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arXiv · 2312.10094

Evaluative Item-Contrastive Explanations in Rankings

Alessandro Castelnovo·Riccardo Crupi·Nicolò Mombelli·Gabriele Nanino·Daniele Regoli
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Abstract

The remarkable success of Artificial Intelligence in advancing automated decision-making is evident both in academia and industry. Within the plethora of applications, ranking systems hold significant importance in various domains. This paper advocates for the application of a specific form of Explainable AI -- namely, contrastive explanations -- as particularly well-suited for addressing ranking problems. This approach is especially potent when combined with an Evaluative AI methodology, which conscientiously evaluates both positive and negative aspects influencing a potential ranking. Therefore, the present work introduces Evaluative Item-Contrastive Explanations tailored for ranking systems and illustrates its application and characteristics through an experiment conducted on publicly available data.

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Alessandro Castelnovo, Riccardo Crupi, Nicolò Mombelli, Gabriele Nanino, Daniele Regoli. 2023-12-14. Evaluative Item-Contrastive Explanations in Rankings. https://arxiv.org/abs/2312.10094

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