Governance and Compliance Recommendations for Artificial Intelligence in Business Management

Authors

DOI:

https://doi.org/10.25057/2500672X.1665

Keywords:

Compliance, Business Management, Artificial Intelligence, Recommendations

Abstract

The research problem of this article is the following: what are the possible legal issues regarding the use of Artificial Intelligence in business management, and how can they be solved? The integrated research, the bibliographic research technique, and the Boolean technique are used in this work. The database used was Google Scholar. The search terms were “Artificial Intelligence” + “management” + “review” and “Artificial Intelligence” + “Organizations” + “review”. The justification for limiting the search to the term "review" lies in the extensive and qualified bibliography of integrated reviews. The articles were selected based on the following criteria: a) open-source availability; b) simultaneous combination of search terms; c) thematic articles on business management; and d) chronology (after 2020). As a result, the main areas for the use of AI in business management are innovation; supply chain management; decision-making; human resources; strategic management; and product management. Furthermore, the possible legal issues that can be faced are lack of accountability; biased decisions; discrimination; non-compliance with digital literacy; violation of privacy; and unfair decisions. Finally, the original contributions of this work are 12 Governance recommendations and 8 Compliance recommendations.

Downloads

Download data is not yet available.

Author Biography

Sthéfano Divino, Universidad Federal de Lavras

Ph.D and Master in Law at Pontifícia Universidade Católica de Minas Gerais. Full Professor of Private Law at the Federal University of Lavras. Researcher on Law and Technologies, with emphasis on Privacy and Artificial Intelligence. Leader of the Center for Studies in Private Law, Innovation, and Technology.

References

Abbott, R., & Rothman, E. (2023). Disrupting creativity: Copyright law in the age of generative artificial intelligence. Florida Law Review, 75(6), 1141. https://www.floridalawreview.com/article/91299-disrupting-creativity-copyright-law-in-the-age-of-generative-artificial-intelligence

Al Mansoori, S., Salloum, S. A., & Shaalan, K. (2020). The impact of artificial intelligence and information technologies on the efficiency of knowledge management at modern organizations: a systematic review. In M. Al-Emran, K. Shaalan, & A. E. Hassanien (eds.), Recent advances in intelligent systems and smart applications (pp. 163-182). Springer.

Aksnes, D. W., Langfeldt, L., & Wouters, P. (2019). Citations, citation indicators, and research quality: An overview of basic concepts and theories. Sage Open, 9(1), 2158244019829575. https://doi.org/10.1177/2158244019829575

Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159-182. https://doi.org/10.1002/job.2735

Bouschery, S. G., Blazevic, V., & Piller, F. T. (2023). Augmenting human innovation teams with artificial intelligence: Exploring transformer‐based language models. Journal of Product Innovation Management, 40(2), 139-153. https://doi.org/10.1111/jpim.12656

Brock, J. K. U., & Von Wangenheim, F. (2019). Demystifying AI: What digital transformation leaders can teach you about realistic artificial intelligence. California Management Review, 61(4), 110-134. https://doi.org/10.1177/1536504219865226

Busuioc, M. (2021). Accountable artificial intelligence: Holding algorithms to account. Public Administration Review, 81(5), 825-836. https://doi.org/10.1111/puar.13293

Carrillo-Mondéjar, J., Martínez, J. L., & Suarez-Tangil, G. (2020). Characterizing Linux-based malware: Findings and recent trends. Future Generation Computer Systems, 110, 267-281. https://doi.org/10.1016/j.future.2020.04.031

Chuan, C. H., Tsai, W. H. S., & Yang, J. (2023). Artificial Intelligence, Advertising, and Society. Advertising & Society Quarterly, 24(3). https://dx.doi.org/10.1353/asr.2023.a911198

Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). Can: Creative adversarial networks, generating" art" by learning about styles and deviating from style norms. International Conference on ComputationalCreativity (ICCC), Atlanta, GA, June 20th-June 22nd, 2017. https://ar5iv.labs.arxiv.org/html/1706.07068.

Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses. The FASEB Journal, 22(2), 338-342. https://doi.org/10.1096/fj.07-9492LSF

Felzmann, H., Fosch-Villaronga, E., Lutz, C., & Tamò-Larrieux, A. (2020). Towards transparency by design for artificial intelligence. Science and Engineering Ethics, 26(6), 3333-3361. https://doi.org/10.1007/s11948-020-00276-4

Ferrara, E. (2023). Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies. Sci, 6(1), 3. https://doi.org/10.3390/sci6010003

Furman, J., & Seamans, R. (2019). AI and the Economy. Innovation policy and the economy, 19(1), 161-191. https://www.journals.uchicago.edu/doi/10.1086/699936

