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KURUMSAL YÖNETİŞİM VE YAPAY ZEKA: POTANSİYEL FIRSATLAR VE ZORLUKLAR

Yıl 2024, Sayı: 31, 18 - 32, 01.12.2024
https://doi.org/10.58348/denetisim.1541327

Öz

Bu çalışma, yapay zekanın (YZ) kurumsal yönetişime entegrasyonunun getirdiği fırsatları ve zorlukları incelemeyi amaçlamaktadır. Literatür taraması yöntemiyle yapılan araştırmada, YZ'nin büyük veri işleme, tahmine dayalı analiz ve karar alma süreçlerinde sağladığı avantajlar incelenmiştir. Bulgular, YZ'nin kurumsal yönetişimde karar alma süreçlerini iyileştirdiğini, risk yönetimini güçlendirdiğini, şeffaflığı artırdığını ve mevzuata uyumu kolaylaştırdığını göstermektedir. Ancak, veri gizliliği, algoritmik önyargı ve etik sorumluluklar gibi zorluklar da YZ’nin kullanımıyla birlikte ortaya çıkmaktadır. Sonuç olarak, YZ'nin kurumsal yönetişimde etkin kullanımı için sürekli eğitim, dijital okuryazarlık, şeffaf algoritmalar ve insan denetimi gereklidir. Etik kuralların oluşturulması, veri gizliliği risklerinin azaltılması ve hesap verebilirlik mekanizmalarının güçlendirilmesi, bu teknolojinin güvenli ve verimli bir şekilde entegrasyonuna katkı sağlayacaktır.

