Artificial Intelligence and the Risk of Discrimination: Frameworks for Ethical AI Development

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As artificial intelligence (AI) becomes a cornerstone of modern industries, concerns about its potential to perpetuate discrimination are growing. A recent study explores the factors contributing to AI bias and provides actionable strategies for organizations to mitigate these risks.

Key Findings:

  • Root Causes of Bias: Historical data and opaque machine learning models are primary sources of AI discrimination, affecting decisions in employment, healthcare, and public policy.

  • The Need for Transparency: Algorithmic transparency and explainability are critical for ensuring accountability in AI systems.

  • Global Governance: International coordination is essential to establish ethical frameworks for AI deployment across borders.

  • Case Study Insights: The iTutor Group incident highlights the risks of biased AI systems, underscoring the importance of regular audits and compliance with anti-discrimination laws.

The study advocates for a multi-faceted approach, including:

  1. Developing ethical AI frameworks from the ground up.
  2. Implementing real-time monitoring systems.
  3. Promoting diverse teams to address bias during AI development.

By integrating fairness and accountability into AI systems, organizations can harness the power of AI responsibly, fostering trust and innovation.

DOI Link: https://dx.doi.org/10.61927/igmin250

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