<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper argues that if we want a sustainable environment of desirable AI systems, we should aim not only at transparent, explainable, fair, lawful, and accountable algorithms, but we also should seek for “just” algorithms, that is, automated decision-making systems that include all the above-mentioned qualities (transparency, explainability, fairness, lawfulness, and accountability). This is possible through a practical “justification” statement and process (eventually derived from algorithmic impact assessment) through which the data controller proves, in practical ways, why the AI system is not unfair, not discriminatory, not obscure, not unlawful, etc. In other words, this justification (eventually derived from data protection impact assessment on the AI system) proves the legality of the system with respect to all data protection principles (fairness, lawfulness, transparency, purpose limitation, data minimisation, accuracy, storage limitation, integrity, and accountability). All these principles are necessary components of a broader concept of just algorithmic decision-making and is already required by the GDPR, in particular considering: the data protection principles (Article 5), the need to enable (meaningful) contestations of automated decisions (Article 22) and the need to assess the AI system necessity, proportionality and legality under the Data Protection Impact Assessment model framework. (Article 35).</p>
</abstract>

This paper argues that if we want a sustainable environment of desirable AI systems, we should aim not only at transparent, explainable, fair, lawful, and accountable algorithms, but we also should seek for “just” algorithms, that is, automated decision-making systems that include all the above-mentioned qualities (transparency, explainability, fairness, lawfulness, and accountability). This is possible through a practical “justification” statement and process (eventually derived from algorithmic impact assessment) through which the data controller proves, in practical ways, why the AI system is not unfair, not discriminatory, not obscure, not unlawful, etc. In other words, this justification (eventually derived from data protection impact assessment on the AI system) proves the legality of the system with respect to all data protection principles (fairness, lawfulness, transparency, purpose limitation, data minimisation, accuracy, storage limitation, integrity, and accountability). All these principles are necessary components of a broader concept of just algorithmic decision-making and is already required by the GDPR, in particular considering: the data protection principles (Article 5), the need to enable (meaningful) contestations of automated decisions (Article 22) and the need to assess the AI system necessity, proportionality and legality under the Data Protection Impact Assessment model framework. (Article 35).

“Just” Algorithms: Justification (Beyond Explanation) of Automated Decisions Under the General Data Protection Regulation

Law and Business

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper argues that if we want a sustainable environment of desirable AI systems, we should aim not only at transparent, explainable, fair, lawful, and...

“Just” Algorithms: Justification (Beyond Explanation) of Automated Decisions Under the General Data Protection Regulation

Article Category: Research Article

Published Online: Nov 20, 2021

Page range: 16 - 28

DOI: https://doi.org/10.2478/law-2021-0003

Keywords
AI, justification, automated decision-making, explainability, Algorithmic Impact Assessment

© 2021 Gianclaudio Malgieri, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

“Just” Algorithms: Justification (Beyond Explanation) of Automated Decisions Under the General Data Protection Regulation

Article Category: Research Article

Published Online: Nov 20, 2021

Page range: 16 - 28

DOI: https://doi.org/10.2478/law-2021-0003

KeywordsAI, justification, automated decision-making, explainability, Algorithmic Impact Assessment

© 2021 Gianclaudio Malgieri, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
AI, justification, automated decision-making, explainability, Algorithmic Impact Assessment