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)

Scholars in science and technology studies, as well as economics and innovation studies, utilize the trajectory metaphor in describing a technology’s maturation. Impetus and purpose may differ, but the trajectory serves as a shared tool for assessing social change either in society at large or within a market sector, a firm, or a discipline. In reverse, the lens of a technology trajectory can be a basis for assessing technology, estimating economic growth, and selecting among plausible product development pathways. Emerging technologies (...) pose a challenge in that the promissory claims that attracted investment may also challenge social safeguards, leading to necessary, but unforeseen hurdles to later technology acceptance. The experiences with biotechnology and nanotechnology demonstrate that knowledge gained in the intervening years was to have tempered exaggerations and addressed the criteria and the responsibilities of those who would later be deciding on product acceptability. In particular, the promissory claim for nanotechnology, unique phenomena, challenged regulatory practice, while claims for biotechnology products were often the fruition of themes found in the educational experience common to both the biotechnology researcher and the regulator. The social science and humanities literature mirrored these points as did the responses of civil society organizations. A prospective trajectory for artificial intelligence is developed using the example of driverless cars and then qualitatively compared to the retrospective trajectories for nanotechnology and biotechnology. Experience with automation, algorithm transparency, and computational modeling of biological mechanisms are identified as pertinent to responsible AI development. (shrink)