The Rise of Generative AI
With the rise of artificial intelligence, generative AI has become increasingly popular. Models like ChatGPT have the ability to generate humanlike outcomes within seconds, greatly enhancing productivity and creativity. The potential benefits of generative AI in the global economy are vast, with the banking industry alone expected to see significant revenue impact. However, with great innovation comes risk.
The Looming Financial Crash
In a paper co-authored by Gary Gensler, chairman of the U.S. Securities and Exchange Commission, he warns of a looming financial crash caused by deep learning, a subfield of AI. Gensler believes that the concentration of data and reliance on a few foundational AI models increases the risk of herding behavior and systemic fragility. He calls for diversifying and decentralizing data sources to mitigate these risks.
The Role of Foundation Models
At the heart of generative AI systems are foundation models, which are trained on curated datasets. These models have the ability to transfer learning from one task to another, enabling scalability. However, the homogenization of data and reliance on a few foundation models can lead to inherited biases and flawed determinations.
Addressing Biases and Systemic Risks
To prevent an AI-induced financial crisis, it is crucial to address biases and systemic risks. This can be done by curating diversified and less centralized datasets, safeguarding models against manipulation, and enhancing robustness. Developers should also strive for explainability and transparency in AI determinations and outcomes.
The Need for Strong Policy Frameworks
To reduce the likelihood of an AI-induced financial crisis, proactive measures must be taken to establish and enforce strong policy frameworks for AI governance. These frameworks should consider social and ethical implications from the start and prioritize the creation of trustworthy AI systems.
