Data-driven decision-making consequential to individuals raises important
questions of accountability and justice. Indeed, European law provides
individuals limited rights to 'meaningful information about the logic' behind
significant, autonomous decisions such as loan approvals, insurance quotes, and
CV filtering. We undertake three experimental studies examining people's
perceptions of justice in algorithmic decision-making under different scenarios
and explanation styles. Dimensions of justice previously observed in response
to human decision-making appear similarly engaged in response to algorithmic
decisions. Qualitative 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 algorithmic decisions, and that reflection on their
automated nature both implicates and mitigates justice dimensions.

本研究采用三项实验研究人们在不同情境和解释方式下对算法决策中正义感的感知。研究结果表明，解释方式仅在被暴露于多种不同方式的情况下对正义感知产生影响，而在同一方式的重复暴露下，情境效应会掩盖任何解释的影响。这些结果表明，关于算法决策的最佳解释方式并不存在，并且自动化本质对正义维度的涉及和缓解都有影响。