Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities. (arXiv:2111.06420v1 [cs.LG])

The past decade has seen significant progress in artificial intelligence
(AI), which has resulted in algorithms being adopted for resolving a variety of
problems. However, this success has been met by increasing model complexity and
employing black-box AI models that lack transparency. In response to this need,
Explainable AI (XAI) has been proposed to make AI more transparent and thus
advance the adoption of AI in critical domains. Although there are several
reviews of XAI topics in the literature that identified challenges and
potential research directions in XAI, these challenges and research directions
are scattered. This study, hence, presents a systematic meta-survey for
challenges and future research directions in XAI organized in two themes: (1)
general challenges and research directions in XAI and (2) challenges and
research directions in XAI based on machine learning life cycle’s phases:
design, development, and deployment. We believe that our meta-survey
contributes to XAI literature by providing a guide for future exploration in
the XAI area.

Source: https://arxiv.org/abs/2111.06420


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