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Artificial intelligence and ethics within the food sector: developing a common language for technology adoption across the supply chain

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Artificial intelligence and ethics within the food sector: developing a common language for technology adoption across the supply chain. / Manning, Louise; Brewer, Steve; Craigon, Peter J. et al.
In: Trends in Food Science and Technology, Vol. 125, 10.05.2022, p. 33-42.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Manning, L, Brewer, S, Craigon, PJ, Frey, J, Gutierrez, A, Jacobs, N, Kanza, S, Munday, S, Sacks, J & Pearson, S 2022, 'Artificial intelligence and ethics within the food sector: developing a common language for technology adoption across the supply chain', Trends in Food Science and Technology, vol. 125, pp. 33-42. https://doi.org/10.1016/j.tifs.2022.04.025

APA

Manning, L., Brewer, S., Craigon, P. J., Frey, J., Gutierrez, A., Jacobs, N., Kanza, S., Munday, S., Sacks, J., & Pearson, S. (2022). Artificial intelligence and ethics within the food sector: developing a common language for technology adoption across the supply chain. Trends in Food Science and Technology, 125, 33-42. https://doi.org/10.1016/j.tifs.2022.04.025

Vancouver

Manning L, Brewer S, Craigon PJ, Frey J, Gutierrez A, Jacobs N et al. Artificial intelligence and ethics within the food sector: developing a common language for technology adoption across the supply chain. Trends in Food Science and Technology. 2022 May 10;125:33-42. Epub 2022 Apr 30. doi: 10.1016/j.tifs.2022.04.025

Author

Manning, Louise ; Brewer, Steve ; Craigon, Peter J. et al. / Artificial intelligence and ethics within the food sector : developing a common language for technology adoption across the supply chain. In: Trends in Food Science and Technology. 2022 ; Vol. 125. pp. 33-42.

Bibtex

@article{bd59669c1acf46cbb99c68e23dd7662c,
title = "Artificial intelligence and ethics within the food sector: developing a common language for technology adoption across the supply chain",
abstract = "Background: The use of artificial intelligence (AI) is growing in food supply chains. The ethical language associated with food supply and technology is contextualised and framed by the meaning given to it by stakeholders. Failure to differentiate between these nuanced meanings can create a barrier to technology adoption and reduce the benefit derived.Scope and approach: The aim of this review paper is to consider the embedded ethical language used by stakeholders who collaborate in the adoption of AI in food supply chains. Ethical perspectives frame this literature review and provide structure to consider how to shape a common discourse to build trust in, and frame more considered utilisation of, AI in food supply chains to the benefit of users, and wider society.Key findings and conclusions: Whilst the nature of data within the food system is much broader than the personal data covered by the European Union General Data Protection Regulation (GDPR), the ethical issues for computational and AI systems are similar and can be considered in terms of particular aspects: transparency, traceability, explainability, interpretability, accessibility, accountability and responsibility. The outputs of this research assist in giving a more rounded understanding of the language used, exploring the ethical interaction of aspects of AI used in food supply chains and also the management activities and actions that can be adopted to improve the applicability of AI technology, increase engagement and derive greater performance benefits. This work has implications for those developing AI governance protocols for the food supply chain as well as supply chain practitioners.",
keywords = "Responsibility, Accessibility, Explainability, Accountability, Interoperability, Artificial intelligence",
author = "Louise Manning and Steve Brewer and Craigon, {Peter J.} and Jeremy Frey and Anabel Gutierrez and Naomi Jacobs and Samantha Kanza and Samuel Munday and Justin Sacks and Simon Pearson",
year = "2022",
month = may,
day = "10",
doi = "10.1016/j.tifs.2022.04.025",
language = "English",
volume = "125",
pages = "33--42",
journal = "Trends in Food Science and Technology",
issn = "0924-2244",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - Artificial intelligence and ethics within the food sector

T2 - developing a common language for technology adoption across the supply chain

AU - Manning, Louise

AU - Brewer, Steve

AU - Craigon, Peter J.

AU - Frey, Jeremy

AU - Gutierrez, Anabel

AU - Jacobs, Naomi

AU - Kanza, Samantha

AU - Munday, Samuel

AU - Sacks, Justin

AU - Pearson, Simon

PY - 2022/5/10

Y1 - 2022/5/10

N2 - Background: The use of artificial intelligence (AI) is growing in food supply chains. The ethical language associated with food supply and technology is contextualised and framed by the meaning given to it by stakeholders. Failure to differentiate between these nuanced meanings can create a barrier to technology adoption and reduce the benefit derived.Scope and approach: The aim of this review paper is to consider the embedded ethical language used by stakeholders who collaborate in the adoption of AI in food supply chains. Ethical perspectives frame this literature review and provide structure to consider how to shape a common discourse to build trust in, and frame more considered utilisation of, AI in food supply chains to the benefit of users, and wider society.Key findings and conclusions: Whilst the nature of data within the food system is much broader than the personal data covered by the European Union General Data Protection Regulation (GDPR), the ethical issues for computational and AI systems are similar and can be considered in terms of particular aspects: transparency, traceability, explainability, interpretability, accessibility, accountability and responsibility. The outputs of this research assist in giving a more rounded understanding of the language used, exploring the ethical interaction of aspects of AI used in food supply chains and also the management activities and actions that can be adopted to improve the applicability of AI technology, increase engagement and derive greater performance benefits. This work has implications for those developing AI governance protocols for the food supply chain as well as supply chain practitioners.

AB - Background: The use of artificial intelligence (AI) is growing in food supply chains. The ethical language associated with food supply and technology is contextualised and framed by the meaning given to it by stakeholders. Failure to differentiate between these nuanced meanings can create a barrier to technology adoption and reduce the benefit derived.Scope and approach: The aim of this review paper is to consider the embedded ethical language used by stakeholders who collaborate in the adoption of AI in food supply chains. Ethical perspectives frame this literature review and provide structure to consider how to shape a common discourse to build trust in, and frame more considered utilisation of, AI in food supply chains to the benefit of users, and wider society.Key findings and conclusions: Whilst the nature of data within the food system is much broader than the personal data covered by the European Union General Data Protection Regulation (GDPR), the ethical issues for computational and AI systems are similar and can be considered in terms of particular aspects: transparency, traceability, explainability, interpretability, accessibility, accountability and responsibility. The outputs of this research assist in giving a more rounded understanding of the language used, exploring the ethical interaction of aspects of AI used in food supply chains and also the management activities and actions that can be adopted to improve the applicability of AI technology, increase engagement and derive greater performance benefits. This work has implications for those developing AI governance protocols for the food supply chain as well as supply chain practitioners.

KW - Responsibility

KW - Accessibility

KW - Explainability

KW - Accountability

KW - Interoperability

KW - Artificial intelligence

U2 - 10.1016/j.tifs.2022.04.025

DO - 10.1016/j.tifs.2022.04.025

M3 - Journal article

VL - 125

SP - 33

EP - 42

JO - Trends in Food Science and Technology

JF - Trends in Food Science and Technology

SN - 0924-2244

ER -