Demystifying the Draft EU Artificial Intelligence Act — Analysing the good, the bad, and the unclear elements of the proposed approach

Veale, Michael; Borgesius, Frederik J. Zuiderveen

doi:10.9785/cri-2021-220402

Cited by 255 publications

(107 citation statements)

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“…Explainable decision-making algorithms, for instance, may be required to declare their lack of agency, intentionality, and rationality alongside their explanations so that people are not influenced to hold them accountable. Such an approach would be similar to the existing proposals that mandate designers to disclose bots [100]. In conclusion, regulation can ensure that explainability and accountability coexist in algorithmic decision-making.…”

Section: The Necessity For Hard Regulationmentioning

confidence: 92%

The Conflict Between Explainable and Accountable Decision-Making Algorithms

Lima

Grgić-Hlača

Jeong

et al. 2022

2022 ACM Conference on Fairness, Accountability, and Transparency

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Decision-making algorithms are being used in important decisions, such as who should be enrolled in health care programs and be hired. Even though these systems are currently deployed in highstakes scenarios, many of them cannot explain their decisions. This limitation has prompted the Explainable Artificial Intelligence (XAI) initiative, which aims to make algorithms explainable to comply with legal requirements, promote trust, and maintain accountability. This paper questions whether and to what extent explainability can help solve the responsibility issues posed by autonomous AI systems. We suggest that XAI systems that provide post-hoc explanations could be seen as blameworthy agents, obscuring the responsibility of developers in the decision-making process. Furthermore, we argue that XAI could result in incorrect attributions of responsibility to vulnerable stakeholders, such as those who are subjected to algorithmic decisions (i.e., patients), due to a misguided perception that they have control over explainable algorithms. This conflict between explainability and accountability can be exacerbated if designers choose to use algorithms and patients as moral and legal scapegoats. We conclude with a set of recommendations for how to approach this tension in the socio-technical process of algorithmic decision-making and a defense of hard regulation to prevent designers from escaping responsibility.

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Section: The Necessity For Hard Regulationmentioning

confidence: 92%

The Conflict Between Explainable and Accountable Decision-Making Algorithms

Lima

Grgić-Hlača

Jeong

et al. 2022

2022 ACM Conference on Fairness, Accountability, and Transparency

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“…The Food and Drug Administration (FDA) guidelines for AI systems integrated into software as a medical device (SaMD) has a strong emphasis on functional performance, clearly not taking product performance as a given [64]. The draft AI Act in the EU includes requirements for pre-marketing controls to establish products' safety and performance, as well as quality management for high risk systems [190]. These mentions suggest that functionality is not always ignored outright.…”

Section: The Functionality Assumptionmentioning

confidence: 99%

“…This includes what Stark and Hutson call "physiognomic artificial intelligence," which attempts to infer or create hierarchies about personal characteristics from data about their physical appearance [179]. Criticizing the EU Act's failure to address this inconvenient truth, Veale and Borgesius [190] pointed out that "those claiming to detect emotion use oversimplified, questionable taxonomies; incorrectly assume universality across cultures and contexts; and risk '[taking] us back to the phrenological past' of analysing character traits from facial structures. "…”

Section: Failure Taxonomymentioning

confidence: 99%

“…A trickier intervention is the avenue of product certification and standards development for AI products. This concept has already made its way into AI policy discourse; CEN (European Committee for Standardisation) and CENELEC (European Committee for Electrotechnical Standardisation), two of three European Standardisation Organisations (ESOs) were heavily involved in the creation of the EU's draft AI Act [190]. On the U.S. front, industry groups IEEE and ISO regularly shape conversations, with IEEE going so far as to attempt the development of a certification program [72,84].…”

Section: Product Certification and Standardsmentioning

confidence: 99%

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The Fallacy of AI Functionality

Raji

Kumar

Horowitz

et al. 2022

2022 ACM Conference on Fairness, Accountability, and Transparency

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Deployed AI systems often do not work. They can be constructed haphazardly, deployed indiscriminately, and promoted deceptively. However, despite this reality, scholars, the press, and policymakers pay too little attention to functionality. This leads to technical and policy solutions focused on "ethical" or value-aligned deployments, often skipping over the prior question of whether a given system functions, or provides any benefits at all. To describe the harms of various types of functionality failures, we analyze a set of case studies to create a taxonomy of known AI functionality issues. We then point to policy and organizational responses that are often overlooked and become more readily available once functionality is drawn into focus. We argue that functionality is a meaningful AI policy challenge, operating as a necessary first step towards protecting affected communities from algorithmic harm.

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“…The European Union (EU) Artificial Intelligence (AI) Act proposes a banning of AI systems that "manipulate persons through subliminal techniques or exploit the fragility of vulnerable individuals, and could potentially harm the manipulated individual or third person" (EU Commission 2021). The EU AI ACT proposes two practices for regulating manipulation (Veale and Borgesius 2021); namely, prohibiting:…”

Section: Introductionmentioning

confidence: 99%

Missing Mechanisms of Manipulation in the EU AI Act

Franklin

Ashton

Gorman³

et al. 2022

FLAIRS

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The European Union Artificial Intelligence (AI) Act proposes to ban AI systems that ”manipulate persons through subliminal techniques or exploit the fragility of vulnerable individuals, and could potentially harm the manipulated individual or third person”. This article takes the perspective of cognitive psychology to analyze and understand what algorithmic manipulation consists of, who vulnerable individuals may be, and what is considered as harm. Subliminal techniques are expanded with concepts from behavioral science and the study of preference change. Individual psychometric differences which can be exploited are used to expand the concept of vulnerable individuals. The concept of harm is explored beyond physical and psychological harm to consider harm to one's time and right to an un-manipulated opinion. The paper offers policy recommendations that extend from the paper's analyses.

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Demystifying the Draft EU Artificial Intelligence Act — Analysing the good, the bad, and the unclear elements of the proposed approach

Cited by 255 publications

References 0 publications

The Conflict Between Explainable and Accountable Decision-Making Algorithms

The Conflict Between Explainable and Accountable Decision-Making Algorithms

The Fallacy of AI Functionality

Missing Mechanisms of Manipulation in the EU AI Act

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