Mutually beneficial exchanges in markets can be exploitative because one party takes advantage of an underlying injustice. For instance, employers of sweatshop workers are often accused of exploiting the desperate conditions of their employees, although the latter accept the terms of their employment voluntarily. A weakness of this account of exploitation is its tendency for over-inclusiveness. Certainly, given the prevalence of global and domestic socioeconomic inequalities, not all exchanges that take place against background injustices should be considered exploitative. This paper (...) offers a framework to identify exploitation in mutually beneficial exchange, focusing on the case of sweatshop labor. It argues that an employer can be viewed as taking unfair advantage of an underlying injustice if and only if the employer’s surplus from the exchange in the unjust state of affairs exceeds the surplus it could maximally obtain in a just state of affairs. The paper illustrates the applicability of this framework using three different conceptions of justice and argues that it is superior to microlevel accounts of exploitation that regard background justice as irrelevant. The paper concludes by describing some normative implications that follow from judging an exchange exploitative. (shrink)

Firms use algorithms to make important business decisions. To date, the algorithmic accountability literature has elided a fundamentally empirical question important to business ethics and management: Under what circumstances, if any, are algorithmic decision-making systems considered legitimate? The present study begins to answer this question. Using factorial vignette survey methodology, we explore the impact of decision importance, governance, outcomes, and data inputs on perceptions of the legitimacy of algorithmic decisions made by firms. We find that many of the procedural governance (...) mechanisms in practice today, such as notices and impact statements, do not lead to algorithmic decisions being perceived as more legitimate in general, and, consistent with legitimacy theory, that algorithmic decisions with good outcomes are perceived as more legitimate than bad outcomes. Yet, robust governance, such as offering an appeal process, can create a legitimacy dividend for decisions with bad outcomes. However, when arbitrary or morally dubious factors are used to make decisions, most legitimacy dividends are erased. In other words, companies cannot overcome the legitimacy penalty of using arbitrary or morally dubious factors, such as race or the day of the week, with a good outcome or an appeal process for individuals. These findings add new perspectives to both the literature on legitimacy and policy discussions on algorithmic decision-making in firms. (shrink)