mlsanityData to 7 Oct 2026

Evaluative Item-Contrastive Explanations in Rankings

arXiv 2312.10094 ↗First posted 14 Dec 2023cs.IR cs.AI cs.CYcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 52 references that are in the map. First posted on arXiv 14 Dec 2023.

Builds on

15 of 52 references.

References · 15

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  2. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  3. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  4. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  5. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  6. 2018Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negativescited by 261
  7. 2021A Survey of Human-in-the-loop for Machine Learningcited by 214
  8. 2018Fairness of Exposure in Rankingscited by 189
  9. 2020Interpretability of machine learning based prediction models in healthcarecited by 75
  10. 2023Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision supportcited by 65
  11. 2021Counterfactual Explainable Recommendationcited by 60
  12. 2019How model accuracy and explanation fidelity influence user trustcited by 44
3 more
  1. 2017Designing Fair Ranking Schemescited by 40
  2. 2021Fairness in Ranking: A Surveycited by 33
  3. 2021Counterfactual Explanations as Interventions in Latent Spacecited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024An Empirical Examination of the Evaluative AI Frameworkcited by 0
  2. 2025Q-STRUM Debate: Query-Driven Contrastive Summarization for Recommendation Comparisoncited by 0
  3. 2026Why This, Not That? Mining User Profiles for Pair-wise Counterfactualscited by 0