Stakeholder-accountability model for artificial intelligence projects

116th Congress (2019-2020). (2020). National Artificial Intelligence Initiative Act of 2020 (H.R. 6216). https://www.congress.gov/bill/116th-congress/house-bill/6216/all-actions Search in Google Scholar

Aggarwal, J., & Kumar, S. (2018). A survey on artificial intelligence. International Journal of Research in Engineering, Science and Management, 1(12), 244-245. https://doi.org/10.31224/osf.io/47a8510.31224/osf.io/47a85 Search in Google Scholar

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In FAccT 2021: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623). Association for Computing Machinery. https://doi.org/10.1145/3442188.344592210.1145/3442188.3445922 Search in Google Scholar

Bonsón, E., Lavorato, D., Lamboglia, R., & Mancini, D. (2021). Artificial intelligence activities and ethical approaches in leading listed companies in the European Union. International Journal of Accounting Information Systems, 43, 100535. https://doi.org/10.1016/j.accinf.2021.10053510.1016/j.accinf.2021.100535 Search in Google Scholar

Bovens, M. (2007). Analysing and assessing accountability: A conceptual framework. European Law Journal, 13(4), 447-468. https://doi.org/10.1111/j.1468-0386.2007.00378.x10.1111/j.1468-0386.2007.00378.x Search in Google Scholar

Bovens, M., Schillemans, T., & Hart, P. T. (2008). Does public accountability work? An assessment tool. Public Administration, 86(1), 225-242. https://doi.org/10.1111/j.1467-9299.2008.00716.x10.1111/j.1467-9299.2008.00716.x Search in Google Scholar

Boyer, M., & Veigl, S. (2015, July 15-17). Privacy preserving video surveillance infrastructure with particular regard to modular video analytics. 6th International Conference on Imaging for Crime Prevention and Detection (ICDP-15), Queen Mary University, London, UK. https://doi.org/10.1049/ic.2015.012010.1049/ic.2015.0120 Search in Google Scholar

Brandsma, G. J. (2014). Quantitative analysis. In M. Bovens, R. E. Goodin, & T. Schillemans (Eds.), The Oxford handbook of public accountability (pp. 143-158). Oxford University Press, https://books.google.pl/books?hl=th&lr=&id=pip8AwAAQBAJ&oi=fnd&pg=PA143&ots=ksisAB5c4P&sig=keACNkGzRMWSOIvEL6DChCcuILI&redir_esc=y#v=onepage&q&f=false Search in Google Scholar

Büchi, M., Fosch-Villaronga, E., Lutz, C., Tamò-Larrieux, A., Velidi, S., & Viljoen, S. (2020). The chilling effects of algorithmic profiling: Mapping the issues. Computer Law & Security Review, 36, 1-15. https://doi.org/10.1016/j.clsr.2019.10536710.1016/j.clsr.2019.105367 Search in Google Scholar

Chasalow, K., & Levy, K. (2021, March 3-10). Representativeness in statistics, politics, and machine learning. In FAccT ‘21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event (pp. 77-89). Association for Computing Machinery. https://doi.org/10.1145/3442188.344587210.1145/3442188.3445872 Search in Google Scholar

Cobbe, J., Lee, M. S. A., & Singh, J. (2021). Reviewable automated decision-making: A framework for accountable algorithmic systems. In FAccT ‘21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 598-609). Association for Computing Machinery. https://doi.org/10.1145/3442188.344592110.1145/3442188.3445921 Search in Google Scholar

Cohen, I. G., Amarasingham, R., Shah, A., Xie, B., & Lo, B. (2014). The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Affairs, 33(7), 1139-1147. https://doi.org/10.1377/hlthaff.2014.004810.1377/hlthaff.2014.0048 Search in Google Scholar

Davis, K. (2017). An empirical investigation into different stakeholder groups perception of project success. International Journal of Project Management, 35(4), 604-617. https://doi.org/10.1016/j.ijproman.2017.02.00410.1016/j.ijproman.2017.02.004 Search in Google Scholar

Derakhshan, R., Turner, R., & Mancini, M. (2019). Project governance and stakeholders: A literature review. International Journal of Project Management, 37(1), 98-116. https://doi.org/10.1016/j.ijproman.2018.10.00710.1016/j.ijproman.2018.10.007 Search in Google Scholar

