Evaluative Item-Contrastive Explanations in Rankings

Alessandro Castelnovo, Riccardo Crupi, Nicolò Mombelli, Gabriele Nanino, Daniele Regoli
12/14/2023
cs.IRcs.AIcs.CY

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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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Cite this paper

@article{castelnovo2023evaluative,
  title  = {Evaluative Item-Contrastive Explanations in Rankings},
  author = {Alessandro Castelnovo and Riccardo Crupi and Nicolò Mombelli and Gabriele Nanino and Daniele Regoli},
  year   = {2023},
  eprint = {2312.10094},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.10094v1}
}

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