Tác động xã hội và đạo đức của trí tuệ nhân tạo trong nông nghiệp: lập bản đồ tài liệu AI trong nông nghiệp

Aggarwal N, Singh D (2021) Technology assisted farming: implications of IoT and AI. In: IOP conference series: materials science and engineering. IOP Publishing, p 012080. Albiero D (2019) Agricultural robotics: a promising challenge. Curr Agric Res J 7:1 Anderson, J., Rainie, L., Luchsinger, A., 2018. Artificial Intelligence and the Future of Humans. Pew Res. Cent. Dec. Aronson J (1995) A pragmatic view of thematic analysis. Qual Rep 2:1–3 Bekken G (2019) The algorithmic governance of data driven-processing employment: evidence-based management practices, artificial intelligence recruiting software, and automated hiring decisions Psychosociological Issues. Hum Resour Manag 7:25–30. https://doi.org/10.22381/pihrm7220194 Belk R (2020) Ethical issues in service robotics and artificial intelligence. Serv Ind J 2:1–17 Beyan T, Felzmann H, Ryan MJ, Lang N, Beyan O (2015) D1.2 ethics framework: managing active and healthy aging with use of caring service robots Birhane A, van Dijk J (2020) Robot rights? Let’s talk about human welfare instead. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. pp. 207–213. Bogomolov A, Nevezhin V, Larionova M, Piskun E, (2021) Review of digital technologies in agriculture as a factor that removes the growth limits to human civilization. In: E3S Web of Conferences. EDP Sciences, p. 01074 Bossman J (2016) Top 9 ethical issues in artificial intelligence [WWW Document]. World Econ. Forum. URL https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/ (accessed 5.10.19). Braun V, Clarke V (2006) Using thematic analysis in psychology. Qual Res Psychol 3:77–101 Brewer S, Pearson S, Maull R, Godsiff P, Frey JG, Zisman A, Parr G, McMillan A, Cameron S, Blackmore H, Manning L, Bidaut L (2021) A trust framework for digital food systems. Nat Food 2:543–545. https://doi.org/10.1038/s43016-021-00346-1 Bronson K (2019) Looking through a responsible innovation lens at uneven engagements with digital farming. NJAS-Wagening J Life Sci 90:100294 Bronson K, Knezevic I (2016) Big data in food and agriculture. Big Data Soc 3:2053951716648174 Camaréna S (2020) Artificial intelligence in the design of transition to sustainable food systems. J Clean Prod 2:122574 Carolan MS (2006) Social change and the adoption and adaptation of knowledge claims: Whose truth do you trust in regard to sustainable agriculture? Agric Hum Values 23:325–339 Carolan M (2017) Publicising food: big data, precision agriculture, and co-experimental techniques of addition. Sociol Rural 57:135–154 Carolan M (2020) Automated agrifood futures: robotics, labor and the distributive politics of digital agriculture. J Peasant Stud 47:184–207 CNIL (2017) How can humans keep the upper hand? Report on the ethical matters raised by algorithms and artificial intelligence [WWW Document]. URL https://www.cnil.fr/en/how-can-humans-keep-upper-hand-report-ethical-matters-raised-algorithms-and-artificial-intelligence (accessed 5.14.19). Donhauser J, van Wynsberghe A, Bearden A (2020) Steps toward an ethics of environmental robotics. Philos Technol 2:1–18 Driessen C, Heutinck LF (2015) Cows desiring to be milked? Milking robots and the co-evolution of ethics and technology on Dutch dairy farms. Agric Hum Values 32:3–20 ECPA (2018) Code of Conduct on agricultural data sharing signing [WWW Document]. ECPA. URL https://www.ecpa.eu/media/news/code-conduct-agricultural-data-sharing-signing (accessed 8.28.20). European Commission (2018) Artificial Intelligence for Europe (Communication No. COM(2018) 237 final). European Commission, Brussels. Farm Data Code of Practice Authority (2014) New Zealand Farm Data Code of Practice. Fleming A, Jakku E, Lim-Camacho L, Taylor B, Thorburn P (2018) Is big data for big farming or for everyone? Perceptions in the Australian grains industry. Agron Sustain Dev 38:24 Floridi L (2019) Establishing the rules for building trustworthy AI. Nat Mach Intell. https://doi.org/10.1038/s42256-019-0055-y Frey CB, Osborne MA (2017) The future of employment: how susceptible are jobs to computerisation? Technol Forecast Soc Change 114:254–280. https://doi.org/10.1016/j.techfore.2016.08.019 Gardezi M, Stock R (2021) Growing algorithmic governmentality: interrogating the social construction of trust in precision agriculture. J Rural Stud 84:1–11. https://doi.org/10.1016/j.jrurstud.2021.03.004 Hleg AI (2020) Assessment list for trustworthy AI (ALTAI). European Commission, Brussels Hleg A (2019) Ethics guidelines for trustworthy AI. Retrieved from High-Level Expert Group on Artificial Intelligence (AI HLEG …. Jakku E, Taylor B, Fleming A, Mason C, Fielke S, Sounness C, Thorburn P (2019) “If they don’t tell us what they do with it, why would we trust them?” Trust, transparency and benefit-sharing in Smart Farming. NJAS-Wagening. J. Life Sci. 90:100285 Jayashankar P, Nilakanta S, Johnston WJ, Gill P, Burres R (2018) IoT adoption in agriculture: the role of trust, perceived value and risk. J Bus Ind Mark 2:2 Jobin A, Ienca M, Vayena