Human–Algorithm Collaboration Works Best if Humans Lead (Because it is Fair!)

Abstract

Autonomous algorithms are increasingly being used by organizations to reach ever increasing heights of organizational efficiency. The emerging business model of today therefore appears to be one where autonomous algorithms are gradually expanding their occupation into becoming a leading decision-maker, and humans by default become increasingly more subordinate to such decisions. We address the question of whether this business perspective is consistent with the sort of collaboration employees want to have with algorithms at work. We explored this question by investigating in what way humans preferred to collaborate with algorithms when making decisions. Using two experimental studies (Study 1, n = 237; Study 2, n = 684), we show that humans consider the collaboration with autonomous algorithms as unfair when the algorithm leads decision-making and will even incur high financial costs in order to avoid this. Our results also show that humans do not want to exclude algorithms entirely but seem to prefer a 60-40% human-algorithm partnership. These findings contrast the position taken by today's emerging business model on the issue of automated organizational decision-making. Our findings also provide support for the existence of an implicit theory-held by both present and future employees-that humans should lead and algorithms follow.

Effective human–AI work design for collaborative decision-making

“…, 2016). These results are supported by some recent work, where humans have emphasized that they would prefer to lead in human–AI collaboration (De Cremer and McGuire, 2022). The results also found that humans experienced clarity in the role when humans and AI worked parallelly with differences in specialization.…”

“…Sowa et al (2021), in a multi-stage study focusing on different types of AI and modes of collaboration with humans, found that humans encouraged human-AI collaboration than complete automation. De Cremer and McGuire (2022) found that humans have opposed the use of AI in a leadership role as they considered them to be less fair. At the same time, they found that humans are open and willing to establish a partnership with AI as teammates but led by humans.…”

Algorithms, leadership, and morality: why a mere human effect drives the preference for human over algorithmic leadership

“…Second, people are also less accepting of the use of algorithms in decision-making that impacts the interests of others as they perceive their use as unfair and less trustworthy [20,67]. For example, research has found that people perceive the employment of algorithms in the domain of performance evaluation and personnel selection to be less fair, when compared with humans making these decisions [29,86]. Furthermore, when conducting a review of the effects of algorithmic decision-making, Langer and Landers [77] concluded that "automated decisions seem to predominantly result in less trust compared to human decisions and this seems to apply to decision-making at work" (p. 9).…”

Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature

“…Lee et al (2019a) showed that allowing respondents to adjust the algorithmic allocation of resources and thereby overrule decisions made by the ADM system increased people's perceptions of algorithmic fairness. Other studies varied the degree of human involvement in the decision-making process: While Wang et al (2020) found no significant differences across the experimental conditions, de Cremer and McGuire (2022) suggested that respondents even incurred financial costs to avoid the algorithm led to the decision because they perceived it to be unfair.…”