Derry, R. (2012). Reclaiming marginalized stakeholders. Journal of Business Ethics, 111(2), 253-264. https://doi.org/10.1007/s10551-012-1205-x10.1007/s10551-012-1205-x Search in Google Scholar

Drouin, N., Müller, R., & Sankaran, S. (Eds.). (2013). Novel approaches to organizational project management research: Translational and transformational (Advances in Organization Studies). Copenhagen Business School Press. Search in Google Scholar

Eskerod, P., & Huemann, M. (2013). Sustainable development and project stakeholder management: What standards say. International Journal of Managing Projects in Business, 6(1), 36-50. https://doi.org/10.1108/1753837131129101710.1108/17538371311291017 Search in Google Scholar

Eslami, M., Vaccaro, K., Lee, M. K., On, A. E. B., Gilbert, E., & Karahalios, K. (2019). User attitudes towards algorithmic opacity and transparency in online reviewing platforms. In CHI 2019: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Paper No. 494; pp. 1-14). Association for Computing Machinery. https://doi.org/10.1145/3290605.330072410.1145/3290605.3300724 Search in Google Scholar

European Commission. (2016). General Data Protection Regulation. http://data.europa.eu/eli/reg/2016/679/2016-05-04 Search in Google Scholar

European Commission. (2021). Proposal for a Regulation laying down harmonised rules on artificial intelligence. Artificial Intelligence Act. https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence Search in Google Scholar

Fazelpour, S., & Lipton, Z. C. (2020, February 7-8). Algorithmic fairness from a non-ideal perspective. In AIES ‘20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 57-63). Association for Computing Machinery. https://doi.org/10.1145/3375627.337582810.1145/3375627.3375828 Search in Google Scholar

Foster, A. T. (1988). Artificial intelligence in project management. Cost Engineering, 30(6), 21-24, https://www.proquest.com/docview/220438981?parentSessionId=I8SQEhpH7AcGNcfFU8HssXBnBvL7Xpi51WHxR3MtqCA%3D Search in Google Scholar

Freeman, R. E., & McVea, J. (2001). A stakeholder approach to strategic management (Working Paper, No. 01-02). Darden Graduate School of Business Administration, University of Virginia. https://doi.org/10.2139/ssrn.26351110.2139/ssrn.263511 Search in Google Scholar

Fridgeirsson, T. V., Ingason, H. T., Jonasson, H. I., & Jonsdottir, H. (2021). An authoritative study on the near future effect of artificial intelligence on project management knowledge areas. Sustainability, 13(4), 2345. https://doi.org/10.3390/su1304234510.3390/su13042345 Search in Google Scholar

Green, B., & Chen, Y. (2019). Disparate interactions: An algorithm-in-the-loop analysis of fairness in risk assessments. In FAT* ‘19: Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 90-99). Association for Computing Machinery. https://doi.org/10.1145/3287560.328756310.1145/3287560.3287563 Search in Google Scholar

Hair, J. F. J., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Pearson College Division. Search in Google Scholar

Ika, L. A. (2009). Project success as a topic in project management journals. Project Management Journal, 40(4), 6-19. https://doi.org/10.1002/pmj.2013710.1002/pmj.20137 Search in Google Scholar

Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2017). Big data analytics and computational intelligence for cyber-physical systems: Recent trends and state of the art applications. Future Generation Computer Systems, 105, 766-778. https://doi.org/10.1016/j.future.2017.10.02110.1016/j.future.2017.10.021 Search in Google Scholar

Jacobsson, M., & Hällgren, M. (2016). Impromptu teams in a temporary organization: On their nature and role. International Journal of Project Management, 34(4), 584-596. https://doi.org/10.1016/j.ijproman.2016.02.00110.1016/j.ijproman.2016.02.001 Search in Google Scholar

Jones, T. M. (1991). Ethical decision making by individuals in organizations: An issue-contingent model. Academy of Management Review, 16(2), 366-395. https://doi.org/10.5465/amr.1991.427895810.2307/258867 Search in Google Scholar

Kasinidou, M., Kleanthous, S., Barlas, P., & Otterbacher, J. (2021). I agree with the decision, but they didn’t deserve this: Future developers’ perception of fairness in algorithmic decisions. In FAccT ‘21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 690-700). Association for Computing Machinery. https://doi.org/10.1145/3442188.344593110.1145/3442188.3445931 Search in Google Scholar

