Trustworthy AI in Business Operations: Explainability, Bias Mitigation, and Compliance in AI Systems
DOI:
https://doi.org/10.56830/WRBA03202605Keywords:
Business Operations, Explainability, Bias Mitigation, Artificial Intelligence, Decision MakingAbstract
As organizations increasingly deploy artificial intelligence and machine learning to unlock value, three trustworthy AI frameworks that define how organizations effectively build stakeholder trust are becoming critical: explainability, bias mitigation, and compliance. Global organizations are relying on AI technologies to support their most important business strategies, driving rapid value generation and competitive differentiation. At the same time, a growing priority for stakeholders is to establish trust in AI and ML technologies to reliably deliver on their promise. According to the World Economic Forum’s 2023 Global Technology Governance Report, “Anxiety over AI’s impact on industries, society, and even government has jumped sharply among stakeholders polled this year, with workers, executives, and industry experts.” As a result, over 70% of respondents in the survey cited social media, generative AI, and AI-enhanced robotics as their biggest concerns. In addition to helping reduce legal exposures, compliance with new regulations such as the European Union’s AI Act will also help build stakeholder trust that organizations are deploying AI technologies in a responsible and ethical way. Creating the foundation for trustworthy AI, while prioritizing the technologies, talent, and processes necessary for trustworthy AI deployment, is the first critical strategic approach organizations should take immediately. Building stakeholder trust in AI will be essential for organizations to realize the full value of AI and drive business success in the future.
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