E (2019) The global landscape of AI ethics guidelines. Nat Mach Intell 1:389–399. https://doi.org/10.1038/s42256-019-0088-2 Klerkx L, Jakku E, Labarthe P (2019) A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda NJAS-Wagening. J Life Sci 90:100315 Krishnan A, Swarna S (2020) Robotics, IoT, and AI in the automation of agricultural industry: a review. in: 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC). IEEE, pp. 1–6 Lewis D, Moorkens J (2020) A rights-based approach to trustworthy AI in social media. Soc MEDIA Soc. https://doi.org/10.1177/2056305120954672 Malta Digital Innovation Authority (2019) Malta: Towards Trustworthy AI - Malta’s Ethical AI Framework. Malta. Marinoudi V, Sørensen CG, Pearson S, Bochtis D (2019) Robotics and labour in agriculture. A context consideration. Biosyst Eng 184:111–121. https://doi.org/10.1016/j.biosystemseng.2019.06.013 Milton CL (2021) Risking human dignity with innovations: artificial intelligence and the future of the discipline of nursing. Nurs Sci Q 34:244–246 Pylianidis C, Osinga S, Athanasiadis IN (2021) Introducing digital twins to agriculture. Comput Electron Agric 184:105942 Rodzalan SA, Yin OG, Noor NNM (2020) A foresight study of artificial intelligence in the agriculture sector in Malaysia. J Crit Rev 7:1339–1346 Rose DC, Chilvers J (2018) Agriculture 4.0: Broadening responsible innovation in an era of smart farming. Front. Sustain. Food Syst. 2:87 Rose DC, Lyon J, de Boon A, Hanheide M, Pearson S (2021) Responsible development of autonomous robotics in agriculture. Nat Food 2:306–309 Ruiz-Real JL, Uribe-Toril J, Torres Arriaza JA, de Pablo Valenciano J (2020) A look at the past, present and future research trends of artificial intelligence in agriculture. Agronomy 10:1839 Russell S (2021) Human-Compatible Artificial Intelligence. Human-Like Machine Intelligence. Oxford University Press, Oxford, pp 3–23. https://doi.org/10.1093/oso/9780198862536.003.0001 Ryan M (2019) Ethics of using AI and big data in agriculture: the case of a large agriculture multinational. ORBIT J 2:2 Ryan M (2020) Agricultural big data analytics and the ethics of power. J Agric Environ Ethics 33:49–69 Ryan M, Stahl BC (2020) Artificial intelligence ethics guidelines for developers and users: clarifying their content and normative implications. J Inf Commun Ethics Soc 2:2 Ryan M, van der Burg S, Bogaardt M-J (2021) Identifying key ethical debates for autonomous robots in agri-food: a research agenda. AI Ethics. https://doi.org/10.1007/s43681-021-00104-w Shamshiri R, Weltzien C, Hameed IA, Yule I, Grift T, Balasundram SK, Pitonakova L, Ahmad D, Chowdhary G (2018) Research and development in agricultural robotics: a perspective of digital farming. Sonka S (2016) Big data: fueling the next evolution of agricultural innovation. J Innov Manag 4:114–136 Sonka S, Cheng Y-T (2015) Big Data in Farming: Why Matters! Farmdoc Dly. 5:2 Sparrow R, Howard M (2020) Robots in agriculture: prospects, impacts, ethics, and policy. Precis Agric 3:1–16 Stock R, Gardezi M (2021) Make bloom and let wither: Biopolitics of precision agriculture at the dawn of surveillance capitalism. Geoforum 122:193–203 van der Burg S, Wiseman L, Krkeljas J (2020) Trust in farm data sharing: reflections on the EU code of conduct for agricultural data sharing. Ethics Inf Technol 2:1–14 van Wynsberghe A (2021) Sustainable AI: AI for sustainability and the sustainability of AI. AI Ethics 2:1–6 van Wynsberghe A, Donhauser J (2018) The dawning of the ethics of environmental robots. Sci Eng Ethics 24:1777–1800 Vasconez JP, Kantor GA, Auat Cheein FA (2019) Human–robot interaction in agriculture: a survey and current challenges. Biosyst Eng 179:35–48. https://doi.org/10.1016/j.biosystemseng.2018.12.005 Vik J, Stræte EP, Hansen BG, Nærland T (2019) The political robot–The structural consequences of automated milking systems (AMS) in Norway. NJAS-Wagening. J Life Sci 90:100305 Vincent J (2019) Forty percent of “AI startups” in Europe don’t actually use AI, claims report [WWW Document]. The Verge. URL https://www.theverge.com/2019/3/5/18251326/ai-startups-europe-fake-40-percent-mmc-report (accessed 7.14.21). Wang T, Xu X, Wang C, Li Z, Li D (2021) From smart farming towards unmanned farms: a new mode of agricultural production. Agriculture 11:145 Wiseman L, Sanderson J (2018) Legal and trust issues in Australian agriculture. Presented at the 40th Annual conference australian society of sugar cane technologists. ASSCT 2018:1–9 Wiseman L, Sanderson J, Zhang A, Jakku E (2019) Farmers and their data: an examination of farmers’ reluctance to share their data through the lens of the laws impacting smart farming NJAS-Wagening. J Life Sci. https://doi.org/10.1016/j.njas.2019.04.007 Ministry of Economic Affairs and Employment (2018) Work in the age of artificial intelligence. Zardiashvili L, Fosch-Villaronga E (2020) “Oh, dignity too?” said the robot: human dignity as the basis for the governance of robotics. Minds Mach 2:1–23 Zicari RV (2021) Z-inspection a process to assess trustworthy AI. https://doi.org/10.1109/TTS.2021.3066209