Toward AI Governance: Identifying Best Practices and Potential Barriers and Outcomes

Papagiannidis, Emmanouil; Enholm, Ida Merete; Dremel, Chirstian; Mikalef, Patrick; Krogstie, John

doi:10.1007/s10796-022-10251-y

Cited by 32 publications

(24 citation statements)

References 49 publications

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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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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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“…Their study contributes to the literature on technological frames, technology-centred ecosystems, and RAI while also elucidating the dimensions and co-shaping of technological frames. Papagiannidis et al, (2023) highlight that despite the use of AI, companies still face challenges and cannot quickly realise performance gains. Adding to the above, firms need to introduce robust AI systems and minimise AI risks, which places a strong emphasis on establishing appropriate AI governance practices.…”

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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“…Inspecting the Ethereum network, the average time to mine a block is 13 seconds. 2 As for the block size, the maximum allowed size in gas is 30 million. 3 Polygon has a similar block size but a block time of 2 seconds.…”

Section: Performance Analysismentioning

confidence: 99%

“…The adoption rates of AI in different domains, such as healthcare, finance, and transport, are consistently increasing. There have been many challenges posed since the inception of AI [1], [2]. For example, AI models and datasets are scattered among individuals, organizations, and companies, hindering the potential of AI and creating a barrier to further contributions [3].…”

Section: Introductionmentioning

confidence: 99%

Blockchain and NFTs for Trusted Ownership, Trading, and Access of AI Models

et al. 2022

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The demand for high-quality Artificial Intelligence (AI) models is ever-increasing in this digital era. However, most of the existing methods leveraged for managing the ownership, trading, and access of AI models fall short of providing traceability, transparency, audit, security, and trustful features. In this paper, we propose a solution based on blockchain and Non-fungible Tokens (NFTs) to manage ownership rights and exchange of AI models in a transparent, traceable, auditable, secure, and trustworthy manner. Smart contracts are employed to enforce ownership, ease of access, and exchange policies for the unique NFT linked to an AI model. We use decentralized storage of the InterPlanetary File System (IPFS) and proxy re-encryption oracles to securely fetch, store, and share data related to AI models. We present algorithms along with their implementation, testing, and validation details. The proposed solution is evaluated using cost and security analyses to show its affordability and resiliency against security threats and attacks. All smart contract codes are made publicly available on GitHub.

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Toward AI Governance: Identifying Best Practices and Potential Barriers and Outcomes

Cited by 32 publications

References 49 publications

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

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

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

Blockchain and NFTs for Trusted Ownership, Trading, and Access of AI Models

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