From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices

Morley, Jessica; Floridi, Luciano; Kinsey, Libby; Elhalal, Anat

doi:10.2139/ssrn.3830348

Cited by 91 publications

(121 citation statements)

References 70 publications

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“…There is also a growing interest in bridging the gap between AI practice and governance Bryson [2020]. This is reflected in the publication of a significant number of ethical guidance documents emerging from both commercial and academic sectors Morley et al [2019], Hagendorff [2020]. The global political landscape also attends to issues concerning ethical AI e.g.…”

Section: Momentum In Ai Ethicsmentioning

confidence: 99%

Interactive Storytelling for Children: A Case-study of Design and Development Considerations for Ethical Conversational AI

Missaouib¹,

Liam²,

Walker³

2021

Preprint

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Conversational Artificial Intelligence (CAI) systems and Intelligent Personal Assistants (IPA), such as Alexa, Cortana, Google Home and Siri are becoming ubiquitous in our lives, including those of children, the implications of which is receiving increased attention, specifically with respect to the effects of these systems on children's cognitive, social and linguistic development. Recent advances address the implications of CAI with respect to privacy, safety, security, and access. However, there is a need to connect and embed the ethical and technical aspects in the design. Using a case-study of a research and development project focused on the use of CAI in storytelling for children, this paper reflects on the social context within a specific case of technology development, as substantiated and supported by argumentation from within the literature. It describes the decision making process behind the recommendations made on this case for their adoption in the creative industries. Further research that engages with developers and stakeholders in the ethics of storytelling through CAI is highlighted as a matter of urgency.

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Section: Momentum In Ai Ethicsmentioning

confidence: 99%

Interactive Storytelling for Children: A Case-study of Design and Development Considerations for Ethical Conversational AI

Missaouib¹,

Liam²,

Walker³

2021

Preprint

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“…These difficult contradiction cast a shadow over the legitimacy of well-intentioned ethics codes and principles and continue to be grappled with even as the field is shifting from the 'what of AI ethics' to a more operational 'how' [8]. Here, it is clear that despite a consensus around ethical principles, we have yet to witness a convergence around ethical practices-despite an emerging literature on technical tools for addressing common ethical challenges (see [8] for an overview of these tools). Two hypotheses may explain this slippage.…”

Section: Ethical Aimentioning

confidence: 99%

“…The first is that the discussion of AI ethics remains too highlevel and abstract, making it difficult for practitioners and technicians to see the their relevance in their daily activities [4], [9]. The second is that there has been insufficient crossfertilisation between ethical regulatory research in academia on the one hand, and real-life application with practitioners on the other [8]. For instance, one study found that AI developers, while aware of the ethical challenges in their work, were not organisationally supported with adequate tools or methods for addressing them in their work [10].…”

Section: Ethical Aimentioning

confidence: 99%

An Ecosystem Approach to Ethical AI and Data Use: Experimental Reflections

Findlay¹,

Seah²

2021

Preprint

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While we have witnessed a rapid growth of ethics documents meant to guide artificial intelligence (AI) development, the promotion of AI ethics has nonetheless proceeded with little input from AI practitioners themselves. Given the proliferation of AI for Social Good initiatives, this is an emerging gap that needs to be addressed in order to develop more meaningful ethical approaches to AI use and development. This paper offers a methodology-a 'shared fairness' approach-aimed at identifying AI practitioners' needs when it comes to confronting and resolving ethical challenges and to find a third space where their operational language can be married with that of the more abstract principles that presently remain at the periphery of their work experiences. We offer a grassroots approach to operational ethics based on dialog and mutualised responsibility: this methodology is centred around conversations intended to elicit practitioners perceived ethical attribution and distribution over key value-laden operational decisions, to identify when these decisions arise and what ethical challenges they confront, and to engage in a language of ethics and responsibility which enables practitioners to internalise ethical responsibility. The methodology bridges responsibility imbalances that rest in structural decision-making power and elite technical knowledge, by commencing with personal, facilitated conversations, returning the ethical discourse to those meant to give it meaning at the sharp end of the ecosystem. Our primary contribution is to add to the recent literature seeking to bring AI practitioners' experiences to the fore by offering a methodology for understanding how ethics manifests as a relational and interdependent sociotechnical practice in their work.

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“…In what follows, I adhere to the interpretation that in order to comply with such guidelines, the development and use of a certain application of AI -for example ABS 2 -should ideally not violate certain moral principles or values 3 entailed in these guidelines. We are here talking about values such as accountability, accuracy, prevention of harm, fairness, non-discrimination, respect for privacy, and transparency, which are all values that have a place in most ethical guidelines for the use of AI (see also Morley et al 2019 for an overview of ethical guidelines for AI/machine learning technology).…”

Section: Introductionmentioning

confidence: 99%

“…Consulting the guidelines and trying to estimate to what extent a specific use of AI complies with them can make us stop and think and discuss whether this particular use is morally acceptable, morally unacceptable or morally obligatory. As Morley et al (2019: 3) have argued, for example, such guidelines can be used as normative constraints for the dos and don'ts of algorithmic use in society. As an example of this, we can refer to the software company Northpoint (now Equivant), which has created an algorithm called COMPAS.…”

Section: Introductionmentioning

confidence: 99%

Ethical guidelines for the use of artificial intelligence and the challenges from value conflicts

Petersen

2021

Etikk Praksis - Nord J Appl Ethics

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The aim of this article is to articulate and critically discuss different answers to the following question: How should decision-makers deal with conflicts that arise when the values usually entailed in ethical guidelines – such as accuracy, privacy, non-discrimination and transparency – for the use of Artificial Intelligence (e.g. algorithm-based sentencing) clash with one another? To begin with, I focus on clarifying some of the general advantages of using such guidelines in an ethical analysis of the use of AI. Some disadvantages will also be presented and critically discussed. Second, I will show that we need to distinguish between three kinds of conflict that can exist for ethical guidelines used in the moral assessment of AI. This section will be followed by a critical discussion of different answers to the question of how to handle what we shall call internal and external values conflicts. Finally, I will wrap up with a critical discussion of three different strategies to resolve what is called a ‘genuine value conflict’. These strategies are: the ‘accepting the existence of irresolvable conflict’ view, the ranking view, and value monism. This article defends the ‘accepting the existence of irresolvable conflict’ view. It also argues that even though the ranking view and value monism, from a merely theoretical (or philosophical) point of view, are better equipped to solve genuine value conflicts among values in ethical guidelines for artificial intelligence, this is not the case in real-life decision-making. Keywords: AI; ethical guidelines; algorithm-based sentencing; value conflicts

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From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices

Cited by 91 publications

References 70 publications

Interactive Storytelling for Children: A Case-study of Design and Development Considerations for Ethical Conversational AI

Interactive Storytelling for Children: A Case-study of Design and Development Considerations for Ethical Conversational AI

An Ecosystem Approach to Ethical AI and Data Use: Experimental Reflections

Ethical guidelines for the use of artificial intelligence and the challenges from value conflicts

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