Kaynakça

  • Abraham, R., Schneider, J., ve Vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International journal of information management, 49, 424-438.
  • Aguilera, R. V., ve Cuervo-Cazurra, A. (2009). Codes of good governance. Corporate Governance: An International Review, 17(3), 376-387.
  • Akter, S., McCarthy, G., Sajib, S., Michael, K., Dwivedi, Y. K., D’Ambra, J., ve Shen, K. N. (2021). Algorithmic bias in data-driven innovation in the age of AI. International Journal of Information Management, 60, 102387.
  • Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., ve Deveci, M. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion, 96, 156-191.
  • Aldboush, H. H., ve Ferdous, M. (2023). Building trust in fintech: an analysis of ethical and privacy considerations in the intersection of big data, AI, and customer trust. International Journal of Financial Studies, 11(3), 90.
  • Alhitmi, H. K., Mardiah, A., Al-Sulaiti, K. I., ve Abbas, J. (2024). Data security and privacy concerns of AI-driven marketing in the context of economics and business field: an exploration into possible solutions. Cogent Business & Management, 11(1), 2393743.
  • Aljuwaiber, A. (2016). Communities of practice as an initiative for knowledge sharing in business organisations: a literature review. Journal of knowledge management, 20(4), 731-748.
  • Allen, B. A., Juillet, L., Paquet, G., ve Roy, J. (2001). E-Governance & government on-line in Canada: Partnerships, people & prospects. Government information quarterly, 18(2), 93-104.
  • Angwin, J., Larson, J., Mattu, S., ve Kirchner, L. (2016). Machine Bias. ProPublica.
  • Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149-159.
  • Antwi, B. O., Adelakun, B. O., ve Eziefule, A. O. (2024). Transforming Financial Reporting with AI: Enhancing Accuracy and Timeliness. International Journal of Advanced Economics, 6(6), 205-223.
  • Ashta, A., ve Herrmann, H. (2021). Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance. Strategic Change, 30(3), 211-222.
  • Balakrishnan, A. (2024). Leveraging Artificial Intelligence for Enhancing Regulatory Compliance in the Financial Sector. International Journal of Computer Trends and Technology.
  • Banwo, A. (2018). Artificial intelligence and financial services: Regulatory tracking and change management. Journal of Securities Operations & Custody, 10(4), 354-365.
  • Bargavi, R. (2024). 11 AI for Optimal Decision-Making. AI-Driven IoT Systems for Industry 4.0, 185.
  • Bejaković, P., ve Mrnjavac, Ž. (2020). The importance of digital literacy on the labour market. Employee Relations: The International Journal, 42(4), 921-932.
  • Bilal Unver, M., ve Asan, O. (2022). Role of trust in AI-driven healthcare systems: Discussion from the perspective of patient safety. In Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care (Vol. 11, No. 1, pp. 129-134). Sage CA: Los Angeles, CA: SAGE Publications.
  • Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Conference on fairness, accountability and transparency (pp. 149-159). PMLR.
  • Birkstedt, T., Minkkinen, M., Tandon, A., ve Mäntymäki, M. (2023). AI governance: themes, knowledge gaps and future agendas. Internet Research, 33(7), 133-167
  • Brynjolfsson, E., ve McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. W.W. Norton & Company.
  • Brynjolfsson, E., ve McElheran, K. (2016). The Rapid Adoption of Data-Driven Decision-Making. American Economic Review, 106(5), 133-139.
  • Caliskan, A., Bryson, J. J., ve Narayanan, A. (2017). Semantics Derived Automatically from Language Corpora Contain Human-Like Biases. Science, 356(6334), 183-186.
  • Caluwe, L. (2022). The role of the board of directors in governing digital transformation. University of Antwerp.
  • Chowdhury, E. K. (2021). Prospects and challenges of using artificial intelligence in the audit process. The Essentials of Machine Learning in Finance and Accounting, 139-156.
  • Chowdhury, R. H. (2024). AI-driven business analytics for operational efficiency. World Journal of Advanced Engineering Technology and Sciences, 12(2), 535-543.
  • Coco, N., Colapinto, C., ve Finotto, V. (2024). Fostering digital literacy among small and micro‐enterprises: digital transformation as an open and guided innovation process. R&D Management, 54(1), 118-136.
  • Cummings, M. L. (2014). Man vs. Machine or Man + Machine? IEEE Intelligent Systems, 29(5), 62-69.
  • Davenport, T. H., ve Kirby, J. (2016). Just How Smart Are Smart Machines? MIT Sloan Management Review, 57(3), 20-25.
  • De Haes, S., Caluwe, L., Huygh, T., ve Joshi, A. (2020). Governing digital transformation. Management for Professionals.
  • Du, S., ve Xie, C. (2021). Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities. Journal of Business Research, 129, 961-974.
  • Dubei, O. (2024). Artificial Intelligence Impact on Risk Management (based on «COR-Medical» case) (Doctoral dissertation, Private Higher Educational Establishment-Institute “Ukrainian-American Concordia University").
