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).
Delegation-Based Personal Data Processing Request Notarization Framework for GDPR Based on Private Blockchain
