-off between performance (measured by LM scores) and fairness (reduction in stereotype scores) as evaluated on datasets such as StereoSet.

Implications and Future Directions

This work sets a foundation for mitigating biases in LMs, which if left unaddressed, can propagate detrimental stereotypes and false generalizations, exacerbating injustices rather than alleviating them. The methodologies advanced by the authors offer a feasible direction towards fairer NLP systems suitable for ethically-sensitive deployments.

The implications extend into future AI research and applications:

• Theoretical Expansion: Future work might explore multi-dimensional biases, considering factors like intersectionality, to extend the robustness of bias detection.
• AI System Design: The integration of fairness mechanisms into AI training pipelines could be explored, potentially incorporating these post-hoc strategies into real-time systems that interact with diverse user bases.
• Cross-lingual and Cultural Considerations: Given global applications of LMs, adapting these tools to understand cultural contexts and languages can enhance the fairness and applicability of AI solutions worldwide.

In summary, this paper brings forth a nuanced examination of biases inherent in language generation models and provides an actionable framework for addressing these issues. These contributions are timely and pertinent, as AI systems continue to play an increasingly influential role in societal functions.