“Computer says no”: Algorithmic decision support and organisational responsibility

Adensamer, Angelika; Gsenger, Rita; Klausner, Lukas Daniel

doi:10.1016/j.jrt.2021.100014

Cited by 8 publications

(5 citation statements)

References 47 publications

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“…However, the distributed responsibility in a AI algorithm system may dilute and blur the decision-making process, and the organizational responsibility map needs to be clarified (Adensamer et al, 2021). This recalls Latour's (1992) analogy that missing responsibility occurring in a system of humans and technologies is like physics when trying to look for the "missing mass" in the universe.…”

Section: Ai Decision-making Process and Actorsmentioning

confidence: 98%

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The loopholes of algorithmic public services: an “intelligent” accountability research agenda

Bracci

2022

AAAJ

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Purpose Governments are increasingly turning to artificial intelligence (AI) algorithmic systems to increase efficiency and effectiveness of public service delivery. While the diffusion of AI offers several desirable benefits, caution and attention should be posed to the accountability of AI algorithm decision-making systems in the public sector. The purpose of this paper is to establish the main challenges that an AI algorithm might bring about to public service accountability. In doing so, the paper also delineates future avenues of investigation for scholars.Design/methodology/approach This paper builds on previous literature and anecdotal cases of AI applications in public services, drawing on streams of literature from accounting, public administration and information technology ethics.Findings Based on previous literature, the paper highlights the accountability gaps that AI can bring about and the possible countermeasures. The introduction of AI algorithms in public services modifies the chain of responsibility. This distributed responsibility requires an accountability governance, together with technical solutions, to meet multiple accountabilities and close the accountability gaps. The paper also delineates a research agenda for accounting scholars to make accountability more “intelligent”.Originality/value The findings of the paper shed new light and perspective on how public service accountability in AI should be considered and addressed. The results developed in this paper will stimulate scholars to explore, also from an interdisciplinary perspective, the issues public service organizations are facing to make AI algorithms accountable.

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Section: Ai Decision-making Process and Actorsmentioning

confidence: 98%

Section: Public Service Accountability and Ai Adoptionmentioning

confidence: 99%

The loopholes of algorithmic public services: an “intelligent” accountability research agenda

Bracci

2022

AAAJ

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“…The use of such technology is prone to cause conflicts of interests between different levels of organisational hierarchies and affect the work lives of employees lacking agency to have a say in decisions surrounding the use of automation. (We will confine ourselves to commenting on some specific aspects regarding automation in policing and justice here; for a more in-depth investigation of the issue of organisational responsibility in the face of automation, see Adensamer, Gsenger and Klausner (2021). For a discussion of questions surrounding algorithmic control and its contestation between employers and workers, see Kellogg, Valentine and Christin (2020), and for an analysis of different ways in which employers are using algorithms to shift risks from themselves to workers, see Moradi and Levy (2020).…”

Section: Base Rate Fallacy and Difficulties Of Scalementioning

confidence: 99%

“Part Man, Part Machine, All Cop”: Automation in Policing

Adensamer

Klausner

2021

Front. Artif. Intell.

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Digitisation, automation, and datafication permeate policing and justice more and more each year—from predictive policing methods through recidivism prediction to automated biometric identification at the border. The sociotechnical issues surrounding the use of such systems raise questions and reveal problems, both old and new. Our article reviews contemporary issues surrounding automation in policing and the legal system, finds common issues and themes in various different examples, introduces the distinction between human “retail bias” and algorithmic “wholesale bias”, and argues for shifting the viewpoint on the debate to focus on both workers' rights and organisational responsibility as well as fundamental rights and the right to an effective remedy.

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“…Responsibility, however, is more strongly related to the duty of completing a certain task and can be taken on by individuals themselves. In the context of personnel preselection, an HR professional is responsible for the task of selecting qualified personnel and he or she can be held accountable for the decision ( Adensamer et al, 2021 ). We use the term responsibility in our study, as being held accountable for something also presumes being responsible for it in the first place.…”

Section: Introductionmentioning

confidence: 99%

Check the box! How to deal with automation bias in AI-based personnel selection

et al. 2023

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Artificial Intelligence (AI) as decision support for personnel preselection, e.g., in the form of a dashboard, promises a more effective and fairer selection process. However, AI-based decision support systems might prompt decision makers to thoughtlessly accept the system’s recommendation. As this so-called automation bias contradicts ethical and legal requirements of human oversight for the use of AI-based recommendations in personnel preselection, the present study investigates strategies to reduce automation bias and increase decision quality. Based on the Elaboration Likelihood Model, we assume that instructing decision makers about the possibility of system errors and their responsibility for the decision, as well as providing an appropriate level of data aggregation should encourage decision makers to process information systematically instead of heuristically. We conducted a 3 (general information, information about system errors, information about responsibility) x 2 (low vs. high aggregated data) experiment to investigate which strategy can reduce automation bias and enhance decision quality. We found that less automation bias in terms of higher scores on verification intensity indicators correlated with higher objective decision quality, i.e., more suitable applicants selected. Decision makers who received information about system errors scored higher on verification intensity indicators and rated subjective decision quality higher, but decision makers who were informed about their responsibility, unexpectedly, did not. Regarding aggregation level of data, decision makers of the highly aggregated data group spent less time on the level of the dashboard where highly aggregated data were presented. Our results show that it is important to inform decision makers who interact with AI-based decision-support systems about potential system errors and provide them with less aggregated data to reduce automation bias and enhance decision quality.

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“Computer says no”: Algorithmic decision support and organisational responsibility

Cited by 8 publications

References 47 publications

The loopholes of algorithmic public services: an “intelligent” accountability research agenda

The loopholes of algorithmic public services: an “intelligent” accountability research agenda

“Part Man, Part Machine, All Cop”: Automation in Policing

Check the box! How to deal with automation bias in AI-based personnel selection

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