AI, Ethics, and the Law

O’Neil, Cathy

doi:10.1007/978-3-030-21134-9_15

Cited by 5 publications

(5 citation statements)

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“…In dealing with issues of required reliability and trust of different decision support agents, one needs to take further into account that humans are not only working together with such agents but can also be evaluated by them (O’Neil, 2020). However, previous human–automation interaction research has typically only addressed issues of trust in automation from the perspective of humans making decisions in collaboration with some kind of automated support agent (Janssen et al, 2019).…”

Section: Collaboration With or Evaluation By (Artificial) Agentsmentioning

confidence: 99%

“…In recent decades, artificial intelligence (AI) has not only entered our everyday lives but it has also become of critical importance to our working environments. Professionals in a wide range of fields like the finance sector (Bahrammirzaee, 2010; O’Neil, 2020) or healthcare (Bejnordi et al, 2017; McKinney et al, 2020) are now supported by AI: Medical practitioners are using AI to screen x-rays and help them detect cancer (McKinney et al, 2020), recruiters are being supported by AI finding appropriate candidates (Langer et al, 2019), and loan decisions (O’Neil, 2020) are made with the support of AI. As a result, the collaboration and decisions made by these teams of humans and AI have a major impact on many of our lives (for a review, see Langer & Landers, 2021).…”

Section: Introductionmentioning

confidence: 99%

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How Much Reliability Is Enough? A Context-Specific View on Human Interaction With (Artificial) Agents From Different Perspectives

Appelganc

Rieger

Roesler

et al. 2022

Journal of Cognitive Engineering and Decision Making

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Tasks classically performed by human–human teams in today’s workplaces are increasingly given to human–technology teams instead. The role of technology is not only played by classic decision support systems (DSSs) but more and more by artificial intelligence (AI). Reliability is a key factor influencing trust in technology. Therefore, we investigated the reliability participants require in order to perceive the support agents (human, AI, and DSS) as “highly reliable.” We then examined how trust differed between these highly reliable agents. Whilst there is a range of research identifying trust as an important determinant in human–DSS interaction, the question is whether these findings are also applicable to the interaction between humans and AI. To study these issues, we conducted an experiment ( N = 300) with two different tasks, usually performed by dyadic teams (loan assignment and x-ray screening), from two different perspectives (i.e., working together or being evaluated by the agent). In contrast to our hypotheses, the required reliability if working together was equal regardless of the agent. Nevertheless, participants trusted the human more than an AI or DSS. They also required that AI be more reliable than a human when used to evaluate themselves, thus illustrating the importance of changing perspective.

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Section: Collaboration With or Evaluation By (Artificial) Agentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How Much Reliability Is Enough? A Context-Specific View on Human Interaction With (Artificial) Agents From Different Perspectives

Appelganc

Rieger

Roesler

et al. 2022

Journal of Cognitive Engineering and Decision Making

View full text Add to dashboard Cite

show abstract

“…When integrating these two key findings, it seems like the predictions of the perfect automation schema (Dzindolet et al, 2002) are limited to situations prior to failure experience where an automated system is compared to a novice human. Moreover, we argue that having a human expert as a support agent is much more realistic in most settings—particularly in domains where AI is recently being introduced (e.g., banking (Bahrammirzaee, 2010; O’Neil, 2020), medicine (Bejnordi et al, 2017; McKinney et al, 2020), or process industry (Mao et al, 2019)). Thus, the imperfect automation schema seems to be more appropriate and makes the following predictions.…”

Section: Discussionmentioning

confidence: 99%

The (Im)perfect Automation Schema: Who Is Trusted More, Automated or Human Decision Support?

Rieger,

Kugler,

Manzey

et al. 2023

Hum Factors

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Objective This study’s purpose was to better understand the dynamics of trust attitude and behavior in human-agent interaction. Background Whereas past research provided evidence for a perfect automation schema, more recent research has provided contradictory evidence. Method To disentangle these conflicting findings, we conducted an online experiment using a simulated medical X-ray task. We manipulated the framing of support agents (i.e., artificial intelligence (AI) versus expert versus novice) between-subjects and failure experience (i.e., perfect support, imperfect support, back-to-perfect support) within subjects. Trust attitude and behavior as well as perceived reliability served as dependent variables. Results Trust attitude and perceived reliability were higher for the human expert than for the AI than for the human novice. Moreover, the results showed the typical pattern of trust formation, dissolution, and restoration for trust attitude and behavior as well as perceived reliability. Forgiveness after failure experience did not differ between agents. Conclusion The results strongly imply the existence of an imperfect automation schema. This illustrates the need to consider agent expertise for human-agent interaction. Application When replacing human experts with AI as support agents, the challenge of lower trust attitude towards the novel agent might arise.

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“…Several new trends related to changes in worker populations and business focus emerged at the beginning of the 21st-century resulting in a new set of challenges for organizations (O'Neil, 2009). One trend that evolved was the unassigned workspace, where the individual employee has no dedicated personally assigned office, workstation or desk (Becker, 2005).…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the effectiveness of unassigned workspaces: a case study analysis of two workplaces

Patel

Alfaro

2020

ARCH

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PurposeThe purpose of this study is to understand how unassigned workspaces translate into an effective workplace. This is achieved by examining employees and their understanding of how unassigned workspaces impact their engagement, satisfaction and desire to stay with their workplace.Design/methodology/approachTwo workplaces designed on the strategy of 100% unassigned workspaces are analyzed as case studies to understand this phenomenon. Mixed methodology, primarily qualitative method with a survey nested within is used for this study. Grounded theory, one of the strategies of qualitative research methodology is applied as a method for analyzing the data. The study is aimed to reveal the participants' perspectives and interpretations of their actions and physical environment on effectiveness in relation to the unassigned work environment.FindingsThe information afforded the development of an overarching theoretical scheme for integrating spatial categories and describing the employees' experiences of their work environment from the various perspectives. The research reveals that unassigned and flexible spatial attributes contribute positively to employee satisfaction, engagement and retention. However, some of the goals surrounding the culture of trust and autonomy such as privacy, personalization and lack of control are compromised in these environments.Research limitations/implicationsThe insights obtained from this study will provide designers, architects, facility planners and managers a new design tool to aid in making the unassigned workspaces model more effective.Originality/valueThis study bridges the gap while documenting how unassigned workspaces translate into an effective workplace for the employees to be productive, engaged and satisfied.

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AI, Ethics, and the Law

Cited by 5 publications

References 1 publication

How Much Reliability Is Enough? A Context-Specific View on Human Interaction With (Artificial) Agents From Different Perspectives

How Much Reliability Is Enough? A Context-Specific View on Human Interaction With (Artificial) Agents From Different Perspectives

The (Im)perfect Automation Schema: Who Is Trusted More, Automated or Human Decision Support?

Exploring the effectiveness of unassigned workspaces: a case study analysis of two workplaces

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