Evaluative Item-Contrastive Explanations in Rankings

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Alessandro Castelnovo, Riccardo Crupi, Nicolò Mombelli, Gabriele Nanino, Daniele Regoli
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引用次数: 0

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.

Abstract Image

排名中的评价性项目对比解释
人工智能在推动自动化决策方面取得的巨大成功在学术界和工业界都有目共睹。在众多的应用中,排名系统在各个领域都占有重要地位。本文主张应用一种特定形式的可解释人工智能--即对比解释--来解决排名问题。这种方法与评价式人工智能方法相结合时尤其有效,因为评价式人工智能方法会有意识地评估影响潜在排名的积极和消极方面。因此,本作品介绍了为排名系统量身定制的 "评价性项目对比解释",并通过在公开数据上进行的实验来说明其应用和特点。
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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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