Explainable AI Reloaded: Challenging the XAI Status Quo in the Era of Large Language Models
<|reference_start|>Explainable AI Reloaded: Challenging the XAI Status Quo in the Era of Large Language Models: When the initial vision of Explainable (XAI) was articulated, the most popular framing was to open the (proverbial) "black-box" of AI so that we could understand the inner workings. With the advent of Large Language Models (LLMs), the very ability to open the black-box is increasingly limited especially when it comes to non-AI expert end-users. In this paper, we challenge the assumption of "opening" the black-box in the LLM era and argue for a shift in our XAI expectations. Highlighting the epistemic blind spots of an algorithm-centered XAI view, we argue that a human-centered perspective can be a path forward. We operationalize the argument by synthesizing XAI research along three dimensions: explainability outside the black-box, explainability around the edges of the black box, and explainability that leverages infrastructural seams. We conclude with takeaways that reflexively inform XAI as a domain.<|reference_end|>
@article{ehsan2024explainable,
    title={Explainable AI Reloaded: Challenging the XAI Status Quo in the Era of
  Large Language Models},
    author={Upol Ehsan, Mark O. Riedl},
    journal={arXiv preprint arXiv:2408.05345},
    year={2024},
    doi={10.1145/3686169.3686185},
    archivePrefix={arXiv},
    eprint={2408.05345},
    primaryClass={cs.HC cs.AI}
}