'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, Eu- ropean law provides individuals limited rights to ‘meaningful information about the logic’ behind significant, autonomous deci- sions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people’s per- ceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previ- ously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualita- tive analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles—under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no ‘best’ approach to explaining al- gorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.