arXiv subjects

Sarah Sterz

Publications and source records attributed to Sarah Sterz.

6 recordsLinked to original sources

arXiv2026

Don't blame me: How Intelligent Support Affects Moral Responsibility in Human Oversight

AI-based systems can increasingly perform work tasks autonomously. In safety-critical tasks, human oversight of these systems is required to mitigate risks and to ensure responsibility in case something goes wrong. Since people often struggle to stay focused and perform good oversight, intelligent support systems are used to assist them, giving decision recommendations, alerting users, or restricting them from dangerous actions. However, in cases where recommendations are wrong, decision support might undermine the very reason why human oversight was employed -- genuine moral responsibility. The goal of our study was to investigate how a decision support system that restricted available interventions would affect overseer's perceived moral responsibility, in particular in cases where the support errs. In a simulated oversight experiment, participants (\textit{N}=274) monitored an autonomous drone that faced ten critical situations, choosing from six possible actions to resolve each situation. An AI system constrained participants' choices to either six, four, two, or only one option (between-subject study). Results showed that participants, who were restricted to choosing from a single action, felt less morally responsible if a crash occurred. At the same time, participants' judgments about the responsibility of other stakeholders (the AI; the developer of the AI) did not change between conditions. Our findings provide important insights for user interface design and oversight architectures: they should prevent users from attributing moral agency to AI, help them understand how moral responsibility is distributed, and, when oversight aims to prevent ethically undesirable outcomes, be designed to support the epistemic and causal conditions required for moral responsibility.

cs.HC↗

arXiv2024

Give Me a Choice: The Consequences of Restricting Choices Through AI-Support for Perceived Autonomy, Motivational Variables, and Decision Performance

Design optimizations in human-AI collaboration often focus on cognitive aspects like attention and task load. Drawing on work design literature, we propose that effective human-AI collaboration requires broader consideration of human needs (e.g., autonomy) that affect motivational variables (e.g., meaningfulness). In a simulated drone oversight experiment, participants (N=274, between-subject) faced 10 critical decision-making scenarios with varying levels of choice restrictions with an AI recommending only 1, 2, 4 or all 6 possible actions. Restricting participants to one selectable action improved task performance (with a perfect AI) but significantly reduced perceived autonomy and work meaningfulness, and these effects intensified over time. In conditions with multiple action choices, participants with higher perceived autonomy performed better. The findings underscore the importance of considering motivational factors to design successful long-term human-AI collaboration at work.

cs.HC↗

arXiv2024

AI Act for the Working Programmer

The European AI Act is a new, legally binding instrument that will enforce certain requirements on the development and use of AI technology potentially affecting people in Europe. It can be expected that the stipulations of the Act, in turn, are going to affect the work of many software engineers, software testers, data engineers, and other professionals across the IT sector in Europe and beyond. The 113 articles, 180 recitals, and 13 annexes that make up the Act cover 144 pages. This paper aims at providing an aid for navigating the Act from the perspective of some professional in the software domain, termed "the working programmer", who feels the need to know about the stipulations of the Act.

cs.CY↗

arXiv2024

On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives

Human oversight is currently discussed as a potential safeguard to counter some of the negative aspects of high-risk AI applications. This prompts a critical examination of the role and conditions necessary for what is prominently termed effective or meaningful human oversight of these systems. This paper investigates effective human oversight by synthesizing insights from psychological, legal, philosophical, and technical domains. Based on the claim that the main objective of human oversight is risk mitigation, we propose a viable understanding of effectiveness in human oversight: for human oversight to be effective, the oversight person has to have (a) sufficient causal power with regard to the system and its effects, (b) suitable epistemic access to relevant aspects of the situation, (c) self-control, and (d) fitting intentions for their role. Furthermore, we argue that this is equivalent to saying that an oversight person is effective if and only if they are morally responsible and have fitting intentions. Against this backdrop, we suggest facilitators and inhibitors of effectiveness in human oversight when striving for practical applicability. We discuss factors in three domains, namely, the technical design of the system, individual factors of oversight persons, and the environmental circumstances in which they operate. Finally, this paper scrutinizes the upcoming AI Act of the European Union -- in particular Article 14 on Human Oversight -- as an exemplary regulatory framework in which we study the practicality of our understanding of effective human oversight. By analyzing the provisions and implications of the European AI Act proposal, we pinpoint how far that proposal aligns with our analyses regarding effective human oversight as well as how it might get enriched by our conceptual understanding of effectiveness in human oversight.

cs.CY↗

arXiv2023

Software Doping Analysis for Human Oversight

This article introduces a framework that is meant to assist in mitigating societal risks that software can pose. Concretely, this encompasses facets of software doping as well as unfairness and discrimination in high-risk decision-making systems. The term software doping refers to software that contains surreptitiously added functionality that is against the interest of the user. A prominent example of software doping are the tampered emission cleaning systems that were found in millions of cars around the world when the diesel emissions scandal surfaced. The first part of this article combines the formal foundations of software doping analysis with established probabilistic falsification techniques to arrive at a black-box analysis technique for identifying undesired effects of software. We apply this technique to emission cleaning systems in diesel cars but also to high-risk systems that evaluate humans in a possibly unfair or discriminating way. We demonstrate how our approach can assist humans-in-the-loop to make better informed and more responsible decisions. This is to promote effective human oversight, which will be a central requirement enforced by the European Union's upcoming AI Act. We complement our technical contribution with a juridically, philosophically, and psychologically informed perspective on the potential problems caused by such systems.

cs.CY↗

arXiv2021

On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness

Recently, requirements for the explainability of software systems have gained prominence. One of the primary motivators for such requirements is that explainability is expected to facilitate stakeholders' trust in a system. Although this seems intuitively appealing, recent psychological studies indicate that explanations do not necessarily facilitate trust. Thus, explainability requirements might not be suitable for promoting trust. One way to accommodate this finding is, we suggest, to focus on trustworthiness instead of trust. While these two may come apart, we ideally want both: a trustworthy system and the stakeholder's trust. In this paper, we argue that even though trustworthiness does not automatically lead to trust, there are several reasons to engineer primarily for trustworthiness -- and that a system's explainability can crucially contribute to its trustworthiness.

cs.SE↗