Algorithms increasingly make managerial decisions that people used to make. Perceptions of algorithms, regardless of the algorithms' actual performance, can significantly influence their adoption, yet we do not fully understand how people perceive decisions made by algorithms as compared with decisions made by humans. To explore perceptions of algorithmic management, we conducted an online experiment using four managerial decisions that required either mechanical or human skills. We manipulated the decision-maker, and measured perceived fairness, trust, and emotional response. With the mechanical (...) tasks, algorithmic and human-made decisions were perceived as equally fair and trustworthy and evoked similar emotions; however, human managers' fairness and trustworthiness were attributed to the manager's authority, whereas algorithms' fairness and trustworthiness were attributed to their perceived efficiency and objectivity. Human decisions evoked some positive emotion due to the possibility of social recognition, whereas algorithmic decisions generated a more mixed response – algorithms were seen as helpful tools but also possible tracking mechanisms. With the human tasks, algorithmic decisions were perceived as less fair and trustworthy and evoked more negative emotion than human decisions. Algorithms' perceived lack of intuition and subjective judgment capabilities contributed to the lower fairness and trustworthiness judgments. Positive emotion from human decisions was attributed to social recognition, while negative emotion from algorithmic decisions was attributed to the dehumanizing experience of being evaluated by machines. This work reveals people's lay concepts of algorithmic versus human decisions in a management context and suggests that task characteristics matter in understanding people's experiences with algorithmic technologies. (shrink)

Government transparency is increasing worldwide and political rhetoric assumes a strong positive relation between transparency and accountability. This chapter opens up the “black box” of the relation between transparency and accountability by examining the expanding body of literature on government transparency. Three theoretical relations between transparency and accountability are identified: transparency facilitates horizontal accountability; transparency strengthens vertical accountability; and transparency reduces the need for accountability. The review of studies into the relation between transparency and accountability show that, under certain conditions (...) and in certain situations, transparency may contribute to accountability: transparency facilitates accountability when it actually presents a significant increase in the available information, when there are actors capable of processing the information, and when exposure has a direct or indirect impact on the government or public agency. (shrink)

Public consumption of artificial intelligence (AI) technologies has been rarely investigated from the perspective of data surveillance and security. We show that the technology acceptance model, when properly modified with security and surveillance fears about AI, builds an insight on how individuals begin to use, accept, or evaluate AI and its automated decisions. We conducted two studies, and found positive roles of perceived ease of use (PEOU) and perceived usefulness (PU). AI security concern, however, negatively affected PEOU and PU, resulting (...) in less acceptance of AI—(1) use, (2) preference, and (3) participation. AI surveillance concern also had negative effects on the credibility of AI and its recommendations. We integrated extant literature on socio-demographic differences, providing an insight on how AI acceptance is based on one’s rationality regarding (1) technological risks (security/surveillance) and (2) benefits (PEOU/PU) as well as other contextual factors of socio-demographics. (shrink)