Operationalising AI governance through ethics-based auditing: an industry case study

Mökander, Jakob; Floridi, Luciano

doi:10.1007/s43681-022-00171-7

Cited by 38 publications

(18 citation statements)

References 144 publications

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“…An example of this possibility is found in the proposed European regulation on AI. As a first step, the EU AIA uses a Switch to demarcate its material scope Mökander et al (2022b). As mentioned in Sect.…”

Section: Discussionmentioning

confidence: 99%

“…A good example of can be found in the proposed US AAA. The bill requires technology providers to (i) establish a baseline for normative evaluation by describing the decisionmaking process they seek to automate and (ii) to justify why and how the deployment of a specific AI system would improve the process in question (Mökander & Floridi, 2022b).…”

Section: Discussionmentioning

confidence: 99%

“…This observation is confirmed by our own empirical research. In a recent case study (which was based on interviews with managers and software developers in the biopharmaceutical industry), we found that one of the main challenges organisations face when attempting to operationalise AI governance is the lack of procedures for demarcating the material scope of such initiatives (Mökander & Floridi, 2022b). As a result, organisations struggle even to produce an inventory of the AI systems they develop or use.…”

Section: Introductionmentioning

confidence: 99%

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The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems

et al. 2023

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Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical challenges. Nevertheless, pragmatic problem-solving demands that things should be sorted so that their grouping will promote successful actions for some specific end. In this article, we review and compare previous attempts to classify AI systems for the purpose of implementing AI governance in practice. We find that attempts to classify AI systems proposed in previous literature use one of three mental models: the Switch, i.e., a binary approach according to which systems either are or are not considered AI systems depending on their characteristics; the Ladder, i.e., a risk-based approach that classifies systems according to the ethical risks they pose; and the Matrix, i.e., a multi-dimensional classification of systems that take various aspects into account, such as context, input data, and decision-model. Each of these models for classifying AI systems comes with its own set of strengths and weaknesses. By conceptualising different ways of classifying AI systems into simple mental models, we hope to provide organisations that design, deploy, or regulate AI systems with the vocabulary needed to demarcate the material scope of their AI governance frameworks.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems

et al. 2023

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“…Recognizing the current gap between AI principles and AI practice, researchers and companies have aimed to make ethical values feasible and actionable in real-world settings. There is a drive to bridge this ethics/practice gap [73], to operationalize AI ethics principles [12,47] and to translate principles into practices [49]. High-minded normative statements must be integrated in meaningful ways into datasets, production pipelines, and product features.…”

Section: The Principles/practice Gapmentioning

confidence: 99%

The uselessness of AI ethics

Munn

2022

AI Ethics

148

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As the awareness of AI’s power and danger has risen, the dominant response has been a turn to ethical principles. A flood of AI guidelines and codes of ethics have been released in both the public and private sector in the last several years. However, these are meaningless principles which are contested or incoherent, making them difficult to apply; they are isolated principles situated in an industry and education system which largely ignores ethics; and they are toothless principles which lack consequences and adhere to corporate agendas. For these reasons, I argue that AI ethical principles are useless, failing to mitigate the racial, social, and environmental damages of AI technologies in any meaningful sense. The result is a gap between high-minded principles and technological practice. Even when this gap is acknowledged and principles seek to be “operationalized,” the translation from complex social concepts to technical rulesets is non-trivial. In a zero-sum world, the dominant turn to AI principles is not just fruitless but a dangerous distraction, diverting immense financial and human resources away from potentially more effective activity. I conclude by highlighting alternative approaches to AI justice that go beyond ethical principles: thinking more broadly about systems of oppression and more narrowly about accuracy and auditing.

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“…ca/ en/ gover nment/ system/ digit al-gover nment/ digit al-gover nment-innov ations/ respo nsible-use-ai/ algor ithmic-impact-asses sment. html 4 An exception is a case study byMökander and Floridi (2022). They provide a detailed case study based on an observation of an "ethics-based audit" of AztraZeneca conducted by a third party (not by the authors).…”

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confidence: 99%

Algorithmic Bias and Risk Assessments: Lessons from Practice

Hasan

Brown

Davidović

et al. 2022

DISO

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In this paper, we distinguish between different sorts of assessments of algorithmic systems, describe our process of assessing such systems for ethical risk, and share some key challenges and lessons for future algorithm assessments and audits. Given the distinctive nature and function of a third-party audit, and the uncertain and shifting regulatory landscape, we suggest that second-party assessments are currently the primary mechanisms for analyzing the social impacts of systems that incorporate artificial intelligence. We then discuss two kinds of assessments: an ethical risk assessment and a narrower, technical algorithmic bias assessment. We explain how the two assessments depend on each other, highlight the importance of situating the algorithm within its particular socio-technical context, and discuss a number of lessons and challenges for algorithm assessments and, potentially, for algorithm audits. The discussion builds on our team's experience of advising and conducting ethical risk assessments for clients across different industries in the last 4 years. Our main goal is to reflect on the key factors that are potentially ethically relevant in the use of algorithms and draw lessons for the nascent algorithm assessment and audit industry, in the hope of helping all parties minimize the risk of harm from their use.

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Operationalising AI governance through ethics-based auditing: an industry case study

Cited by 38 publications

References 144 publications

The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems

The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems

The uselessness of AI ethics

Algorithmic Bias and Risk Assessments: Lessons from Practice

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