Kasy, M., & Abebe, R. (2021). Fairness, equality, and power in algorithmic decision-making. In FAccT ‘21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 576-586). Association for Computing Machinery. https://doi.org/10.1145/3442188.344591910.1145/3442188.3445919 Search in Google Scholar

Kieslich, K., Keller, B., & Starke, C. (2022). Artificial intelligence ethics by design. Evaluating public perception on the importance of ethical design principles of artificial intelligence. Big Data & Society, 9(1). https://doi.org/10.1177/2053951722109295610.1177/20539517221092956 Search in Google Scholar

Di Maddaloni, F., & Davis, K. (2018). Project manager’s perception of the local communities’ stakeholder in megaprojects. An empirical investigation in the UK. International Journal of Project Management, 36(3), 542-565. https://doi.org/10.1016/j.ijproman.2017.11.00310.1016/j.ijproman.2017.11.003 Search in Google Scholar

Manders-Huits, N. (2006). Moral responsibility and IT for human enhancement. In SAC 2006: Proceedings of the 2006 ACM Symposium on Applied Computing (Vol. 1, pp. 267-271). Association for Computing Machinery. https://doi.org/10.1145/1141277.114134010.1145/1141277.1141340 Search in Google Scholar

Martin, K. (2019). Ethical implications and accountability of algorithms. Journal of Business Ethics, 160(4), 835-850. https://doi.org/10.1007/s10551-018-3921-310.1007/s10551-018-3921-3 Search in Google Scholar

McGrath, S. K., & Whitty, S. J. (2018). Accountability and responsibility defined. International Journal of Managing Projects in Business, 11(3), 687-707. https://doi.org/10.1108/IJMPB-06-2017-005810.1108/IJMPB-06-2017-0058 Search in Google Scholar

Miao, Z. (2018). Investigation on human rights ethics in artificial intelligence researches with library literature analysis method. The Electronic Library, 37(5), 914-926. https://doi.org/10.1108/EL-04-2019-008910.1108/EL-04-2019-0089 Search in Google Scholar

Michalczyk, S., Nadj, M., Mädche, A., & Gröger, C. (2021, June 14-16). Demystifying job roles in data science: A text mining approach. Twenty-Ninth European Conference on Information Systems (ECIS 2021), Marrakesh, Morocco|A Virtual AIS Conference, 1622. https://aisel.aisnet.org/ecis2021_rp/115/ Search in Google Scholar

Miller, G. J. (2022a). Artificial intelligence project success factors – beyond the ethical principles. In E. Ziemba & W. Chmielarz (Eds.), FedCSIS-AIST 2021/ISM 2021: Information technology for management: Business and social issues. (Lecture Notes in Business Information Processing; Vol. 442; pp. 65-96). Springer International Publishing. https://doi.org/10.1007/978-3-030-98997-2_410.1007/978-3-030-98997-2_4 Search in Google Scholar

Miller, G. J. (2022b). Stakeholder roles in artificial intelligence projects. Project Leadership and Society, 3, 100068. https://doi.org/10.1016/j.plas.2022.10006810.1016/j.plas.2022.100068 Search in Google Scholar

Mitchell, R. K., Agle, B. R., & Wood, D. J. (1997). Toward a theory of stakeholder identification and salience: Defining the principle of who and what really counts. Academy of Management Review, 22(4), 853-886. https://doi.org/10.5465/amr.1997.971102210510.2307/259247 Search in Google Scholar

Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501-507. https://doi.org/10.1038/s42256-019-0114-410.1038/s42256-019-0114-4 Search in Google Scholar

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group (2010). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. International Journal of Surgery, 8(5), 336-341. https://doi.org/10.1016/j.ijsu.2010.02.00710.1016/j.ijsu.2010.02.007 Search in Google Scholar

Moser, C., den Hond, F., & Lindebaum, D. (2022). Morality in the age of artificially intelligent algorithms. Academy of Management Learning & Education, 21(1), 139-155. https://doi.org/10.5465/amle.2020.028710.5465/amle.2020.0287 Search in Google Scholar

Müller, R., Turner, R., Andersen, E. S., Shao, J., & Kvalnes, Ø. (2014). Ethics, trust, and governance in temporary organizations. Project Management Journal, 45(4), 39-54. https://doi.org/10.1002/pmj.2143210.1002/pmj.21432 Search in Google Scholar

