  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.
