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<p>Artificial Intelligence (AI) is transforming business operations through predictive analytics, automation, and adaptive decision-making. While offering opportunities for innovation and competitiveness, AI adoption also raises pressing ethical and governance challenges. Key concerns include algorithmic bias, data privacy breaches, and accountability gaps in automated decision-making processes (Binns et al. in Soc Sci Comput Rev 39(4):685–704, 2021. <a href="https://doi.org/10.1177/0894439319865515">https://doi.org/10.1177/0894439319865515</a>). These issues demand robust frameworks to ensure responsible, transparent, and fair AI deployment (European Commission in Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act), 2021). This study examines the dual nature of AI in business—its transformative potential and the ethical risks it poses. Drawing on secondary data and corporate case studies, including Amazon’s biased hiring algorithm, Tesla’s autonomous accountability issues (National Transportation Safety Board in Preliminary report on Tesla Autopilot crash, 2020), and Meta’s content moderation dilemmas (Meta in Approach to AI and data privacy, 2022), the paper analyzes real-world ethical challenges and governance responses. Literature from scholars like Binns et al. (2021), Floridi (Philos Technol 34(2):215–221, 2021. <a href="https://doi.org/10.1007/s13347-020-00408-x">https://doi.org/10.1007/s13347-020-00408-x</a>), and Brynjolfsson is used to contextualize contrasting viewpoints on regulating AI while fostering innovation. The findings highlight the need for a hybrid governance approach, combining internal corporate self-regulation with external oversight inspired by frameworks like the EU’s GDPR (European Commission 2021). The paper offers strategic insights for integrating ethical values into AI systems, ensuring businesses can innovate responsibly while maintaining public trust and compliance.</p>

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AI ethics and governance in business management: challenges, opportunities, and a comparative analysis

  • 期刊论文

  • 发表日期：

    2025年8月28日

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  • Trusha Panchal,
  • Devarsh Panchal

摘要

Artificial Intelligence (AI) is transforming business operations through predictive analytics, automation, and adaptive decision-making. While offering opportunities for innovation and competitiveness, AI adoption also raises pressing ethical and governance challenges. Key concerns include algorithmic bias, data privacy breaches, and accountability gaps in automated decision-making processes (Binns et al. in Soc Sci Comput Rev 39(4):685–704, 2021. https://doi.org/10.1177/0894439319865515). These issues demand robust frameworks to ensure responsible, transparent, and fair AI deployment (European Commission in Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act), 2021). This study examines the dual nature of AI in business—its transformative potential and the ethical risks it poses. Drawing on secondary data and corporate case studies, including Amazon’s biased hiring algorithm, Tesla’s autonomous accountability issues (National Transportation Safety Board in Preliminary report on Tesla Autopilot crash, 2020), and Meta’s content moderation dilemmas (Meta in Approach to AI and data privacy, 2022), the paper analyzes real-world ethical challenges and governance responses. Literature from scholars like Binns et al. (2021), Floridi (Philos Technol 34(2):215–221, 2021. https://doi.org/10.1007/s13347-020-00408-x), and Brynjolfsson is used to contextualize contrasting viewpoints on regulating AI while fostering innovation. The findings highlight the need for a hybrid governance approach, combining internal corporate self-regulation with external oversight inspired by frameworks like the EU’s GDPR (European Commission 2021). The paper offers strategic insights for integrating ethical values into AI systems, ensuring businesses can innovate responsibly while maintaining public trust and compliance.