Müller, R., Turner, R. J., Andersen, E. S., Shao, J., & Kvalnes, Ø. (2016). Governance and ethics in temporary organizations: The mediating role of corporate governance. Project Management Journal, 47(6), 7-23. https://eprints.whiterose.ac.uk/161389/10.1177/875697281604700602 Search in Google Scholar

Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167(2), 209-234. https://doi.org/10.1007/s10551-019-04407-110.1007/s10551-019-04407-1 Search in Google Scholar

Nemati, H. R., Todd, D. W., & Brown, P. D. (2002). A hybrid intelligent system to facilitate information system project management activities. Project Management Journal, 33(3), 42-52. https://doi.org/10.1177/87569728020330030610.1177/875697280203300306 Search in Google Scholar

Neumann, T., De-Arteaga, M., & Fazelpour, S. (2022). Justice in misinformation detection systems: An analysis of algorithms, stakeholders, and potential harms. In FAccT ‘22: 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1504-1515). Association for Computing Machinery. https://doi.org/10.1145/3531146.353320510.1145/3531146.3533205 Search in Google Scholar

Nguyen, T. H. D., Chileshe, N., Rameezdeen, R., & Wood, A. (2019). External stakeholder strategic actions in projects: A multi-case study. International Journal of Project Management, 37(1), 176-191. https://doi.org/10.1016/j.ijproman.2018.12.00110.1016/j.ijproman.2018.12.001 Search in Google Scholar

OECD. (2019). Artificial intelligence in society. https://doi.org/10.1787/eedfee77-en10.1787/eedfee77-en Search in Google Scholar

Ong, S., & Uddin, S. (2020). Data science and artificial intelligence in project management: The past, present and future. The Journal of Modern Project Management, 7(4), 04. https://journalmodernpm.com/manuscript/index.php/jmpm/article/view/JMPM02202/376 Search in Google Scholar

Prado, P., & Sapsed, J. (2016). The anthropophagic organization: How innovations transcend the temporary in a project-based organization. Organization Studies, 37(12), 1793-1818. https://doi.org/10.1177/017084061665549110.1177/0170840616655491 Search in Google Scholar

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In FAT* ‘20: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33-44). Association for Computing Machinery. https://doi.org/10.1145/3351095.337287310.1145/3351095.3372873 Search in Google Scholar

Rezania, D., Baker, R., & Nixon, A. (2019). Exploring project managers’ accountability. International Journal of Managing Projects in Business, 12(4), 919-937. https://doi.org/10.1108/IJMPB-03-2018-003710.1108/IJMPB-03-2018-0037 Search in Google Scholar

Rodrigues, R. (2020). Legal and human rights issues of AI: Gaps, challenges and vulnerabilities. Journal of Responsible Technology, 4, 100005. https://doi.org/10.1016/j.jrt.2020.10000510.1016/j.jrt.2020.100005 Search in Google Scholar

Ryan, M., & Stahl, B. C. (2021). Artificial intelligence ethics guidelines for developers and users: Clarifying their content and normative implications. Journal of Information, Communication and Ethics in Society, 19(1), 61-86. https://doi.org/10.1108/JICES-12-2019-013810.1108/JICES-12-2019-0138 Search in Google Scholar

Sambasivan, N., Arnesen, E., Hutchinson, B., Doshi, T., & Prabhakaran, V. (2021, March 3-10). Re-imagining algorithmic fairness in India and beyond. In FAccT ‘21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event (pp. 315-328). Association for Computing Machinery. https://doi.org/10.1145/3442188.344589610.1145/3442188.3445896 Search in Google Scholar

Saurabh, K., Arora, R., Rani, N., Mishra, D., & Ramkumar, M. (2021). AI led ethical digital transformation: Framework, research and managerial implications. Journal of Information, Communication and Ethics in Society, 20(2), 229-256. https://doi.org/10.1108/JICES-02-2021-002010.1108/JICES-02-2021-0020 Search in Google Scholar

De Schepper, S., Dooms, M., & Haezendonck, E. (2014). Stakeholder dynamics and responsibilities in public-private partnerships: A mixed experience. International Journal of Project Management, 32(7), 1210-1222. https://doi.org/10.1016/j.ijproman.2014.01.00610.1016/j.ijproman.2014.01.006 Search in Google Scholar

