arXiv:2408.05568v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit potentially harmful biases that reinforce culturally embedded stereotypes, influence moral judgments, or amplify positive evaluations of majority groups. We propose metacognitive myopia as a cognitive-ecological framework accounting for a conglomerate of established and emerging LLM biases. Our theoretical framework posits that biased samples in the information environment cause five symptoms of metacognitive myopia in LLMs: integration of invalid embeddings, susceptibility to redundant information,
This paper establishes a new theoretical framework for understanding persistent and widespread biases in advanced AI models, which are becoming increasingly integrated into society.
Understanding the intrinsic biases within large language models is critical for mitigating their potentially harmful societal impacts and ensuring fair and robust AI system development.
This research provides a cognitive-ecological framework, 'metacognitive myopia,' offering a new lens through which to analyze and potentially address fundamental LLM limitations beyond simple data filtering.
- · AI ethicists
- · Companies investing in bias mitigation research
- · Regulators developing AI governance frameworks
- · Developers ignoring bias in LLM deployment
- · Applications relying on unchecked LLM outputs
- · Organizations using LLMs without robust oversight
Increased focus on understanding and correcting biases inherent in LLM training data and architectures.
Development of new evaluation metrics and frameworks for assessing 'metacognitive myopia' and similar LLM shortfalls.
Potential for new regulatory requirements mandating transparency and explainability regarding LLM bias mechanisms.
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Read at arXiv cs.CL