Artificial Intelligence Governance For Businesses

Schneider, Johannes; Abraham, Rene; Meske, Christian

doi:10.1080/10580530.2022.2085825

Cited by 51 publications

(24 citation statements)

References 71 publications

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“…A growing literature stream discusses the organizational challenges related to governing AI systems, which we label the "easy problem" of AI governance [11], [12], [31]- [33]. The "easy problem" concerns how organizations' use of AI systems aligns with laws, values, and norms stemming from legislation, ethics guidelines, and the surrounding society.…”

Section: B the "Easy Problem" Of Ai Governancementioning

confidence: 99%

Discerning Between the “Easy” and “Hard” Problems of AI Governance

Minkkinen

Mäntymäki

2023

IEEE Trans. Technol. Soc.

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While there is widespread consensus that artificial intelligence (AI) needs to be governed owing to its rapid diffusion and societal implications, the current scholarly discussion on AI governance is dispersed across numerous disciplines and problem domains. This paper clarifies the situation by discerning two problem areas, metaphorically titled the "easy" and "hard" problems of AI governance, using a dialectic theory synthesis approach. The "easy problem" of AI governance concerns how organizations' design, development, and use of AI systems align with laws, values, and norms stemming from legislation, ethics guidelines, and the surrounding society. Organizations can provisionally solve the "easy problem" by implementing appropriate organizational mechanisms to govern data, algorithms, and algorithmic systems. The "hard problem" of AI governance concerns AI as a general-purpose technology that transforms organizations and societies. Rather than a matter to be resolved, the "hard problem" is a sensemaking process regarding socio-technical change. Partial solutions to the "hard problem" may open unforeseen issues. While societies should not lose track of the "hard problem" of AI governance, there is significant value in solving the "easy problem" for two reasons. First, the "easy problem" can be provisionally solved by tackling bias, harm, and transparency issues. Second, solving the "easy problem" helps solve the "hard problem," as responsible organizational AI practices create virtuous rather than vicious cycles.

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Section: B the "Easy Problem" Of Ai Governancementioning

confidence: 99%

Discerning Between the “Easy” and “Hard” Problems of AI Governance

Minkkinen

Mäntymäki

2023

IEEE Trans. Technol. Soc.

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“…Nevertheless, there are emerging governance approaches to AI worth noting, and these both can and should be considered when using the AI ESG protocol. The protocol itself favors no specific approach, and simply requires an entity to describe and disclose their approach to the governance of AI and data related risks and opportunities, and this could be based on some of the approaches to AI governance being developed (Mäntymäki et al, 2022a; Mäntymäki et al, 2022b; Papagiannidis et al, 2022; Schneider et al, 2022). It is, however, imperative that AI governance is seen as an integrated part of an entity's existing governance structure, and the proposals by Mäntymäki et al (2022a) and Mäntymäki et al (2022b) account for this need and focus on AI unique aspects and how AI governance relates to, for example, IT and data governance.…”

Section: Esg and Aimentioning

confidence: 99%

The AI ESG protocol: Evaluating and disclosing the environment, social, and governance implications of artificial intelligence capabilities, assets, and activities

Sætra

2022

Sustainable Development

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AI and data are key strategic resources and enablers of the digital transition. Artificial Intelligence (AI) and data are also intimately related to a company's environment, social, and governance (ESG) performance and the generation of sustainability related impacts. These impacts are increasingly scrutinized by markets and other stakeholders, as ESG performance impacts both valuation and risk assessments. It impacts an entity's potential to contribute to good, but it also relates to risks concerning, for example, alignment with current and coming regulations and frameworks. There is currently limited information on and a lack of a unified approach to AI and ESG and a need for tools for systematically assessing and disclosing the ESG related impacts of AI and data capabilities. I here propose the AI ESG protocol, which is a flexible high-level tool for evaluating and disclosing such impacts, engendering increased awareness of impacts, better AI governance, and stakeholder communication.

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“…The increasing AI capabilities and applications bring novel risks and potential harms for individuals and societies, such as lack of transparency and accountability, as well as biases against individuals and groups (Dignum, 2020;Floridi et al, 2018;Martin, 2019). These challenges and risks related to AI systems underscore the importance of AI governance at the organizational, interorganizational, and societal levels (Laato et al, 2022;Mäntymäki et al, 2022a, b;Minkkinen et al, 2022a, b;Schneider et al, 2022;Seppälä et al, 2021). As a closely related parallel to governance, auditing of AI is promoted as a means of tackling risks by holding AI systems and organizations that use AI to certain criteria and by requiring necessary controls (Koshiyama et al, 2021;Minkkinen et al, 2022a, b;Mökander et al, 2021;Sandvig et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Continuous Auditing of Artificial Intelligence: a Conceptualization and Assessment of Tools and Frameworks

Minkkinen

Laine

Mäntymäki

2022

DISO

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Artificial intelligence (AI), which refers to both a research field and a set of technologies, is rapidly growing and has already spread to application areas ranging from policing to healthcare and transport. The increasing AI capabilities bring novel risks and potential harms to individuals and societies, which auditing of AI seeks to address. However, traditional periodic or cyclical auditing is challenged by the learning and adaptive nature of AI systems. Meanwhile, continuous auditing (CA) has been discussed since the 1980s but has not been explicitly connected to auditing of AI. In this paper, we connect the research on auditing of AI and CA to introduce CA of AI (CAAI). We define CAAI as a (nearly) real-time electronic support system for auditors that continuously and automatically audits an AI system to assess its consistency with relevant norms and standards. We adopt a bottom-up approach and investigate the CAAI tools and methods found in the academic and grey literature. The suitability of tools and methods for CA is assessed based on criteria derived from CA definitions. Our study findings indicate that few existing frameworks are directly suitable for CAAI and that many have limited scope within a particular sector or problem area. Hence, further work on CAAI frameworks is needed, and researchers can draw lessons from existing CA frameworks; however, this requires consideration of the scope of CAAI, the human–machine division of labour, and the emerging institutional landscape in AI governance. Our work also lays the foundation for continued research and practical applications within the field of CAAI.

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Artificial Intelligence Governance For Businesses

Cited by 51 publications

References 71 publications

Discerning Between the “Easy” and “Hard” Problems of AI Governance

Discerning Between the “Easy” and “Hard” Problems of AI Governance

The AI ESG protocol: Evaluating and disclosing the environment, social, and governance implications of artificial intelligence capabilities, assets, and activities

Continuous Auditing of Artificial Intelligence: a Conceptualization and Assessment of Tools and Frameworks

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