Gama, F., & Magistretti, S. (2023). Artificial intelligence in innovation management: A review of innovation capabilities and a taxonomy of AI applications. Journal of Product Innovation Management, 1-36. https://doi.org/10.1111/jpim.12698

Ganesh, A. D., & Kalpana, P. (2022). Future of artificial intelligence and its influence on supply chain risk management–A systematic review. Computers & Industrial Engineering, 169, 108206. https://doi.org/10.1016/j.cie.2022.108206

García‐Pérez, M. A. (2010). Accuracy and completeness of publication and citation records in the Web of Science, PsycINFO, and Google Scholar: A case study for the computation of h indices in Psychology. Journal of the American society for information science and technology, 61(10), 2070-2085. https://doi.org/10.1002/asi.21372

Gélinas, D., Sadreddin, A., & Vahidov, R. (2022). Artificial intelligence in human resources management: A review and research agenda. Pacific Asia Journal of the Association for Information Systems, 14(6), 1. https://aisel.aisnet.org/pajais/vol14/iss6/1/

Gerlick, J. A., & Liozu, S. M. (2020). Ethical and legal considerations of artificial intelligence and algorithmic decision-making in personalized pricing. Journal of Revenue and Pricing Management, 19, 85-98. https://doi.org/10.1057/s41272-019-00225-2

Gilster, P. (1997). Digital literacy. Wiley Computer Pub.

Greco, C. M., & Tagarelli, A. (2023). Bringing order into the realm of Transformer-based language models for artificial intelligence and law. Artificial Intelligence and Law, 1-148. https://doi.org/10.48550/arXiv.2308.05502

Grover, P., Kar, A. K., & Dwivedi, Y. K. (2022). Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions. Annals of Operations Research, 308(1), 177-213. https://doi.org/10.1007/s10479-020-03683-9

Hacker, P. (2018). Teaching fairness to artificial intelligence: existing and novel strategies against algorithmic discrimination under EU law. Common Market Law Review, 55(4), 1143-1185. https://doi.org/10.54648/cola2018095

Haddaway, N. R., Collins, A. M., Coughlin, D., & Kirk, S. (2015). The role of Google Scholar in evidence reviews and its applicability to grey literature searching. PloS one, 10(9), e0138237. https://doi.org/10.1371/journal.pone.0138237

Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda✰. Technological Forecasting and Social Change, 162, 120392. https://doi.org/10.1016/j.techfore.2020.120392

Higgins, J. P., & Green, S. (2008). Cochrane handbook for systematic reviews of interventions version 5.0. 1. The Cochrane Collaboration.

Heinrichs, B. (2022). Discrimination in the age of artificial intelligence. AI & Society, 37(1), 143-154. https://doi.org/10.1007/s00146-021-01192-2

Hughes, C., Robert, L., Frady, K., & Arroyos, A. (2019). Artificial intelligence, employee engagement, fairness, and job outcomes. In Managing technology and middle-and low-skilled employees (pp. 61-68). Emerald Publishing Limited.

Johansen, N., & Quon, G. (2019). scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data. Genome biology, 20(1), 166. https://doi.org/10.1186/s13059-019-1766-4

Kaaniche, N., Laurent, M., & Belguith, S. (2020). Privacy enhancing technologies for solving the privacy-personalization paradox: Taxonomy and survey. Journal of Network and Computer Applications, 171, 102807. https://doi.org/10.1016/j.jnca.2020.102807

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25. https://doi.org/10.1016/j.bushor.2018.08.004

Katyal, S. K. (2019). Private accountability in the age of artificial intelligence. UCLA Law Review, 66(54), 55-141. https://www.uclalawreview.org/private-accountability-age-algorithm/

Keding, C. (2021). Understanding the interplay of artificial intelligence and strategic management: four decades of research in review. Management Review Quarterly, 71(1), 91-134. https://doi.org/10.1007/s11301-020-00181-x

Krishnamoorthi, S., & Raphael, B. (2022). A review of methodologies for performance evaluation of automated construction processes. Built Environment Project and Asset Management, 12(5), 719-737. https://doi.org/10.1108/BEPAM-03-2021-0059

Lee, M. C., Scheepers, H., Lui, A. K., & Ngai, E. W. (2023). The implementation of artificial intelligence in organizations: A systematic literature review. Information & Management, 60(5), 103816. https://doi.org/10.1016/j.im.2023.103816

Moussa, M. (2015). Monitoring employee behavior through the use of technology and issues of employee privacy in America. Sage Open, 5(2), 2158244015580168. https://doi.org/10.1177/2158244015580168

Namatherdhala, B., Mazher, N., & Sriram, G. K. (2022). Artificial Intelligence in Product Management: Systematic review. International Research Journal of Modernization in Engineering Technology and Science, 4(7), 2914-2917.

Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E., ... & Staab, S. (2020). Bias in data‐driven artificial intelligence systems—An introductory survey. Wires Data Mining and Knowledge Discovery, 10(3), e1356. https://doi.org/10.1002/widm.1356

Pagallo, U. (2018). Vital, Sophia, and Co.—The quest for the legal personhood of robots. Information, 9(9), 230. https://doi.org/10.3390/info9090230

Patel, A., Kethavath, A., Kushwaha, N. L., Naorem, A., Jagadale, M., Sheetal, K. R., & Renjith, P. S. (2023). Review of artificial intelligence and internet of things technologies in land and water management research during 1991–2021: A bibliometric analysis. Engineering Applications of Artificial Intelligence, 123, 106335. https://doi.org/10.1016/j.engappai.2023.106335

Pereira, C., & Ferreira, C. (2015). Identificação de Práticas e Recursos de Gestão do Valor das TI no COBIT 5/Identification of IT Value Management Practices and Resources in COBIT 5. Revista Ibérica de Sistemas e Tecnologias de Informação, (15), 17.

Pietronudo, M. C., Croidieu, G., & Schiavone, F. (2022). A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management. Technological Forecasting and Social Change, 182, 121828. https://doi.org/10.1016/j.techfore.2022.121828

Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192-210. https://doi.org/10.5465/amr.2018.0072

Rajagopal, V., Venkatesan, S. P., & Goh, M. (2017). Decision-making models for supply chain risk mitigation: A review. Computers & Industrial Engineering, 113, 646-682. https://doi.org/10.1016/j.cie.2017.09.043

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson.

Sampson, M., McGowan, J., Cogo, E., Grimshaw, J., Moher, D., & Lefebvre, C. (2009). An evidence-based practice guideline for the peer review of electronic search strategies. Journal of Clinical Epidemiology, 62(9), 944-952. https://www.jclinepi.com/article/S0895-4356(08)00320-X/fulltext

Santos Divino, S. B. (2021). Inteligência Artificial como sujeito de direito: construção e teorização crítica sobre pessoalidade e subjetivação. Revista de Bioética y Derecho, (52), 237-252. https://doi.org/10.1344/rbd2021.52.31503

Sbai, O., Elhoseiny, M., Bordes, A., LeCun, Y., & Couprie, C. (2018). DeSIGN: Design inspiration from generative networks. In L. Leal-Taixé, & S. Roth (eds.), Proceedings of the European Conference on Computer Vision (ECCV) Workshops (pp. 37-44). Springer.

Schmidt, P., Biessmann, F., & Teubner, T. (2020). Transparency and trust in artificial intelligence systems. Journal of Decision Systems, 29(4), 260-278. https://doi.org/10.1080/12460125.2020.1819094

Seele, P., Dierksmeier, C., Hofstetter, R., & Schultz, M. D. (2021). Mapping the ethicality of algorithmic pricing: A review of dynamic and personalized pricing. Journal of Business Ethics, 170, 697-719. https://doi.org/10.1007/s10551-019-04371-w

Taboada, I., Daneshpajouh, A., Toledo, N., & de Vass, T. (2023). Artificial Intelligence Enabled Project Management: A Systematic Literature Review. Applied Sciences, 13(8), 5014. https://doi.org/10.3390/app13085014

Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., Person, K. A., Ceder, G., & Jain, A. (2019). Unsupervised word embeddings capture latent knowledge from materials science literature. Nature, 571(7763), 95-98. https://doi.org/10.1038/s41586-019-1335-8

Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502-517. https://doi.org/10.1016/j.jbusres.2020.09.009

Veiga, R., & Cadete Pires, C. M. P. (2018). Impacto da inteligência artificial nos locais de trabalho. Rede de Investigação sobre Condições de Trabalho, 67-79.

Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404-409. https://doi.org/10.5465/amd.2018.0084

Votto, A. M., Valecha, R., Najafirad, P., & Rao, H. R. (2021). Artificial intelligence in tactical human resource management: A systematic literature review. International Journal of Information Management Data Insights, 1(2), 100047. https://doi.org/10.1016/j.jjimei.2021.100047

Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2021). Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. The International Journal of Human Resource Management, 33(6), 1237-1266. https://doi.org/10.1080/09585192.2020.1871398

Zarsky, T. Z. (2014). Understanding discrimination in the scored society. Washington Law Review, 89(4), 1375.

Published

2024-11-19

How to Cite

Divino, S. (2024). Governance and Compliance Recommendations for Artificial Intelligence in Business Management. Nuevo Derecho, 20(35), 1–17. https://doi.org/10.25057/2500672X.1665

Altmetric

Article metrics
Abstract views
Galley vies
PDF Views
HTML views
Other views
Crossref Cited-by logo