  • Dwork, C., Hardt, M., Pitassi, T., Reingold, O., ve Zemel, R. (2012). Fairness Through Awareness. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, 214-226.
  • Eitel-Porter, R. (2021). Beyond the promise: implementing ethical AI. AI and Ethics, 1(1), 73-80.
  • Falco, G., Shneiderman, B., Badger, J., Carrier, R., Dahbura, A., Danks, D., ve Yeong, Z. K. (2021). Governing AI safety through independent audits. Nature Machine Intelligence, 3(7), 566-571.
  • Floridi, L., Cowls, J., King, T. C., ve Taddeo, M. (2018). How to Design AI for Social Good: Seven Essential Factors. Science and Engineering Ethics, 24(6), 1993-2020.
  • German, E. S. (2017). An investigation of human-model interaction for model-centric decision-making (Doctoral dissertation, Massachusetts Institute of Technology).
  • Golbin, I., Rao, A. S., Hadjarian, A., ve Krittman, D. (2020, December). Responsible AI: a primer for the legal community. In 2020 IEEE international conference on big data (big data) (pp. 2121-2126). IEEE.
  • Gwebu, K. L., Wang, J., & Wang, L. (2018). The role of corporate reputation and crisis response strategies in data breach management. Journal of management information systems, 35(2), 683-714.
  • Han, H., Shiwakoti, R. K., Jarvis, R., Mordi, C., ve Botchie, D. (2023). Accounting and auditing with blockchain technology and artificial Intelligence: A literature review. International Journal of Accounting Information Systems, 48, 100598.
  • Hassan, M., Aziz, L. A. R., ve Andriansyah, Y. (2023). The role artificial intelligence in modern banking: an exploration of AI-driven approaches for enhanced fraud prevention, risk management, and regulatory compliance. Reviews of Contemporary Business Analytics, 6(1), 110-132.
  • Hickman, E., ve Petrin, M. (2021). Trustworthy AI and corporate governance: the EU’s ethics guidelines for trustworthy artificial intelligence from a company law perspective. European Business Organization Law Review, 22, 593-625.
  • Hirt, M., ve Willmott, P. (2014). Strategic Principles for Competing in the Digital Age. McKinsey Quarterly. https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/strategic-principles-for-competing-in-the-digital-age
  • Isley, R. (2022). Algorithmic Bias and Its Implications: How to Maintain Ethics through AI Governance. NYU American Public Policy Review.
  • Jobin, A., Ienca, M., ve Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine Intelligence, 1(9), 389-399.
  • Jordan, M. I., ve Mitchell, T. M. (2015). Machine Learning: Trends, Perspectives, and Prospects. Science, 349(6245), 255-260.
  • Kaggwa, S., Eleogu, T. F., Okonkwo, F., Farayola, O. A., Uwaoma, P. U., ve Akinoso, A. (2024). AI in decision making: transforming business strategies. International Journal of Research and Scientific Innovation, 10(12), 423-444.
  • Kang, Y., Cai, Z., Tan, C. W., Huang, Q., ve Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139-172.
  • Kaur, D., Uslu, S., Rittichier, K. J., ve Durresi, A. (2022). Trustworthy artificial intelligence: a review. ACM computing surveys (CSUR), 55(2), 1-38.
  • Khinvasara, T., Shankar, A., ve Wong, C. (2024). Survey of Artificial Intelligence for Automated Regulatory Compliance Tracking. Journal of Engineering Research and Reports, 26(7), 390-406.
  • Laux, J. (2023). Institutionalised distrust and human oversight of artificial intelligence: towards a democratic design of AI governance under the European Union AI Act. AI & SOCIETY, 1-14.
  • Lechterman, T. M. (2022). The concept of accountability in AI ethics and governance. In The Oxford Handbook of AI Governance. Oxford University Press.
  • Milakovich, M. E. (2012). Digital governance: New technologies for improving public service and participation. Routledge.
  • Minkkinen, M., Laine, J., ve Mäntymäki, M. (2022). Continuous auditing of artificial intelligence: A conceptualization and assessment of tools and frameworks. Digital Society, 1(3), 21.
  • Mir, U., Kar, A. K., ve Gupta, M. P. (2022). AI-enabled digital identity–inputs for stakeholders and policymakers. Journal of Science and Technology Policy Management, 13(3), 514-541.
  • Mitan, J. (2024). Enhancing audit quality through artificial intelligence: an external auditing perspective. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., ve Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2).
  • Nerdrum, S. (2024). Board work in a constantly transforming world: requirements on board members now and in the future. Arcada University of Applied Sciences: International Business Management
  • Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E., ve Staab, S. (2020). Bias in data‐driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356.
  • Odeyemi, O., Okoye, C. C., Ofodile, O. C., Adeoye, O. B., Addy, W. A., ve Ajayi-Nifise, A. O. (2024). Integrating AI with blockchain for enhanced financial services security. Finance & Accounting Research Journal, 6(3), 271-287.
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CORPORATE GOVERNANCE AND ARTIFICIAL INTELLIGENCE: TRANSFORMATIVE POTENTIAL AND POSSIBLE CHALLENGES

