A Data Analytics Organisation’s Perspective on Trust and AI Adoption

Smit, Danie; Eybers, Sunet; Smith, J.

doi:10.1007/978-3-030-95070-5_4

Cited by 3 publications

(11 citation statements)

References 30 publications

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“…The other two studies are part of the larger research effort and form part of the case study at the analytics competence centre. One of the two studies investigated the barriers to AI adoption [43] and the other the socio-enabling factors leading to organisational AI adoption [42]. Purposively sampled industry focus groups from the analytics competence centre are used to evaluate and develop the framework further.…”

Section: Research Approachmentioning

confidence: 99%

“…The second study, part of the larger research effort, applied the TOE framework and focused on the barriers to adoption and highlights the extent to which fairness, accountability, transparency and explainability influence trust in AI and consequently AI adoption [43]. Online questionnaires involving analytics and AI experts were analysed using structural equation modelling (SEM) as the underlying statistical methodology.…”

Section: Suggested Solutionmentioning

confidence: 99%

“…The findings from the three related studies [43,42,24] are combined into a framework. The proposed SAIAF includes four main areas (see Figure 2).…”

Section: Development Of the Solutionmentioning

confidence: 99%

“…The first is an introduction to AI in a data-driven context, which is followed by a high-level overview of the AI adoption decision stages [42]. Then measures to support AI adoption momentum are listed [42], and lastly, the AI adoption considerations based on the TOE framework are given [24,43].…”

Section: Development Of the Solutionmentioning

confidence: 99%

“…In the context of the SAIAF, a data-driven organisation is defined as an organisation that uses analytical tools and abilities, that creates a culture to integrate and foster analytical expertise and acts on observed data to achieve benefits [43]. The goal is not that AI replaces humans, but rather that AI can support data-driven organisations within a human machine-partnership [29,25] while supporting or automating some decision making [7].…”

Section: Ai In a Data-driven Organisationmentioning

confidence: 99%

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Towards a socio-specific artificial intelligence adoption framework

Smit¹,

Eybers²

EPiC Series in Computing

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Organisations need to be able to adopt AI successfully, but also responsibly. This requirement is not trivial, as AI can deliver real value to adopters. However, can also result in serious impacts on humans. AI’s technical capabilities make AI powerful, still the implementation of AI in organisations is not limited to the technical elements and requires a more holistic approach. An AI implementation within an organisation is a socio-technical system, with the interplay between social and technical components. When AI makes decisions that impact people, the socio considerations in AI adoption frame- works are paramount. Although technical adoption challenges are well researched and can overlap with aspects associated with traditional IT implementations, artificial intelli- gence adoption often faces additional social implication. This study focuses on these social challenges, which is a problem frequently experienced by many organisations. The study investigates how an organisation can increase adoption of AI as part of its quest to become more data-driven. This study was conducted at an automotive manufacturer’s analytics competence centre, located in South Africa. This paper describes the first iteration of a larger research effort that follows the design science research methodology. A socio-specific artificial intelligence adoption framework was created and can be used by organisations to help them succeed with their AI adoption initiatives in a responsible manner.

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Section: Research Approachmentioning

confidence: 99%

Section: Suggested Solutionmentioning

confidence: 99%

“…The findings from the three related studies [43,42,24] are combined into a framework. The proposed SAIAF includes four main areas (see Figure 2).…”

Section: Development Of the Solutionmentioning

confidence: 99%

Section: Development Of the Solutionmentioning

confidence: 99%

Section: Ai In a Data-driven Organisationmentioning

confidence: 99%

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Towards a socio-specific artificial intelligence adoption framework

Smit¹,

Eybers²

EPiC Series in Computing

Self Cite

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Technology Days: An AI Democratisation Journey Begins with a Single Step

Smit

Eybers

Sibanyoni

et al. 2022

Communications in Computer and Information Science

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Ethics and Trustworthiness of AI for Predicting the Risk of Recidivism: A Systematic Literature Review

Farayola,

Tal,

Connolly

et al. 2023

Information

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Artificial Intelligence (AI) can be very beneficial in the criminal justice system for predicting the risk of recidivism. AI provides unrivalled high computing power, speed, and accuracy; all harnessed to strengthen the efficiency in predicting convicted individuals who may be on the verge of recommitting a crime. The application of AI models for predicting recidivism has brought positive effects by minimizing the possible re-occurrence of crime. However, the question remains of whether criminal justice system stakeholders can trust AI systems regarding fairness, transparency, privacy and data protection, consistency, societal well-being, and accountability when predicting convicted individuals’ possible risk of recidivism. These are all requirements for a trustworthy AI. This paper conducted a systematic literature review examining trust and the different requirements for trustworthy AI applied to predicting the risks of recidivism. Based on this review, we identified current challenges and future directions regarding applying AI models to predict the risk of recidivism. In addition, this paper provides a comprehensive framework of trustworthy AI for predicting the risk of recidivism.

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A Data Analytics Organisation’s Perspective on Trust and AI Adoption

Cited by 3 publications

References 30 publications

Towards a socio-specific artificial intelligence adoption framework

Towards a socio-specific artificial intelligence adoption framework

Technology Days: An AI Democratisation Journey Begins with a Single Step

Ethics and Trustworthiness of AI for Predicting the Risk of Recidivism: A Systematic Literature Review

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