- ML is increasingly being used to create data-driven decision-making systems for which an algorithmic solution is not feasible due to the complexity of the problem.
- The volume of data, the relations among the pieces of information collected, and the continuous data acquisition and learning activities conducted to improve the accuracy of ML-intensive systems, make it unfeasible for developers to properly verify that such systems act according to the ethical principles raised by the European Artificial Intelligence Act and make predictions that do not perpetuate discrimination against sensitive groups (Zhang, 2021).
- The relevance of software fairness in ML-intensive systems has been also made popular by infamous incidents happened to the recruitment instrument employed by Amazon (Reuters, 2018) and the criminal recidivism predictions made by the commercial risk assessment software COMPAS (AI Incident Base, 2016).