Yıl 2024, Sayı: 31, 18 - 32, 01.12.2024
https://doi.org/10.58348/denetisim.1541327

Öz

This study aims to examine the opportunities and challenges of integrating artificial intelligence (AI) into corporate governance. Through a literature review, the study examines the advantages of AI in big data processing, predictive analysis and decision-making processes. The findings show that AI improves decision-making processes in corporate governance, strengthens risk management, increases transparency and facilitates regulatory compliance. However, challenges such as data privacy, algorithmic bias and ethical responsibilities also arise with the use of AI. As a result, the effective use of AI in corporate governance requires continuous training, digital literacy, transparent algorithms and human oversight. Establishing ethical rules, mitigating data privacy risks and strengthening accountability mechanisms will contribute to the safe and efficient integration of this technology.

Kaynakça

  • Abraham, R., Schneider, J., ve Vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International journal of information management, 49, 424-438.
  • Aguilera, R. V., ve Cuervo-Cazurra, A. (2009). Codes of good governance. Corporate Governance: An International Review, 17(3), 376-387.
  • Akter, S., McCarthy, G., Sajib, S., Michael, K., Dwivedi, Y. K., D’Ambra, J., ve Shen, K. N. (2021). Algorithmic bias in data-driven innovation in the age of AI. International Journal of Information Management, 60, 102387.
  • Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., ve Deveci, M. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion, 96, 156-191.
  • Aldboush, H. H., ve Ferdous, M. (2023). Building trust in fintech: an analysis of ethical and privacy considerations in the intersection of big data, AI, and customer trust. International Journal of Financial Studies, 11(3), 90.
  • Alhitmi, H. K., Mardiah, A., Al-Sulaiti, K. I., ve Abbas, J. (2024). Data security and privacy concerns of AI-driven marketing in the context of economics and business field: an exploration into possible solutions. Cogent Business & Management, 11(1), 2393743.
  • Aljuwaiber, A. (2016). Communities of practice as an initiative for knowledge sharing in business organisations: a literature review. Journal of knowledge management, 20(4), 731-748.
  • Allen, B. A., Juillet, L., Paquet, G., ve Roy, J. (2001). E-Governance & government on-line in Canada: Partnerships, people & prospects. Government information quarterly, 18(2), 93-104.
  • Angwin, J., Larson, J., Mattu, S., ve Kirchner, L. (2016). Machine Bias. ProPublica.
  • Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149-159.
  • Antwi, B. O., Adelakun, B. O., ve Eziefule, A. O. (2024). Transforming Financial Reporting with AI: Enhancing Accuracy and Timeliness. International Journal of Advanced Economics, 6(6), 205-223.
  • Ashta, A., ve Herrmann, H. (2021). Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance. Strategic Change, 30(3), 211-222.
  • Balakrishnan, A. (2024). Leveraging Artificial Intelligence for Enhancing Regulatory Compliance in the Financial Sector. International Journal of Computer Trends and Technology.
  • Banwo, A. (2018). Artificial intelligence and financial services: Regulatory tracking and change management. Journal of Securities Operations & Custody, 10(4), 354-365.
  • Bargavi, R. (2024). 11 AI for Optimal Decision-Making. AI-Driven IoT Systems for Industry 4.0, 185.
  • Bejaković, P., ve Mrnjavac, Ž. (2020). The importance of digital literacy on the labour market. Employee Relations: The International Journal, 42(4), 921-932.
  • Bilal Unver, M., ve Asan, O. (2022). Role of trust in AI-driven healthcare systems: Discussion from the perspective of patient safety. In Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care (Vol. 11, No. 1, pp. 129-134). Sage CA: Los Angeles, CA: SAGE Publications.
  • Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Conference on fairness, accountability and transparency (pp. 149-159). PMLR.
  • Birkstedt, T., Minkkinen, M., Tandon, A., ve Mäntymäki, M. (2023). AI governance: themes, knowledge gaps and future agendas. Internet Research, 33(7), 133-167
  • Brynjolfsson, E., ve McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. W.W. Norton & Company.
  • Brynjolfsson, E., ve McElheran, K. (2016). The Rapid Adoption of Data-Driven Decision-Making. American Economic Review, 106(5), 133-139.
  • Caliskan, A., Bryson, J. J., ve Narayanan, A. (2017). Semantics Derived Automatically from Language Corpora Contain Human-Like Biases. Science, 356(6334), 183-186.
  • Caluwe, L. (2022). The role of the board of directors in governing digital transformation. University of Antwerp.
  • Chowdhury, E. K. (2021). Prospects and challenges of using artificial intelligence in the audit process. The Essentials of Machine Learning in Finance and Accounting, 139-156.
  • Chowdhury, R. H. (2024). AI-driven business analytics for operational efficiency. World Journal of Advanced Engineering Technology and Sciences, 12(2), 535-543.
