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Data-driven decision-making consequential to individuals raises importantquestions of accountability and justice. Indeed, European law providesindividuals limited rights to 'meaningful information about the logic' behindsignificant, autonomous decisions such as loan approvals, insurance quotes, andCV filtering. We undertake three experimental studies examining people'sperceptions of justice in algorithmic decision-making under different scenariosand explanation styles. Dimensions of justice previously observed in responseto human decision-making appear similarly engaged in response to algorithmicdecisions. Qualitative analysis identified several concerns and heuristicsinvolved in justice perceptions including arbitrariness, generalisation, and(in)dignity. Quantitative analysis indicates that explanation styles primarilymatter to justice perceptions only when subjects are exposed to multipledifferent styles---under repeated exposure of one style, scenario effectsobscure any explanation effects. Our results suggests there may be no 'best'approach to explaining algorithmic decisions, and that reflection on theirautomated nature both implicates and mitigates justice dimensions.

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