AI Regulation and Bias Mitigation

Summary:

Prakash Sharma discussed various aspects of research methodology and AI reliability. He emphasized the importance of collaborations and the Chief Artificial Intelligence Officers network, highlighting the significance of sample size and population in building prediction models. Sharma also stressed the need to comprehend the normal curve in data analysis and achieving 99.7% accuracy in models.

He discussed the technical aspects of functional testing, load testing, stability testing, regression testing, and usability testing, emphasizing their significance in ensuring the reliability and performance of algorithms. Furthermore, he highlighted the need for continuous monitoring and control of formulas, as well as the impact of stability testing on algorithm reliability.

Sharma also discussed the complexities of model building, highlighting the practical implications of demand and supply dynamics and the need for transparency and explainability in the process. He emphasized the importance of understanding scatter plots, linear regression, and the limitations of relying solely on AI algorithms without comprehending the underlying data dynamics. Furthermore, he discussed the significance of stability testing, regression testing, and usability testing in ensuring the reliability of models, and the correlation between performance testing and reliability testing. Finally, Sharma led a discussion on the voluntary nature of the forum, encouraging participants to consider future opportunities in AI governance, ethics management, and other domains.

What You’ll Discover:

Discussion on Research Methodology and AI Reliability
Prakash Sharma initiates a discussion on research methodology and AI reliability, outlining the upcoming focus on explaining research methodology and the need for research papers in the next week. He emphasizes the importance of collaborations and the Chief Artificial Intelligence Officers network, and delves into the significance of sample size and population in building prediction models, as well as the normal curve in data analysis.

* Research Methodology for AI
* Collaboration with Organizations
* Building a Community for AI Governance
* Reliability and Robustness of AI Algorithms

Importance of Statistical Accuracy and Reliability in Model Building
Prakash Sharma stresses the need for 99.7% accuracy in models and explains the concept of Six Sigma as mean plus or minus T Sigma. He delves into the significance of hypothesis testing and the impact of biasness on prediction models, cautioning against the manipulation of data to show false improvements.

Importance of Reliability Testing in Machine Learning Algorithms
Prakash Sharma, as an auditor, explains the necessity of understanding the technical aspects of reliability testing in machine learning algorithms, such as load testing, regression testing, and stability testing. He underscores the impact of load testing on the performance of machine learning algorithms and the need for continuous monitoring and control of formulas. Additionally, he discusses the importance of stability testing in ensuring the reliability of algorithms.

Understanding Reliability Testing in Model Building
Prakash Sharma discusses the challenges of model building, emphasizing the need for transparency and explainability in the process. He stresses the importance of understanding scatter plots, linear regression, and the limitations of relying solely on AI algorithms without understanding the underlying data dynamics. Additionally, he explains the significance of stability testing, regression testing, and usability testing in ensuring the reliability of models.

Discussion on Resilience and AI Governance
Prakash Sharma delves into the intricacies of resilience within AI governance, addressing concerns related to continuous monitoring, regulatory compliance, data security, and disaster recovery. He emphasizes the need for models to be dynamic and adaptable to alternative parameters in the face of potential disasters, particularly in health science projects where sensitive data elements must be carefully considered.


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