Can everyday AI be ethical. Fairness of Machine Learning Algorithms
This addresses the problem of ensuring fairness and trust in AI systems for developers, users, and regulators, but it is incremental as it builds on existing legal and ethical discussions without introducing new methods.
The paper examines the risks of discrimination, transparency, and quality in machine learning algorithms used for automatic decision-making, highlighting the need for technological solutions to detect or reduce discrimination and provide explanations for decisions. It proposes institutional controls, ethical charters, and external audits as ways to ensure algorithms operate within a strict ethical framework.
Combining big data and machine learning algorithms, the power of automatic decision tools induces as much hope as fear. Many recently enacted European legislation (GDPR) and French laws attempt to regulate the use of these tools. Leaving aside the well-identified problems of data confidentiality and impediments to competition, we focus on the risks of discrimination, the problems of transparency and the quality of algorithmic decisions. The detailed perspective of the legal texts, faced with the complexity and opacity of the learning algorithms, reveals the need for important technological disruptions for the detection or reduction of the discrimination risk, and for addressing the right to obtain an explanation of the auto- matic decision. Since trust of the developers and above all of the users (citizens, litigants, customers) is essential, algorithms exploiting personal data must be deployed in a strict ethical framework. In conclusion, to answer this need, we list some ways of controls to be developed: institutional control, ethical charter, external audit attached to the issue of a label.