A high-level overview of AI ethics

Kazim, Emre; Koshiyama, Adriano

doi:10.1016/j.patter.2021.100314

Cited by 103 publications

(32 citation statements)

References 50 publications

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“…The tier 2 information requirements ensure that more technical audiences are satisfied as to the purpose, design, use and risk of an algorithmic tool. Pertinently, the tier 2 information provides such technical audiences with a level of detail that encourages the model owner to ensure that best practices are followed, using a 'processes' approach [31].…”

Section: Public Transparencymentioning

confidence: 99%

Public sector AI transparency standard: UK Government seeks to lead by example

Kingsman¹,

Kazim²,

Chaudhry³

et al. 2022

Discov Artif Intell

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In releasing the Algorithmic Transparency Standard, the UK government has reiterated its commitment to greater algorithmic transparency in the public sector. The Standard signals that the UK government is both pushing forward with the AI standards agenda and ensuring that those standards benefit from empirical practitioner-led experience, enabling coherent, widespread adoption. The two-tier approach of the Algorithmic Transparency Standard encourages transparency inclusivity across distinct audiences, facilitating trust across algorithm stakeholders. Moreover, it can be understood that implementation of the Standard within the UK’s public sector will inform standards more widely, influencing best practice in the private sector. This article provides a summary and commentary of the text.

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Section: Public Transparencymentioning

confidence: 99%

Public sector AI transparency standard: UK Government seeks to lead by example

Kingsman¹,

Kazim²,

Chaudhry³

et al. 2022

Discov Artif Intell

Self Cite

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“…Indeed, recent years have seen progress toward a consensus on the need for assurance in the creation and adoption of AI systems. To this end, we have started to see the creation of industry standards, consideration of AI ethics (Kazim and Koshiyama, 2021 ), and the creation of AI practitioners.…”

Section: Commentarymentioning

confidence: 99%

“…Artificial intelligence (AI) ethics is a nascent field that has been gaining traction in recent years and aims to address and mitigate the risk of the use of AI through an interdisciplinary approach, drawing on concepts from disciplines including philosophy, computer science, and law (Kazim and Koshiyama, 2021 ). One of the key themes emerging in this field is the need for algorithm assurance through greater governance, regulation, and standardization of best practices (Kazim and Koshiyama, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Overview and commentary of the CDEI's extended roadmap to an effective AI assurance ecosystem

Barrance¹,

Kazim²,

Hilliard³

et al. 2022

Front. Artif. Intell.

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In recent years, the field of ethical artificial intelligence (AI), or AI ethics, has gained traction and aims to develop guidelines and best practices for the responsible and ethical use of AI across sectors. As part of this, nations have proposed AI strategies, with the UK releasing both national AI and data strategies, as well as a transparency standard. Extending these efforts, the Centre for Data Ethics and Innovation (CDEI) has published an AI Assurance Roadmap, which is the first of its kind and provides guidance on how to manage the risks that come from the use of AI. In this article, we provide an overview of the document's vision for a “mature AI assurance ecosystem” and how the CDEI will work with other organizations for the development of regulation, industry standards, and the creation of AI assurance practitioners. We also provide a commentary of some key themes identified in the CDEI's roadmap in relation to (i) the complexities of building “justified trust”, (ii) the role of research in AI assurance, (iii) the current developments in the AI assurance industry, and (iv) convergence with international regulation.

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“…There is a large and fast growing body of literature on the ethics of AI [37,42,68,94] that this paper cannot replicate. For the purpose of understanding the role of AI impact assessments, it is sufficient to highlight some of the key ethical and social issues that AI raises.…”

Section: Ethical Concernsmentioning

confidence: 99%

Assessing the ethical and social concerns of artificial intelligence in neuroinformatics research: an empirical test of the European Union Assessment List for Trustworthy AI (ALTAI)

Stahl

Leach

2022

AI Ethics

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Ethical and social concerns are a key obstacle to the adoption of artificial intelligence (AI) in the life sciences and beyond. The discussion of these issues has intensified in recent years and led to a number of approaches, tools and initiatives. Key amongst them is the idea of ex-ante impact assessments that aim to identify issues at the early stages of development. One prominent example of such ex-ante impact assessment is the European Union's (EU) Assessment list for Trustworthy AI (ALTAI). This article uses the findings of a large-scale application of the ALTAI to a large neuro-informatics project as an exemplar to demonstrate the effectiveness and limitations of the ALTAI in practice. The article shows that ex-ante impact assessment has the potential to help identify and address ethical and social issues. However, they need to be understood as part of a broader socio-technical ecosystem of AI. For ALTAI and related approaches to be useful in bio-medical research, they should be interpreted from a systems theory perspective which allows for their integration into the rich set of tools, legislation and approaches. The paper argues that ex-ante impact assessments have the best chance of being successful if seen applied in conjunction with other approaches in the context of the overall AI ecosystem.

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A high-level overview of AI ethics

Cited by 103 publications

References 50 publications

Public sector AI transparency standard: UK Government seeks to lead by example

Public sector AI transparency standard: UK Government seeks to lead by example

Overview and commentary of the CDEI's extended roadmap to an effective AI assurance ecosystem

Assessing the ethical and social concerns of artificial intelligence in neuroinformatics research: an empirical test of the European Union Assessment List for Trustworthy AI (ALTAI)

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