The Current State of Industrial Practice in Artificial Intelligence Ethics

Vakkuri, Ville; Kemell, Kai-Kristian; Kultanen, Joni; Abrahamsson, Pekka

doi:10.1109/ms.2020.2985621

Cited by 64 publications

(48 citation statements)

References 9 publications

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“…For instance, job automation and the substantial displacement of workforces induced by AI has triggered considerable stress for healthcare practitioners (Davenport & Kalakota, 2019). Vakkuri et al (2020) point out that the public is becoming aware of the ethical implications of AI technology, relating to, for example, the lack of data structure (Panch et al, 2019), ethical concerns over its unintended impacts (Peters et al, 2020), and human acceptance of machines (Anderson & Anderson, 2007). Hospitals face not only the challenges of understanding how AI can be deployed responsibly, but also of whether their engagement with AI can subsequently generate positive outcomes such as favourable attitudes, greater satisfaction, and higher usage intentions.…”

Section: Introductionmentioning

confidence: 99%

Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care

et al. 2021

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Artificial Intelligence (AI) technology is transforming the healthcare sector. However, despite this, the associated ethical implications remain open to debate. This research investigates how signals of AI responsibility impact healthcare practitioners’ attitudes toward AI, satisfaction with AI, AI usage intentions, including the underlying mechanisms. Our research outlines autonomy, beneficence, explainability, justice, and non-maleficence as the five key signals of AI responsibility for healthcare practitioners. The findings reveal that these five signals significantly increase healthcare practitioners’ engagement, which subsequently leads to more favourable attitudes, greater satisfaction, and higher usage intentions with AI technology. Moreover, ‘techno-overload’ as a primary ‘techno-stressor’ moderates the mediating effect of engagement on the relationship between AI justice and behavioural and attitudinal outcomes. When healthcare practitioners perceive AI technology as adding extra workload, such techno-overload will undermine the importance of the justice signal and subsequently affect their attitudes, satisfaction, and usage intentions with AI technology.

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Section: Introductionmentioning

confidence: 99%

Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care

et al. 2021

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“…Aside from evaluating ECCOLA, this study provides an initial look into writing ethical user stories. Bridging the gap between research and practice in AI ethics has been a recurring challenge in the area, with companies struggling to implement abstract ethical guidelines in practice [2,3]. User stories can help us bridge this gap.…”

Section: Discussionmentioning

confidence: 99%

“…For example, McNamara et al [9] replicated a prior behavior ethics study, and found out that explicitly instructing participants to consider the ACM Code of Ethics in relation to the impacts of their software development decision-making had no influence on actual ethical decision-making itself. In the field of AI ethics, [2] discovered a gap between research and practice regarding the ways in which AI ethics are implemented. While on the one hand, AI ethics are discussed in academic circles, on the other hand, discussions had not carried over to industrial application.…”

Section: Implementing Ethics Into Software Developmentmentioning

confidence: 99%

“…Furthermore, AI ethics has seldom been studied from the perspective of practical application in SE. Ethically aligned AI/AS development principles and guidelines exist [1], yet as recent research demonstrates [2], there are still major challenges in translating these to practice.…”

Section: Introductionmentioning

confidence: 99%

“…Overall, AI ethics currently seems to be an area with a prominent gap between research and practice [2]. While we now have some degree of consensus on what AI ethics is and what ethical principles and issues are important to consider in AI development [1], translating these principles into concrete action is challenging [2,3]. Organizations and developers seem to struggle with turning ethical guidelines into tangible requirements.…”

Section: Introductionmentioning

confidence: 99%

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How to Write Ethical User Stories? Impacts of the ECCOLA Method

Halme

Vakkuri

Kultanen

et al. 2021

Lecture Notes in Business Information Processing

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Artificial Intelligence (AI) systems are increasing in significance within software services. Unfortunately, these systems are not flawless. Their faults, failures and other systemic issues have emphasized the urgency for consideration of ethical standards and practices in AI engineering. Despite the growing number of studies in AI ethics, comparatively little attention has been placed on how ethical issues can be mitigated in software engineering (SE) practice. Currently understanding is lacking regarding the provision of useful tools that can help companies transform high-level ethical guidelines for AI ethics into the actual workflow of developers. In this paper, we explore the idea of using user stories to transform abstract ethical requirements into tangible outcomes in Agile software development. We tested this idea by studying master’s level student projects (15 teams) developing web applications for a real industrial client over the course of five iterations. These projects resulted in 250+ user stories that were analyzed for the purposes of this paper. The teams were divided into two groups: half of the teams worked using the ECCOLA method for AI ethics in SE, while the other half, a control group, was used to compare the effectiveness of ECCOLA. Both teams were tasked with writing user stories to formulate customer needs into system requirements. Based on the data, we discuss the effectiveness of ECCOLA, and Primary Empirical Contributions (PECs) from formulating ethical user stories in Agile development.

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The AI field needs translational Ethical AI research

Borg

2022

AI Magazine

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Calls for Ethical AI have become urgent and pervasive, especially as ethical issues surrounding AI products at tech companies are increasingly scrutinized by the public. Yet even after a first wave of responses to these calls coalesced around Ethical AI principles to guide decision-making and a second wave generated technical tools to mitigate specific ethical issues, multiple lines of evidence indicate that these Ethical AI principles and technical tools have only a limited impact on the daily practices of AI users and producers. In other words, there is a big gap between what we publish in academic papers and what AI creators need to generate AI products that reflect society's values. Ethical AI is by no means the only field to have this problem. However, when medical and ecology fields documented similar gaps between their fields' scientific discoveries and the practices and products that people actually use, they invested tremendous resources into subfields that developed evidence about how to translate what was done in the lab to adopted solutions. I argue in this commentary that it is our research community's moral duty to invest in our own subfield of "Translational Ethical AI" that will determine how best to ensure AI practitioners can implement the Ethical AI technical tools we publish in academic venues in production settings. Further, I offer concrete steps for doing that, drawing on insights gleaned from other translational fields. Closing the "Ethical AI Publication-to-Practice gap" will be a considerable transdisciplinary challenge, but one of the AI research community has the unique expertise, political leverage, and moral responsibility to tackle.

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The Current State of Industrial Practice in Artificial Intelligence Ethics

Abstract: This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details.

Cited by 64 publications

References 9 publications

Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care

Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care

How to Write Ethical User Stories? Impacts of the ECCOLA Method

The AI field needs translational Ethical AI research

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