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AI Risk Categorization

In: AI and the Boardroom

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  • Rohan Sharma

Abstract

AI offers immense potential to transform businesses, but it also introduces significant risks. How ready is your organization to manage the uncertainties that come with AI adoption? This chapter dives into the risks—from unpredictable AI behavior to ethical dilemmas—that can hinder successful AI deployment if left unmanaged. Operational risks like data quality issues, reliance on external vendors, and the complexity of AI can disrupt business performance. Legal risks add another layer, as evolving regulations complicate liability and compliance. Meanwhile, ethical risks, including biases and lack of transparency, can erode trust with customers and stakeholders. To mitigate these risks, organizations must implement robust governance, embrace transparency, and develop proactive risk management strategies. Start by understanding potential risks in AI projects, build strong governance structures, and ensure ongoing monitoring. The key to responsible AI use is not just in embracing its benefits but also in recognizing and effectively managing its risks. How will you ensure your AI initiatives are not just groundbreaking but also responsibly deployed?

Suggested Citation

  • Rohan Sharma, 2024. "AI Risk Categorization," Springer Books, in: AI and the Boardroom, chapter 0, pages 275-286, Springer.
    • Handle: RePEc:spr:sprchp:979-8-8688-0796-1_22
    DOI: 10.1007/979-8-8688-0796-1_22
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