Scoleze Ferrer, P. S., Araujo Galvão G. D., & Monteiro de Carvalho, M. (2020). Tensions between compliance, internal controls and ethics in the domain of project governance. International Journal of Managing Projects in Business, 13(4), 845-865. https://doi.org/10.1108/IJMPB-07-2019-017110.1108/IJMPB-07-2019-0171 Search in Google Scholar

Shaw, N. P., Stöckel, A., Orr, R. W., Lidbetter, T. F., & Cohen, R. (2018). Towards provably moral AI agents in bottom-up learning frameworks. In AIES 2018: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (pp. 271-277). Association for Computing Machinery. https://doi.org/10.1145/3278721.327872810.1145/3278721.3278728 Search in Google Scholar

Shneiderman, B. (2020). Bridging the gap between ethics and practice: Guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems, 10(4), 1-31. https://doi.org/10.1145/341976410.1145/3419764 Search in Google Scholar

Simon, J. P. (2019). Artificial intelligence: Scope, players, markets and geography. Digital Policy, Regulation and Governance, 21(3), 208-237. https://doi.org/10.1108/DPRG-08-2018-003910.1108/DPRG-08-2018-0039 Search in Google Scholar

Singh, J., Cobbe, J., & Norval, C. (2019). Decision provenance: Harnessing data flow for accountable systems. IEEE Access, 7, 6562-6574. https://doi.org/10.1109/ACCESS.2018.288720110.1109/ACCESS.2018.2887201 Search in Google Scholar

Stapleton, L., Lee, M. H., Qing, D., Wright, M., Chouldechova, A., Holstein, K., Wu, Z. S., & Zhu, H. (2022). Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders. In FAccT ‘22: 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1162-1177). Association for Computing Machinery. https://doi.org/10.1145/3531146.353317710.1145/3531146.3533177 Search in Google Scholar

Turner, R. J., & Zolin, R. (2012). Forecasting success on large projects: Developing reliable scales to predict multiple perspectives by multiple stakeholders over multiple time frames. Project Management Journal, 43(5), 87-99. https://doi.org/10.1002/pmj.2128910.1002/pmj.21289 Search in Google Scholar

Mir, U. B., Sharma, S., Kar, A. K., & Gupta, M. P. (2020). Critical success factors for integrating artificial intelligence and robotics. Digital Policy, Regulation and Governance, 22(4), 307-331. https://doi.org/10.1108/DPRG-03-2020-003210.1108/DPRG-03-2020-0032 Search in Google Scholar

Vesa, M., & Tienari, J. (2020). Artificial intelligence and rationalized unaccountability: Ideology of the elites? Organization, 29(6), 1133-1145. https://doi.org/10.1177/135050842096387210.1177/1350508420963872 Search in Google Scholar

Wang, Q. (2018). A bibliometric model for identifying emerging research topics. Journal of the Association for Information Science and Technology, 69(2), 290-304. https://doi.org/10.1002/asi.2393010.1002/asi.23930 Search in Google Scholar

Webb, H., Koene, A., Patel, M., & Perez Vallejos, E. (2018, July 18-20). Multi-stakeholder dialogue for policy recommendations on algorithmic fairness. In SMSociety ‘18: Proceedings of the 9th International Conference on Social Media and Society (pp. 395-399). Association for Computing Machinery. https://doi.org/10.1145/3217804.321795210.1145/3217804.3217952 Search in Google Scholar

Węgrzyn, J., & Wojewnik-Filipkowska, A. (2022). Stakeholder analysis and their attitude towards PPP success. Sustainability, 14(3), 1570. https://doi.org/10.3390/su1403157010.3390/su14031570 Search in Google Scholar

Wieringa, M. (2020). What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability. In FAT* ‘20: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 1-18). Association for Computing Machinery. https://doi.org/10.1145/3351095.337283310.1145/3351095.3372833 Search in Google Scholar

Willems, L. L., & Vanhoucke, M. (2015). Classification of articles and journals on project control and earned value management. International Journal of Project Management, 33(7), 1610-1634. https://doi.org/10.1016/j.ijproman.2015.06.00310.1016/j.ijproman.2015.06.003 Search in Google Scholar

Zwikael, O., & Meredith, J. R. (2018). Who’s who in the project zoo? The ten core project roles. International Journal of Operations & Production Management, 38(2), 474-492. https://doi.org/10.1108/IJOPM-05-2017-027410.1108/IJOPM-05-2017-0274 Search in Google Scholar