Sharing responsibility with a machine

Kirchkamp, Oliver; Strobel, Christina

doi:10.1016/j.socec.2019.02.010

Cited by 29 publications

(22 citation statements)

References 86 publications

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“…On the other hand, in a modified dictator game, no significant differences in taking responsibility were detected between human-human and humancomputer teams -though participants in human-computer teams did behave slightly more selfishly. (79). Note that by refraining from shifting responsibility towards computers, these participants forewent an apparently effective way to avoid responsibility: as an earlier study showed, observers of accidents tend to attribute less responsibility to a company if technology was involved in the accident (80).…”

Section: The Collaboration Of Humans and Automated Agents In Teamsmentioning

confidence: 96%

We and It: An Interdisciplinary Review of the Experimental Evidence on Human-Machine Interaction

Chugunova¹,

Sele²

2020

SSRN Journal

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Today, humans interact with technology frequently and in a variety of settings. Their behavior in these interactions has attracted considerable research interest across several fields, with sometimes little exchange among them and seemingly inconsistent findings. Here, we review over 110 experimental studies on human-machine interaction. We synthesize the evidence from different disciplines, suggest ways to reconcile inconsistencies, and elaborate on political and societal implications. The reviewed studies show that people react to automated agents differently than to humans: They behave more rationally, and are less prone to emotional and social responses. We show that there are several factors which systematically impact the willingness to accept automated decisions: task context, performance expectations and the distribution of decision authority. That is, humans seem willing to (over-)rely on algorithmic support, yet averse to fully ceding their decision authority. These behavioral regularities need to be considered when deliberating the benefits and risks of automation.

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Section: The Collaboration Of Humans and Automated Agents In Teamsmentioning

confidence: 96%

We and It: An Interdisciplinary Review of the Experimental Evidence on Human-Machine Interaction

Chugunova¹,

Sele²

2020

SSRN Journal

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“…Considering the Uncanny Valley [23] and the balance of social cues and competence, researchers examined the least actual capabilities of an agent, which are to understand its teammates and to react appropriately with adequate length. Eventually, the outcome depends on the contributions of each member including the agent [30][31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Implementing an Intelligent Collaborative Agent as Teammate in Collaborative Writing: toward a Synergy of Humans and AI

Wiethof¹,

Tavanapour²,

Bittner³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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This paper aims at implementing a hybrid form of group work through the incorporation of an intelligent collaborative agent into a Collaborative Writing process. With that it contributes to the overall research gap establishing acceptance of AI towards complementary hybrid work. To approach this aim, we follow a Design Science Research process. We identify requirements for the agent to be considered a teammate based on expert interviews in the light of Social Response Theory and the concept of the Uncanny Valley. Next, we derive design principles for the implementation of an agent as teammate from the collected requirements. For the evaluation of the design principles and the human teammates' perception of the agent, we instantiate a Collaborative Writing process via a web-application incorporating the agent. The evaluation reveals the partly successful implementation of the developed design principles. Additionally, the results show the potential of hybrid collaboration teams accepting non-human teammates.

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“…Other researchers have analyzed the differences in the definitions of ethical aspects for human-only, AI-only, or combined decision making and found that moral fault was always attributed to humans (Shank et al 2019). Kirchkamp and Strobel (2019) discovered that the feeling of guilt also does not change, while responsibility in human-AI teams is perceived as being higher than in human-only teams, and selfish-acting decreases. According to their findings, any higher form of moral responsibility is so far not attributed to machines.…”

Section: Ethical Perspectives On Using Ai In Strategic Organizationalmentioning

confidence: 99%

On the current state of combining human and artificial intelligence for strategic organizational decision making

Trunk

Birkel

Hartmann

2020

Bus Res

120

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Strategic organizational decision making in today’s complex world is a dynamic process characterized by uncertainty. Therefore, diverse groups of responsible employees deal with the large amount and variety of information, which must be acquired and interpreted correctly to deduce adequate alternatives. The technological potential of artificial intelligence (AI) is expected to offer further support, although research in this regard is still developing. However, as the technology is designed to have capabilities beyond those of traditional machines, the effects on the division of tasks and the definition of roles established in the current human–machine relationship are discussed with increasing awareness. Based on a systematic literature review, combined with content analysis, this article provides an overview of the possibilities that current research identifies for integrating AI into organizational decision making under uncertainty. The findings are summarized in a conceptual model that first explains how humans can use AI for decision making under uncertainty and then identifies the challenges, pre-conditions, and consequences that must be considered. While research on organizational structures, the choice of AI application, and the possibilities of knowledge management is extensive, a clear recommendation for ethical frameworks, despite being defined as a crucial foundation, is missing. In addition, AI, other than traditional machines, can amplify problems inherent in the decision-making process rather than help to reduce them. As a result, the human responsibility increases, while the capabilities needed to use the technology differ from other machines, thus making education necessary. These findings make the study valuable for both researchers and practitioners.

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Sharing responsibility with a machine

Cited by 29 publications

References 86 publications

We and It: An Interdisciplinary Review of the Experimental Evidence on Human-Machine Interaction

We and It: An Interdisciplinary Review of the Experimental Evidence on Human-Machine Interaction

Implementing an Intelligent Collaborative Agent as Teammate in Collaborative Writing: toward a Synergy of Humans and AI

On the current state of combining human and artificial intelligence for strategic organizational decision making

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