Mapping the Stony Road toward Trustworthy AI: Expectations, Problems, Conundrums

Rieder, Gernot; Simon, Judith; Wong, Pak‐Hang

doi:10.2139/ssrn.3717451

Cited by 12 publications

(17 citation statements)

References 13 publications

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“…The concept of trust toward systems will certainly be a component of the future paradigm of AI development, but we are questioning whether this concept should be a central one, or if this is simply one among the other factors that determine the quality of the HAII. In line with other practitioners' perspectives (Rieder et al 2020;Ryan 2020) we appreciated the EC intention to achieve a culture of "Trustworthy AI for Europe" (AIHLEG 2019, p. 35), however, it is unclear whether trust toward AI is the ultimate goal, or if trustworthiness is intended as a convenient way to push the idea of an AI market by diverting responsibility and accountability from developers and users (Ryan 2020). While saying this, we still do recommend that the (national and international) political decision-makers encourage "prosocial and safe-conduct" of operators (Cimpeanu et al 2020, p. 16).…”

Section: The Future Of Interaction With Ai Systemsmentioning

confidence: 55%

“…The main EC agenda is to build a market based on the concept of "enabling trustworthy AI" with a strong focus on data (ECWP 2020 p. 3) pointing toward an unspecified approach of safety-by-design (Hale et al 2007). Rieder et al (2020) recently highlighted that the EU plan emphasises the concept of trustworthy AI as a 'ready-made brand', even though it is yet unclear what can be concretely operationalised in terms of AI safety. Data access, re-use, exchange, and interoperability are considered central to the idea of the EU AI market and to facilitate international exchanges based on trustworthiness (ECWP 2020, p. 8).…”

Section: Is Eu Policy Enough? Safe To Use Ai For a Frictionless Marketmentioning

confidence: 99%

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Embedding artificial intelligence in society: looking beyond the EU AI master plan using the culture cycle

et al. 2022

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The European Union (EU) Commission’s whitepaper on Artificial Intelligence (AI) proposes shaping the emerging AI market so that it better reflects common European values. It is a master plan that builds upon the EU AI High-Level Expert Group guidelines. This article reviews the masterplan, from a culture cycle perspective, to reflect on its potential clashes with current societal, technical, and methodological constraints. We identify two main obstacles in the implementation of this plan: (i) the lack of a coherent EU vision to drive future decision-making processes at state and local levels and (ii) the lack of methods to support a sustainable diffusion of AI in our society. The lack of a coherent vision stems from not considering societal differences across the EU member states. We suggest that these differences may lead to a fractured market and an AI crisis in which different members of the EU will adopt nation-centric strategies to exploit AI, thus preventing the development of a frictionless market as envisaged by the EU. Moreover, the Commission aims at changing the AI development culture proposing a human-centred and safety-first perspective that is not supported by methodological advancements, thus taking the risks of unforeseen social and societal impacts of AI. We discuss potential societal, technical, and methodological gaps that should be filled to avoid the risks of developing AI systems at the expense of society. Our analysis results in the recommendation that the EU regulators and policymakers consider how to complement the EC programme with rules and compensatory mechanisms to avoid market fragmentation due to local and global ambitions. Moreover, regulators should go beyond the human-centred approach establishing a research agenda seeking answers to the technical and methodological open questions regarding the development and assessment of human-AI co-action aiming for a sustainable AI diffusion in the society.

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Section: The Future Of Interaction With Ai Systemsmentioning

confidence: 55%

Section: Is Eu Policy Enough? Safe To Use Ai For a Frictionless Marketmentioning

confidence: 99%

Embedding artificial intelligence in society: looking beyond the EU AI master plan using the culture cycle

et al. 2022

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“…Secondly, responsibility encompasses more dimensions than are covered by current regulations, most importantly social and moral dimensions [7], [57]. Quoting Dignum [58], responsibility is "not just about making rules to govern intelligent machines; it is about the whole sociotechnical system in which the system operates, and which encompasses people, machines and institutions."…”

Section: Regulations and Responsibilitymentioning

confidence: 99%

Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions

Petersen

Potdevin

Mohammadi³

et al. 2022

IEEE Access

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project, see www.ki-sigs.de, which is funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) and involves several universities, research institutions and private companies in northern Germany. Researchers YP, SB and LP are employed through funding from the KI-SIGS project. Neither the funding agency nor the involved private companies have had any influence on the planning and design of this survey, the writing of the manuscript, or the publication process. This research was conducted while EP was at Universität zu Lübeck.

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“…Here, I suggest taking their formulation of the underlying question on "who is responsible for the way it works?" to consider the wider socio-technical system of which the ethical and-in terms of the European Commission's guidelines-trustworthy AI system [27] is a part. This entails that we should not only be interested in the developing party as the responsible entity, but also in the commissioning, deploying and, ultimately, the utilizing parties.…”

Section: Explicability As An Ethical Principlementioning

confidence: 99%

On the risk of confusing interpretability with explicability

Herzog

2021

AI Ethics

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AbstractThis Comment explores the implications of a lack of tools that facilitate an explicable utilization of epistemologically richer, but also more involved white-box approaches in AI. In contrast, advances in explainable artificial intelligence for black-box approaches have led to the availability of semi-standardized and attractive toolchains that offer a seemingly competitive edge over inherently interpretable white-box models in terms of intelligibility towards users. Consequently, there is a need for research on efficient tools for rendering interpretable white-box approaches in AI explicable to facilitate responsible use.

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Mapping the Stony Road toward Trustworthy AI: Expectations, Problems, Conundrums

Cited by 12 publications

References 13 publications

Embedding artificial intelligence in society: looking beyond the EU AI master plan using the culture cycle

Embedding artificial intelligence in society: looking beyond the EU AI master plan using the culture cycle

Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions

On the risk of confusing interpretability with explicability

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