RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models (2106.03521v1)

Abstract: Text representation models are prone to exhibit a range of societal biases, reflecting the non-controlled and biased nature of the underlying pretraining data, which consequently leads to severe ethical issues and even bias amplification. Recent work has predominantly focused on measuring and mitigating bias in pretrained LLMs. Surprisingly, the landscape of bias measurements and mitigation resources and methods for conversational LLMs is still very scarce: it is limited to only a few types of bias, artificially constructed resources, and completely ignores the impact that debiasing methods may have on the final performance in dialog tasks, e.g., conversational response generation. In this work, we present RedditBias, the first conversational data set grounded in the actual human conversations from Reddit, allowing for bias measurement and mitigation across four important bias dimensions: gender, race, religion, and queerness. Further, we develop an evaluation framework which simultaneously 1) measures bias on the developed RedditBias resource, and 2) evaluates model capability in dialog tasks after model debiasing. We use the evaluation framework to benchmark the widely used conversational DialoGPT model along with the adaptations of four debiasing methods. Our results indicate that DialoGPT is biased with respect to religious groups and that some debiasing techniques can remove this bias while preserving downstream task performance.

Overview of "RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational LLMs"

The paper "RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational LLMs" presents a novel dataset and evaluation framework designed to assess and mitigate biases within conversational LLMs. This work addresses a significant gap in the current landscape, where the focus on bias within conversational models often lacks comprehensive real-world grounding and multi-dimensional analysis. The authors introduce RedditBias, a resource derived from actual human conversations on Reddit, with annotations across four major bias dimensions: gender, race, religion, and queerness.

Key Contributions

1. RedditBias Dataset: The authors delineate the creation of RedditBias, which contains comments from Reddit that are annotated for bias across multiple dimensions, specifically targeting more than simple gender bias which is often the focus of previous work. The dataset is structured to enable a nuanced understanding of latent biases in conversation settings and is crafted from real-world data rather than synthetic or artificially constructed resources.
2. Evaluation Framework: The paper introduces a framework that measures bias via perplexity differences in generated language against counterfactual instances. It also evaluates model performance on specific downstream dialog tasks such as dialog state tracking and conversational response generation, ensuring that debiasing efforts do not compromise the model's functional efficacy.
3. Debiasing Methods: Four debiasing strategies adapted for conversational LLMs are applied: LLM Debiasing Loss (LMD), Attribute Distance Debiasing (ADD), Hard Debiasing Loss (HD), and Counterfactual Augmentation (CDA). The effectiveness of each method is assessed not only by its ability to reduce bias but also by its impact on dialog performance metrics.

Results and Analysis

The experimental results reveal that despite prior biases removal efforts during DialoGPT's training data preprocessing, significant biases persist, particularly around religious dimensions in conversational models. Notably, HD and CDA methods are shown to effectively mitigate this bias while preserving performance metrics across dialog tasks.

These findings underscore the subtle nature of biases that are often not eliminated by straightforward pre-processing techniques, highlighting the importance of the comprehensive evaluation and application of debiasing methods tailored for the complex dynamics of conversational AI.

Implications and Future Directions

Practically, the application of RedditBias and the associated framework provides a robust tool for ongoing bias detection and correction in conversational systems, pivotal for ethical AI deployment. Theoretical implications suggest more advanced multi-dimensional bias metrics are necessary for future frameworks to effectively capture the intricacies of biased representations without degrading model performance.

Future research may focus on the intersectionality of biases and embedding nuanced bias correction mechanisms that are adaptable across different conversational contexts and user demographics. Additionally, expanding the dataset to cover a broader spectrum of social identity factors could provide deeper insights into cascading societal implications of biases in AI systems.

In summary, this work invites an essential discourse within the research community regarding fairness and ethical considerations in AI, providing a foundational resource and methodology that can be expanded upon to meet the evolving challenges posed by advanced conversational AI systems.