The Trade-off Between Privacy & Quality for Counterfactual Explanations

Author:

Berning Sjoerd1ORCID,Dunning Vincent2ORCID,Spagnuelo Dayana2ORCID,Veugen Thijs3ORCID,van der Waa Jasper1ORCID

Affiliation:

1. Human Machine Teaming, Netherlands Organisation for Applied Scientific Research (TNO), Netherlands

2. Applied Cryptography & Quantum Algorithms, Netherlands Organisation for Applied Scientific Research (TNO), Netherlands

3. Applied Cryptography & Quantum Algorithms, Netherlands Organisation for Applied Scientific Research (TNO), University of Twente, Netherlands and Semantics, Cybersecurity & Services, University of Twente, Netherlands

Publisher

ACM

Reference30 articles.

1. Shahabeddin Abhari, Sharareh R Niakan Kalhori, Mehdi Ebrahimi, Hajar Hasannejadasl, and Ali Garavand. 2019. Artificial intelligence applications in type 2 diabetes mellitus care: focus on machine learning methods. Healthcare informatics research 25, 4 (2019), 248.

2. To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems

3. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

4. Isabel Barbera. [n. d.]. PLOT4AI - Privacy Library of Threats 4 Artificial Intelligence. https://plot4.ai/

5. Sarah Bauermeister, Christopher Orton, Simon Thompson, Roger A Barker, Joshua R Bauermeister, Yoav Ben-Shlomo, Carol Brayne, David Burn, Archie Campbell, Catherine Calvin, 2020. The dementias platform UK (DPUK) data portal. European journal of epidemiology 35 (2020), 601–611.

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