Formalising trade-offs beyond algorithmic fairness: lessons from ethical philosophy and welfare economics

Type Journal Article Author Michelle Seng Ah Lee Author Luciano Floridi Author Jatinder Singh URL https://doi.org/10.1007/s43681-021-00067-y Publication AI and Ethics ISSN 2730-5961 Date 2021-06-12 Journal Abbr AI Ethics DOI 10.1007/s43681-021-00067-y Accessed 2021-08-14 00:45:26 Library Catalog Springer Link Language en Abstract There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented within narrow and targeted fairness toolkits for algorithm assessments that are difficult to integrate into an algorithm’s broader ethical assessment. In this paper, we derive lessons from ethical philosophy and welfare economics as they relate to the contextual factors relevant for fairness. In particular we highlight the debate around the acceptability of particular inequalities and the inextricable links between fairness, welfare and autonomy. We propose Key Ethics Indicators (KEIs) as a way towards providing a more holistic understanding of whether or not an algorithm is aligned to the decision-maker’s ethical values. Short Title Formalising trade-offs beyond algorithmic fairness