  • Coco, N., Colapinto, C., ve Finotto, V. (2024). Fostering digital literacy among small and micro‐enterprises: digital transformation as an open and guided innovation process. R&D Management, 54(1), 118-136.
  • Cummings, M. L. (2014). Man vs. Machine or Man + Machine? IEEE Intelligent Systems, 29(5), 62-69.
  • Davenport, T. H., ve Kirby, J. (2016). Just How Smart Are Smart Machines? MIT Sloan Management Review, 57(3), 20-25.
  • De Haes, S., Caluwe, L., Huygh, T., ve Joshi, A. (2020). Governing digital transformation. Management for Professionals.
  • Du, S., ve Xie, C. (2021). Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities. Journal of Business Research, 129, 961-974.
  • Dubei, O. (2024). Artificial Intelligence Impact on Risk Management (based on «COR-Medical» case) (Doctoral dissertation, Private Higher Educational Establishment-Institute “Ukrainian-American Concordia University").
  • Dwork, C., Hardt, M., Pitassi, T., Reingold, O., ve Zemel, R. (2012). Fairness Through Awareness. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, 214-226.
  • Eitel-Porter, R. (2021). Beyond the promise: implementing ethical AI. AI and Ethics, 1(1), 73-80.
  • Falco, G., Shneiderman, B., Badger, J., Carrier, R., Dahbura, A., Danks, D., ve Yeong, Z. K. (2021). Governing AI safety through independent audits. Nature Machine Intelligence, 3(7), 566-571.
  • Floridi, L., Cowls, J., King, T. C., ve Taddeo, M. (2018). How to Design AI for Social Good: Seven Essential Factors. Science and Engineering Ethics, 24(6), 1993-2020.
  • German, E. S. (2017). An investigation of human-model interaction for model-centric decision-making (Doctoral dissertation, Massachusetts Institute of Technology).
  • Golbin, I., Rao, A. S., Hadjarian, A., ve Krittman, D. (2020, December). Responsible AI: a primer for the legal community. In 2020 IEEE international conference on big data (big data) (pp. 2121-2126). IEEE.
  • Gwebu, K. L., Wang, J., & Wang, L. (2018). The role of corporate reputation and crisis response strategies in data breach management. Journal of management information systems, 35(2), 683-714.
  • Han, H., Shiwakoti, R. K., Jarvis, R., Mordi, C., ve Botchie, D. (2023). Accounting and auditing with blockchain technology and artificial Intelligence: A literature review. International Journal of Accounting Information Systems, 48, 100598.
  • Hassan, M., Aziz, L. A. R., ve Andriansyah, Y. (2023). The role artificial intelligence in modern banking: an exploration of AI-driven approaches for enhanced fraud prevention, risk management, and regulatory compliance. Reviews of Contemporary Business Analytics, 6(1), 110-132.
  • Hickman, E., ve Petrin, M. (2021). Trustworthy AI and corporate governance: the EU’s ethics guidelines for trustworthy artificial intelligence from a company law perspective. European Business Organization Law Review, 22, 593-625.
  • Hirt, M., ve Willmott, P. (2014). Strategic Principles for Competing in the Digital Age. McKinsey Quarterly. https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/strategic-principles-for-competing-in-the-digital-age
  • Isley, R. (2022). Algorithmic Bias and Its Implications: How to Maintain Ethics through AI Governance. NYU American Public Policy Review.
  • Jobin, A., Ienca, M., ve Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine Intelligence, 1(9), 389-399.
  • Jordan, M. I., ve Mitchell, T. M. (2015). Machine Learning: Trends, Perspectives, and Prospects. Science, 349(6245), 255-260.
  • Kaggwa, S., Eleogu, T. F., Okonkwo, F., Farayola, O. A., Uwaoma, P. U., ve Akinoso, A. (2024). AI in decision making: transforming business strategies. International Journal of Research and Scientific Innovation, 10(12), 423-444.
  • Kang, Y., Cai, Z., Tan, C. W., Huang, Q., ve Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139-172.
  • Kaur, D., Uslu, S., Rittichier, K. J., ve Durresi, A. (2022). Trustworthy artificial intelligence: a review. ACM computing surveys (CSUR), 55(2), 1-38.
  • Khinvasara, T., Shankar, A., ve Wong, C. (2024). Survey of Artificial Intelligence for Automated Regulatory Compliance Tracking. Journal of Engineering Research and Reports, 26(7), 390-406.
  • Laux, J. (2023). Institutionalised distrust and human oversight of artificial intelligence: towards a democratic design of AI governance under the European Union AI Act. AI & SOCIETY, 1-14.
  • Lechterman, T. M. (2022). The concept of accountability in AI ethics and governance. In The Oxford Handbook of AI Governance. Oxford University Press.
  • Milakovich, M. E. (2012). Digital governance: New technologies for improving public service and participation. Routledge.
  • Minkkinen, M., Laine, J., ve Mäntymäki, M. (2022). Continuous auditing of artificial intelligence: A conceptualization and assessment of tools and frameworks. Digital Society, 1(3), 21.
  • Mir, U., Kar, A. K., ve Gupta, M. P. (2022). AI-enabled digital identity–inputs for stakeholders and policymakers. Journal of Science and Technology Policy Management, 13(3), 514-541.
