arXiv subjects

Anne Lauber-Rönsberg

Publications and source records attributed to Anne Lauber-Rönsberg.

4 recordsLinked to original sources

arXiv2024

Assessing Privacy Policies with AI: Ethical, Legal, and Technical Challenges

The growing use of Machine Learning and Artificial Intelligence (AI), particularly Large Language Models (LLMs) like OpenAI's GPT series, leads to disruptive changes across organizations. At the same time, there is a growing concern about how organizations handle personal data. Thus, privacy policies are essential for transparency in data processing practices, enabling users to assess privacy risks. However, these policies are often long and complex. This might lead to user confusion and consent fatigue, where users accept data practices against their interests, and abusive or unfair practices might go unnoticed. LLMss can be used to assess privacy policies for users automatically. In this interdisciplinary work, we explore the challenges of this approach in three pillars, namely technical feasibility, ethical implications, and legal compatibility of using LLMs to assess privacy policies. Our findings aim to identify potential for future research, and to foster a discussion on the use of LLM technologies for enabling users to fulfil their important role as decision-makers in a constantly developing AI-driven digital economy.

cs.CY↗

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↗