Enhancing User Agency in Generative Language Models: Algorithmic Recourse for Toxicity Filtering
Generative Language Models (GLMs) are being integrated into various sectors such as customer service and content creation. Moderating potentially harmful content while preserving linguistic diversity and inclusiveness is crucial.
The Challenge
Toxicity scoring systems help filter out offensive language but often struggle with false positives, especially concerning marginalized communities. This restricts access to relevant information and stifles language reclamation efforts.
The Solution
Dynamic thresholding for toxicity scoring in GLMs is introduced to allow users to override toxicity thresholds for specific phrases while protecting them from unnecessary exposure to toxic language. This fosters a sense of agency among users and tailors the GLM’s responses to align more closely with individual and societal norms.
The Results
The dynamic thresholding mechanism demonstrated effectiveness in a pilot study, securing an average System Usability Scale score of 66.8. Participants appreciated the enhanced control and engagement facilitated by the dynamic thresholding, enabling adjustments to content filtering based on individual user preferences.
Conclusion
Exploring dynamic thresholding for toxicity scoring in GLMs offers promising insights into the future of user experience and agency. It represents a significant step towards more inclusive and flexible technology that respects the evolving nature of language and the diverse needs of its users.
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