Defining organizational AI governance

Mäntymäki, Matti; Minkkinen, Matti; Birkstedt, Teemu; Viljanen, Mika

doi:10.1007/s43681-022-00143-x

Cited by 67 publications

(43 citation statements)

References 46 publications

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“…As the use of artificial intelligence (AI) spreads across industries and societies, it has become evident that AIbased systems need to be effectively governed to avoid, or at least mitigate, potential risks and harms (Gasser and Almeida 2017;Butcher and Beridze 2019). AI governance requires not only ethical principles and guidelines but also and especially actionable tools and mechanisms (Brundage et al 2020;Shneiderman 2020;Mäntymäki et al 2022). Translation from abstract principles to practical tools for AI development and governance (Morley et al 2020;Seppälä et al 2021;Mäntymäki et al 2022) is a critical challenge that hinders the widespread adoption and beneficial use of AI (Cowls et al 2021).…”

Section: Introductionmentioning

confidence: 99%

“…AI governance requires not only ethical principles and guidelines but also and especially actionable tools and mechanisms (Brundage et al 2020;Shneiderman 2020;Mäntymäki et al 2022). Translation from abstract principles to practical tools for AI development and governance (Morley et al 2020;Seppälä et al 2021;Mäntymäki et al 2022) is a critical challenge that hinders the widespread adoption and beneficial use of AI (Cowls et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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What about investors? ESG analyses as tools for ethics-based AI auditing

2022

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Artificial intelligence (AI) governance and auditing promise to bridge the gap between AI ethics principles and the responsible use of AI systems, but they require assessment mechanisms and metrics. Effective AI governance is not only about legal compliance; organizations can strive to go beyond legal requirements by proactively considering the risks inherent in their AI systems. In the past decade, investors have become increasingly active in advancing corporate social responsibility and sustainability practices. Including nonfinancial information related to environmental, social, and governance (ESG) issues in investment analyses has become mainstream practice among investors. However, the AI auditing literature is mostly silent on the role of investors. The current study addresses two research questions: (1) how companies’ responsible use of AI is included in ESG investment analyses and (2) what connections can be found between principles of responsible AI and ESG ranking criteria. We conducted a series of expert interviews and analyzed the data using thematic analysis. Awareness of AI issues, measuring AI impacts, and governing AI processes emerged as the three main themes in the analysis. The findings indicate that AI is still a relatively unknown topic for investors, and taking the responsible use of AI into account in ESG analyses is not an established practice. However, AI is recognized as a potentially material issue for various industries and companies, indicating that its incorporation into ESG evaluations may be justified. There is a need for standardized metrics for AI responsibility, while critical bottlenecks and asymmetrical knowledge relations must be tackled.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

What about investors? ESG analyses as tools for ethics-based AI auditing

2022

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“…

The governance of AI and the promotion of its socially responsible development and use are large-scale challenges that take place among multiple actors (cf. Minkkinen et al 2022;Mäntymäki et al, 2022;Seppälä et al, 2021; Yeung et al, 2020). Accordingly, the European Union (EU) has articulated an ecosystem approach to responsible AI (RAI) (European Commission, 2020).

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confidence: 99%

Co-Shaping an Ecosystem for Responsible AI: Five Types of Expectation Work in Response to a Technological Frame

2022

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Governing artificial intelligence (AI) requires cooperation, although the collaboration’s form remains unclear. Technological frames provide a theoretical perspective for understanding how actors interpret a technology and act upon its development, use, and governance. However, we know little about how actors shape technological frames. In this paper, we study the shaping of the technological frame of the European ecosystem for responsible AI (RAI). Through an analysis of EU documents, we identified four expectations that constitute the EU’s technological frame for the RAI ecosystem. Moreover, through interviews with RAI actors, we revealed five types of expectation work responding to this frame: reproducing, translating, and extending (congruent expectation work), and scrutinizing and rooting (incongruent expectation work). Furthermore, we conceptualize expectation work as actors’ purposive actions in creating and negotiating expectations. Our study contributes to the literature on technological frames, technology-centered ecosystems, and RAI while also elucidating the dimensions and co-shaping of technological frames.

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“…Hence, the developers must create tools and systems that enable end users to understand the system they are using and feel control of it. Moreover, this connects to the discussion on the role of XAI as an element of AI governance (Minkkinen et al ., 2020b, 2022a; Mäntymäki et al. , 2022; Seppälä et al.…”

Section: Discussionmentioning

confidence: 99%

How to explain AI systems to end users: a systematic literature review and research agenda

Laato

Tiainen

Islam³

et al. 2022

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PurposeInscrutable machine learning (ML) models are part of increasingly many information systems. Understanding how these models behave, and what their output is based on, is a challenge for developers let alone non-technical end users.Design/methodology/approachThe authors investigate how AI systems and their decisions ought to be explained for end users through a systematic literature review.FindingsThe authors’ synthesis of the literature suggests that AI system communication for end users has five high-level goals: (1) understandability, (2) trustworthiness, (3) transparency, (4) controllability and (5) fairness. The authors identified several design recommendations, such as offering personalized and on-demand explanations and focusing on the explainability of key functionalities instead of aiming to explain the whole system. There exists multiple trade-offs in AI system explanations, and there is no single best solution that fits all cases.Research limitations/implicationsBased on the synthesis, the authors provide a design framework for explaining AI systems to end users. The study contributes to the work on AI governance by suggesting guidelines on how to make AI systems more understandable, fair, trustworthy, controllable and transparent.Originality/valueThis literature review brings together the literature on AI system communication and explainable AI (XAI) for end users. Building on previous academic literature on the topic, it provides synthesized insights, design recommendations and future research agenda.

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Defining organizational AI governance

Cited by 67 publications

References 46 publications

What about investors? ESG analyses as tools for ethics-based AI auditing

What about investors? ESG analyses as tools for ethics-based AI auditing

Co-Shaping an Ecosystem for Responsible AI: Five Types of Expectation Work in Response to a Technological Frame

How to explain AI systems to end users: a systematic literature review and research agenda

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