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

Minkkinen, Matti; Zimmer, Markus Philipp; Mäntymäki, Matti

doi:10.1007/s10796-022-10269-2

Cited by 27 publications

(11 citation statements)

References 68 publications

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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: Discussionsupporting

confidence: 64%

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

Laato

Tiainen

Islam³

et al. 2022

INTR

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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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Section: Discussionsupporting

confidence: 64%

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

Laato

Tiainen

Islam³

et al. 2022

INTR

Self Cite

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“…Also, as a result of the study, unlike Li et al (2022), Minkkinen et al (2022b), Rehman Khan et al (2022); Saetra (2021), it has been proved that the development of "smart" technologies alone are not enough to increase the volume of ESG investments -the development of institutes of the AI economy is also required. This served as an argument for a clear division of the AI economies developed and developing countries, whose institutes contribute differently (among institutes and categories of countries) to the development of ESG investments.…”

Section: Discussionmentioning

confidence: 98%

“…As practice shows, the availability of advanced technologies is not enough for their application. Based on the technological approach, the works of Li et al (2022), Minkkinen et al (2022b), Abdur Rehman Khan et al (2022), andSaetra (2021) indicate the feasibility of developing "smart" technologies to increase the volume of ESG investments. Focusing on the existing concept of "smart" ESG investments, the AI economy is progressing, but despite the similar level of development of advanced "smart" technologies, there are serious differences in the intensity of automation of ESG investments in developed and developing countries (Jonsdottir et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Fritz-Morgenthal et al (2022), Minkkinen et al (2022b), and Nauck (2019) point out in their works that AI allows (based on the Internet of Things, IoT) to collecting and analyzing Big Data on the topic of green innovation and corporate social and environmental responsibility. This data can be used as a basis for drawing much more complete and reliable internal and external corporate sustainability reporting (Maas, 2018).…”

Section: Introductionmentioning

confidence: 99%

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ESG investing in the AI era: Features of developed and developing countries

Khoruzhy

Semenov

Averin

et al. 2022

Front. Environ. Sci.

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“…This study offers insights for AI governance borrowing from the Internet Governance history and the broader technology governance field. Minkkinen et al, (2023) focus on addressing a gap in knowledge related to the governing AI which requires cooperation, yet the collaboration's form remains unclear. Technological frames provide a theoretical perspective for understanding how actors interpret technology and act upon its development, use, and governance.…”

Section: The Special Issuementioning

confidence: 99%

Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI

et al. 2022

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example the responsible design (Dennehy et al., 2021) and governance (Mäntymäki et al., 2022b) of AI systems. While organisations are increasingly investing in ethical AI and Responsible AI (RAI) (Zimmer et al., 2022), recent reports suggest that this comes at a cost and may lead to burnout in responsible-AI teams (Heikkilä, 2022). Thus, it is critical to consider how we educate about RAI (Grøder et al., 2022) and rethink our traditional learning designs (Pappas & Giannakos, 2021), as this can influence end-users' perceptions towards AI applications (Schmager et al., 2023) as well as how future employees approach the design and implementation of AI applications (Rakova et al., 2021;Vassilakopoulou et al., 2022).The use of algorithmic decision-making and decisionsupport processes, particularly AI is becoming increasingly pervasive in the public sector, also in high-risk application areas such as healthcare, traffic, and finance (European Commission, 2020). Against this backdrop, there is growing concern over the ethical use and safety of AI, fuelled by reports of ungoverned military applications (Butcher and Beridze, 2019; Dignum, 2020), privacy violations attributed to facial recognition technologies used by the police (Rezende, 2022), unwanted biases exhibited by AI applications used by courts (Imai et al., 2020), and racial biases in clinical algorithms (Vyas et al. 2020). The opacity and lack of explainability frequently attributed to AI systems makes evaluating the trustworthiness of algorithmic decisions challenging even for technical experts, let alone the public. Together with the algorithm-propelled proliferation of misinformation, hate speech and polarising content on social media platforms, there is a high risk for erosion of trust in algorithmic systems used by the public sector (Janssen et al., 2020). Ensuring that people can trust in the algorithmic processes is essential not only for reaping the potential benefits from AI (Dignum, 2020) but also for fostering trust and resilience at a societal level.AI researchers and practitioners have expressed their fears about AI systems being developed that are Denis Dennehy

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

Cited by 27 publications

References 68 publications

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

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

ESG investing in the AI era: Features of developed and developing countries

Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI

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