Confiance en GPT-4 et la nouvelle génération d'IA: Conférence FinTech NYU Stern 2023

Table of Contents

1. Introduction
2. The Intersection of Trust and AI
   • 2.1 Historical Overview of AI
   • 2.2 Trust Dimensions in AI
3. Governance of AI
   • 3.1 Multi-Stakeholder Consultations
   • 3.2 Social-Technical Solutions
   • 3.3 Risk-Based Approach to Regulation
4. Mitigating Bias in AI
5. Auditing AI Systems
6. The Open Letter and Moratorium on AI
7. The Impact of Generative AI in Finance
   • 7.1 Trading and Systematic Trading
   • 7.2 Commercial Lending and Underwriting
8. Challenges and Considerations in Trusting AI
9. Conclusion

The Intersection of Trust and AI

Artificial Intelligence (AI) has become an integral part of our lives, affecting various sectors and industries. As AI continues to advance, it is crucial to address the issue of trust. Trust in AI can be defined as a willingness to commit to collaborative effort before knowing how the other party will act. However, building trust in AI is a complex process that involves multiple dimensions. This article explores the intersection of trust and AI, discussing its historical overview, trust dimensions in AI, governance, bias mitigation, auditing, and the impact of generative AI in the finance industry.

1. Introduction

AI plays a significant role in our society, impacting various aspects of our lives. However, the issue of trust in AI has become increasingly critical. Building trust in AI requires understanding its history, exploring the dimensions of trust, and implementing effective governance mechanisms. This article delves into the subject matter, offering insights into the intersection of trust and AI.

2. The Intersection of Trust and AI

2.1 Historical Overview of AI

AI has a rich history that dates back over 60 years. It has evolved from attempts to replicate human reasoning and problem-solving to data-driven approaches and, eventually, generative AI. Understanding this historical Context helps contextualize the trust challenges that arise with the development and use of AI technologies.

2.2 Trust Dimensions in AI

Trust in AI encompasses various dimensions that need to be addressed. Privacy, fairness, explainability, robustness, misinformation, value alignment, and deep fakes are among the dimensions that influence trust in AI systems. Moreover, AI's social impact, including inclusion, human labor exploitation, and manipulation of individuals, also plays a role in trust formation. Recognizing and navigating these trust dimensions is crucial in shaping public confidence in AI.

3. Governance of AI

Governance plays a vital role in ensuring the responsible and trustworthy use of AI. It encompasses both regulatory frameworks and internal governance within AI companies. Multi-stakeholder consultations, addressing social, technical, and human-centered solutions, and adopting risk-based approaches are essential elements in effective AI governance. Transparency, accountability, risk assessment, and policy challenges are among the critical factors to consider in developing robust governance mechanisms.

4. Mitigating Bias in AI

Bias is a persistent concern in AI systems. Addressing bias in training data, models, and generated content is crucial to ensure fairness and avoid reinforcing prejudiced or harmful outcomes. While mitigating bias in generative AI trained on uncurated data poses its challenges, focusing on specific applications within an enterprise context allows for more curated and trusted training data. A combination of technical tools, human-centered solutions, and risk assessment is key to effectively mitigating bias in AI.

5. Auditing AI Systems

Auditing AI systems is a critical aspect of ensuring their trustworthiness. The transparency and sharing of information regarding AI development, including the training data and the process used to build the system, are essential. Auditing can be conducted by external parties, internal audits, or through the establishment of frameworks that enable accountability, transparency, and verification. Effective auditing mechanisms contribute to building trust in AI systems.

6. The Open Letter and Moratorium on AI

The open letter and call for a moratorium on AI systems, proposed by some prominent figures, have sparked discussions within the AI community. While the intentions behind the letter are well-meaning, focusing on Upstream regulation and expressing concerns about human-like AI may not address the practical challenges and risks associated with AI technologies. Balancing regulation with innovation, adopting a risk-based approach, and focusing on downstream applications provide a more comprehensive and effective approach to ensuring trust in AI.

7. The Impact of Generative AI in Finance

Generative AI has the potential to revolutionize the finance industry, particularly in areas such as trading, commercial lending, and underwriting. While trading systems already utilize AI, incorporating generative AI poses new challenges and opportunities. The ability of AI to make accurate recommendations, enhance efficiency, objectivity, consistency, and adaptability makes it an attractive tool in finance. However, considerations regarding risk, liabilities, and the integration of human decision-making are crucial to maintain trust in the financial domain.

8. Challenges and Considerations in Trusting AI

Trusting AI systems is not without challenges and considerations. Over-attribution of capabilities, reliance on human-like conversational abilities, and the understandability of complex AI systems pose potential risks and misconceptions. Balancing the need for quality, addressing bias, and ensuring transparency, among other factors, are essential in building trust in AI. As AI continues to advance and impact our lives, addressing these challenges becomes increasingly critical.

9. Conclusion

Trust is a fundamental Pillar in the adoption and acceptance of AI. Building trust in AI involves understanding its historical development, exploring the dimensions of trust, implementing effective governance mechanisms, addressing bias, auditing AI systems, and considering the impact of generative AI in specific industries like finance. By understanding the complexities, challenges, and considerations surrounding trust in AI, we can navigate the path towards responsible and trustworthy AI adoption.

Highlights

• AI has a rich history spanning over 60 years, evolving from attempts to replicate human reasoning to data-driven approaches and generative AI.
• Trust in AI encompasses various dimensions, including privacy, fairness, explainability, robustness, misinformation, value alignment, and deep fakes.
• Effective governance of AI requires multi-stakeholder consultations, social-technical solutions, and a risk-based regulatory approach.
• Mitigating bias in AI involves addressing bias in training data, models, and generated content through a combination of technical tools and human-centered solutions.
• Auditing AI systems ensures transparency, accountability, and verification, contributing to building trust in AI.
• The open letter and calls for a moratorium on AI highlight concerns but may not effectively address the practical challenges and risk mitigation in AI deployment.
• Generative AI has significant potential impact in finance, including trading, commercial lending, and underwriting, but requires careful considerations of risk and liability management.
• Challenges in trusting AI include over-attribution of capabilities, the understandability of complex AI systems, and addressing bias and transparency.
• Building trust in AI involves understanding historical context, exploring dimensions of trust, effective governance mechanisms, bias mitigation, auditing, and considering the specific industry impact.

Frequently Asked Questions

1. What is the historical overview of AI?
2. What are the dimensions of trust in AI?
3. How should the governance of AI be approached?
4. What are the challenges in mitigating bias in AI systems?
5. How can AI systems be audited for transparency and accountability?
6. What is the purpose of the open letter and moratorium on AI?
7. What is the potential impact of generative AI in finance?
8. How can trust in AI be built while addressing challenges and considerations?
9. What are the highlights of this article on the intersection of trust and AI?