Social Bias Frames: Reasoning about Social and Power Implications of Language (1911.03891v3)

Abstract: Warning: this paper contains content that may be offensive or upsetting. Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but rather the implied meanings, that frame people's judgments about others. For example, given a statement that "we shouldn't lower our standards to hire more women," most listeners will infer the implicature intended by the speaker -- that "women (candidates) are less qualified." Most semantic formalisms, to date, do not capture such pragmatic implications in which people express social biases and power differentials in language. We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others. In addition, we introduce the Social Bias Inference Corpus to support large-scale modelling and evaluation with 150k structured annotations of social media posts, covering over 34k implications about a thousand demographic groups. We then establish baseline approaches that learn to recover Social Bias Frames from unstructured text. We find that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias (80% F1), they are not effective at spelling out more detailed explanations in terms of Social Bias Frames. Our study motivates future work that combines structured pragmatic inference with commonsense reasoning on social implications.

• The paper introduces Social Bias Frames that formalizes the extraction of social bias implications from language.
• It presents the Social Bias Inference Corpus (SBIC), a dataset with 150K annotated social media posts to reveal bias and offensiveness.
• The authors evaluate baseline models achieving an 80% F1 score, highlighting challenges in detecting nuanced bias for improved AI ethics.

Analyzing Social Bias in Language: A Model for Social Bias Frames

The paper "Social Bias Frames: Reasoning about Social and Power Implications of Language" introduces an innovative framework for understanding and analyzing the social biases inherent in language. The authors present a structured approach termed "Social Bias Frames" to discern the pragmatic layers that convey societal biases and stereotypes often not captured by semantic formalisms. The research focuses on implicatures—implied meanings inferred in communication—revealing how statements perpetuate social bias.

Conceptual Framework and Dataset

The principal contribution is the development of Social Bias Frames, a formalism that captures a wide range of social bias implications expressed in language. The authors complement this theoretical construct with a novel dataset called the Social Bias Inference Corpus (SBIC), comprising 150,000 annotations from social media posts. These annotations include both categorical labels indicating the offensiveness, intent to offend, and target group implications, as well as free-text statements elucidating these biases.

The dataset reflects a comprehensive annotation scheme. Annotators identify whether a post is offensive or intentionally so, whether it contains lewd content, and if it targets a demographic group. They also specify the implicated demographic group and the stereotypes or biases implied, collected in natural language form. This rich annotation framework offers potential for more nuanced models that can better understand the biases embedded in language.

Experimental Evaluation

Using the SBIC, the authors establish baseline models to recover Social Bias Frames from text, leveraging state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) networks. These models achieved an F1 score of 80% for high-level categorization tasks like identifying offensive content. However, they performed less effectively in generating specific bias implications articulated in social media content.

Implications and Future Research Directions

This paper's implications span both theoretical and practical domains. Theoretically, it contributes a nuanced understanding of how language conveys complex social biases. Practically, these insights are crucial for developing AI systems that interact responsibly with human users, with applications such as content moderation tools and AI-augmented writing platforms that flag potentially harmful content.

Despite the progress made, the paper highlights limitations in existing neural models' ability to spell out detailed social bias implications, calling for research into more sophisticated models that integrate structured pragmatic inference with commonsense reasoning about social dynamics. This direction could pave the way for AI systems capable of deeper social awareness, thus mitigating the risk of perpetuating harmful stereotypes and biases.

Conclusion

The paper demonstrates that while technology has made strides in detecting overtly toxic content, capturing nuanced social biases remains a complex challenge. The Social Bias Frames formalism and the accompanying SBIC dataset are instrumental steps towards more holistic and responsible AI systems, emphasizing the necessity for balanced models that are aware of diverse social contexts and power differentials in language. As research progresses, more robust models are needed to effectively address social bias implications and ensure ethical AI deployment in societal applications.