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[White Paper] Enhancing AI Industry Stability and Mitigating Threats: The Necessity of Independent AI Supervision Institutions

Updated: Sep 30

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A white paper on the


Enhancing AI Industry Stability and Mitigating Threats: The Necessity of Independent AI Supervision Institutions


Author: Chang Han, Sungjin (James) Kim, Eun Seok Lee, Hansoo Lee, and Yung Kim


Date: July 8, 2024


Table of Contents


  1. Introduction


As AI technology rapidly advances, it is driving transformative changes across various industries. However, the inherent uncertainty and unreliability of AI systems present significant threats. This white paper proposes the introduction of independent AI supervision institutions to address these issues. By ensuring the stability and reliability of AI technology, we aim to foster the sustainable development of the AI industry.


  1. Issues of Uncertainty and Unreliability in AI Technology


AI systems operate based on complex algorithms and vast datasets. This complexity leads to several critical issues:


  • Algorithmic Opacity: The operation of AI models is often not fully understandable.

  • Data Bias: Biases in training data can be reflected in AI models, resulting in unfair outcomes.

  • Predictive Uncertainty: AI systems may not provide consistent results for the same inputs.

  • Security Vulnerabilities: AI systems can be susceptible to hacking and malicious manipulation.


These problems undermine trust in AI technology and can negatively impact various sectors.


  1. Construction Industry's Contractor-Supervisor Model


In the construction industry, contractors and supervisors operate as independent entities, each with distinct roles and responsibilities. Contractors handle the actual construction work, while supervisors inspect and oversee the quality and safety of the construction process. This model is essential for ensuring the quality and safety of construction projects.


  1. The Need for Independent AI Supervision Institutions


The AI industry can benefit from adopting a similar model. Independent AI supervision institutions can perform the following roles:


  • Evaluation and Verification: Assess the accuracy, fairness, and reliability of AI models and systems.

  • Risk Management: Analyze and propose improvements for the security vulnerabilities of AI systems.

  • Compliance Review: Ensure AI systems comply with relevant laws and regulations.

  • Ethical Review: Evaluate whether AI systems meet ethical standards.


Introducing independent supervision institutions will enhance trust in AI technology and prevent potential risks.


  1. Roles and Responsibilities of AI Supervision Institutions


Independent AI supervision institutions would have the following roles and responsibilities:


  • Periodic Reviews: Conduct periodic reviews of AI systems to continuously monitor and evaluate performance.

  • Transparency Assurance: Transparently disclose the operating principles of the AI supervision method, verification data, and result judgment process.

  • Bias Identification: Check and report bias of training data and algorithms.

  • Security Enhancement: Monitor the security of AI systems and analyze vulnerabilities to suggest ways to strengthen security.

  • Ethical Compliance: Ensure AI systems adhere to ethical standards.


  1. Policy Recommendations


To secure the stability and reliability of AI technology, the following policy recommendations are proposed:


  • Legal Institutionalization: Mandate the establishment and operation of independent AI supervision institutions by law.

  • Standardization: Standardize evaluation and review criteria for consistent supervision.

  • Education and Training: Introduce education and training programs to produce AI supervision experts.

  • Research Support: Provide support for research and development related to AI supervision technologies.


  1. Case Studies for Potential Applications


Case Study 1: AI Model Supervision in the Financial Industry


A financial institution implemented an AI model for customer credit evaluation, but faced issues with bias, resulting in inappropriate credit scores for some customers. In this case, an independent AI supervision institution can review the model, identify the biases, and help to build a more reliable AI credit evaluation system.


Case Study 2: AI Diagnostic System in the Healthcare Industry


A healthcare institution deployed an AI-based diagnostic system, which initially had low diagnostic accuracy, causing issues in patient care. In this case, an independent AI supervision institution can review and provide recommendations to improve the algorithm, enhancing diagnostic accuracy and ensuring patient safety.


  1. Conclusion


The rapid advancement of AI technology, coupled with its inherent uncertainties and various instances of unreliability, poses significant threats. To mitigate these threats, it is necessary to adopt the contractor-supervisor model from the construction industry by introducing independent AI supervision institutions. These institutions will enhance trust and stability in AI technology through regular evaluations, transparency assurances, bias elimination, security enhancements, and ethical compliance.


This white paper highlights the necessity of independent AI supervision institutions to ensure the reliability and sustainability of AI technology. The provided case studies underscore the importance of such institutions in practical scenarios, demonstrating their critical role in the ongoing development and safe deployment of AI systems.



References


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