  • Mitan, J. (2024). Enhancing audit quality through artificial intelligence: an external auditing perspective. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., ve Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2).
  • Nerdrum, S. (2024). Board work in a constantly transforming world: requirements on board members now and in the future. Arcada University of Applied Sciences: International Business Management
  • Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E., ve Staab, S. (2020). Bias in data‐driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356.
  • Odeyemi, O., Okoye, C. C., Ofodile, O. C., Adeoye, O. B., Addy, W. A., ve Ajayi-Nifise, A. O. (2024). Integrating AI with blockchain for enhanced financial services security. Finance & Accounting Research Journal, 6(3), 271-287.
  • Oliveira, F., Kakabadse, N., ve Khan, N. (2022). Board engagement with digital technologies: A resource dependence framework. Journal of Business Research, 139, 804-818.
  • Oubari, Z., ve Leontjeva, L. (2024). Maximizing Anti Money Laundering Compliance through AI: Assessing the Obligations and Responsibilities of Financial Institutions under the Proposed EU AI Act.
  • Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.
  • Petersen, C. L., Halter, R., Kotz, D., Loeb, L., Cook, S., Pidgeon, D., ve Batsis, J. A. (2020). Using natural language processing and sentiment analysis to augment traditional user-centered design: development and usability study. JMIR mHealth and uHealth, 8(8), e16862.
  • Rane, N., Choudhary, S., ve Rane, J. (2024). Artificial Intelligence-driven corporate finance: enhancing efficiency and decision-making through machine learning, natural language processing, and robotic process automation in corporate governance and sustainability. Natural Language Processing, and Robotic Process Automation in Corporate Governance and Sustainability, 5(2), 1-22.
  • Ransbotham, S., Kiron, D., Gerbert, P., ve Reeves, M. (2017). Reshaping Business with Artificial Intelligence. MIT Sloan Management Review, 59(1), 1-7.
  • Reddy, S., Allan, S., Coghlan, S., ve Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491-497.
  • Robert, A. (2024). Automating Financial Compliance with Intelligent Process Automation: Designing AI-Powered Control Systems for Regulatory Compliance and Fraud Prevention.
  • Russell, S., ve Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
  • Sarrazin, H., ve Willmott, P. (2016). Adapting your board to the digital age. McKinsey Quarterly, 2, 1-8.
  • Sun, T. Q., ve Medaglia, R. (2019). Mapping the Challenges of Artificial Intelligence in the Public Sector: Evidence from Public Healthcare. Government Information Quarterly, 36(2), 368-383.
  • Tene, O. ve Polonetsky, J. (2013). Big Data for All: Privacy and User Control in the Age of Analytics. Northwestern Journal of Technology and Intellectual Property, 11(5), 239-273.
  • Thuraisingham, B. (2020). Artificial intelligence and data science governance: Roles and responsibilities at the c-level and the board. In 2020 IEEE 21st international conference on information reuse and integration for data science (IRI) (pp. 314-318). IEEE.
  • Tricker, R. B. (2015). Corporate Governance: Principles, Policies, and Practices. Oxford University Press.
  • Usman, M., Moinuddin, M., ve Khan, R. (2024). Unlocking insights: harnessing the power of business intelligence for strategic growth. International Journal of Advanced Engineering Technologies and Innovations, 1(4), 97-117.
  • Verma, S. (2019). Weapons of math destruction: how big data increases inequality and threatens democracy. Vikalpa, 44(2), 97-98.
  • Victor-Mgbachi, T. (2024). Leveraging Artificial Intelligence (AI) Effectively: Managing Risks and Boosting Productivity. IRE Journals. 7(7), 54-69
  • Wall, A. M. (2021). Guidelines for artificial intelligence-driven enterprise compliance management systems. Edinburgh Napier University.
  • Westerman, G., Bonnet, D., ve McAfee, A. (2014). Leading Digital: Turning Technology into Business Transformation. Harvard Business Review Press.
  • Xu, J., Yang, T., Zhuang, S., Li, H., ve Lu, W. (2024). AI-based financial transaction monitoring and fraud prevention with behaviour prediction.
Toplam 78 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Kurumsal Yönetişim
Bölüm Makale
Yazarlar

Münire Tuğba Erdem Aladağ 0009-0000-0229-2359

Yayımlanma Tarihi 1 Aralık 2024
Gönderilme Tarihi 31 Ağustos 2024
Kabul Tarihi 8 Ekim 2024
Yayımlandığı Sayı Yıl 2024 Sayı: 31

Kaynak Göster

APA Erdem Aladağ, M. T. (2024). KURUMSAL YÖNETİŞİM VE YAPAY ZEKA: POTANSİYEL FIRSATLAR VE ZORLUKLAR. Denetişim(31), 18-32. https://doi.org/10.58348/denetisim.1541327

TR Dizin'de yer alan Denetişim dergisi yayımladığı çalışmalarla; alanındaki profesyoneller, akademisyenler ve düzenleyiciler arasında etkili bir iletişim ağı kurarak, etkin bir denetim ve yönetim sistemine ulaşma yolculuğunda önemli mesafelerin kat edilmesine katkı sağlamaktadır.