Empathy and the Right to Be an Exception: What LLMs Can and Cannot Do
This addresses a philosophical and ethical problem in AI regarding the limitations of LLMs in handling human individuality, which is incremental as it builds on existing debates about theory of mind in AI.
The paper examines whether large language models (LLMs) can honor an individual's right to be an exception by considering internal mental states like beliefs and intentions, rather than relying solely on linguistic patterns from datasets, and concludes that empathy has distinct significance beyond predictive accuracy.
Advances in the performance of large language models (LLMs) have led some researchers to propose the emergence of theory of mind (ToM) in artificial intelligence (AI). LLMs can attribute beliefs, desires, intentions, and emotions, and they will improve in their accuracy. Rather than employing the characteristically human method of empathy, they learn to attribute mental states by recognizing linguistic patterns in a dataset that typically do not include that individual. We ask whether LLMs' inability to empathize precludes them from honoring an individual's right to be an exception, that is, from making assessments of character and predictions of behavior that reflect appropriate sensitivity to a person's individuality. Can LLMs seriously consider an individual's claim that their case is different based on internal mental states like beliefs, desires, and intentions, or are they limited to judging that case based on its similarities to others? We propose that the method of empathy has special significance for honoring the right to be an exception that is distinct from the value of predictive accuracy, at which LLMs excel. We conclude by considering whether using empathy to consider exceptional cases has intrinsic or merely practical value and we introduce conceptual and empirical avenues for advancing this investigation.