AI Regulation and Bias Mitigation

Summary:

Prakash Sharma discussed various aspects of AI regulation and bias mitigation, emphasizing the importance of risk management and assessment in AI planning. He highlighted the complexities of structured and unstructured data and the use of natural language programming for bias identification. Sharma also discussed the dimensions of fairness, accountability, confidentiality, transparency, and safety, as well as the expectations from the NIST and EU AI frameworks. He stressed the importance of trusted and responsible AI across policymakers and the technology community.

Sharma also delved into the complexities of addressing bias in AI development, emphasizing the critical role of supervised and unsupervised learning data sets in identifying and mitigating bias. He stressed the importance of embedding bias checks in the AI lifecycle and the need for continuous monitoring throughout the AI lifecycle. Additionally, he discussed the prescriptive nature of the ISO 42001 standard and the need to interpret and apply frameworks such as EUAX and NIST for effective bias mitigation.

The meeting also covered the importance of accountability and transparency in AI governance, emphasizing the need for stakeholders to exercise discretion when using large language models (LLMs) and to understand the limitations of data accuracy. Sharma highlighted the significance of building a confusion matrix, identifying biases within unstructured data, and involving human decision-making to ensure reliability and ethical practices in AI systems. He also addressed the importance of documenting the implementation of AI code and the inclusion of accountability, transparency, and fairness elements in AI policy documents.

What You’ll Discover:

• Discussion on AI Regulation and Bias Mitigation
• Addressing Bias in AI Development
• Fairness and Accountability in Information Retrieval Systems
• Importance of Accountability and Transparency in AI Governance
• Ethical Implications of Data Collection and Privacy
• Transparency and Privacy Concerns in Technology
• Discussion on Trusted AI and Responsible AI
• Importance of Traceability in Responsible AI
• Discussion on AI Research Papers and Ethical AI Practices
• Discussion on Research Participation and Video Sharing

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