A data quality approach to the identification of discrimination risk in automated decision making systems

Antonio Vetrò, Marco Torchiano, Mariachiara Mecati

Abstract: Highlights•The automation of decision processes in public sector is rapidly expanding.•Automated decision-making (ADM) discriminates if trained with imbalanced data.•We propose to use balance measures to assess the risk of unfairness in ADM systems.•We observed varying abilities of the balance measures to detect discrimination.•The proposal resonates with the EU approach to regulate algorithmic systems.