Understanding AI Risks and Mitigation Strategies

AI, short for artificial intelligence, refers to a technology that simulates human intelligence through computers. It can mimic human thought processes and behaviors, achieving various functions such as autonomous decision-making, learning, understanding, and communication. AI has sparked widespread innovation, benefiting many aspects of society and the economy—from business and healthcare to transportation and cybersecurity. AI technology is frequently used to provide information, suggestions, or simplify tasks, thereby creating positive impacts. However, the continuous development of AI technology also brings many risks and challenges.

The factors causing AI risks are multifaceted, primarily covering data, technology, and management. First, AI systems rely on training data, which often suffers from selection bias or poor quality; second, AI technology itself has a series of deficiencies, including lack of interpretability and poor robustness; third, existing management systems in enterprises are increasingly inadequate to adapt to the development of new technologies like AI. In short, AI risks may stem from the data used to train AI systems, the AI systems themselves, their usage, or the interaction between humans and AI systems.

1. Incomplete Data: When AI makes automated decisions, insufficient or substandard data can lead to biased conclusions.

2. Data Poisoning: If the training dataset is mixed with false data, it can deceive the algorithm, resulting in incorrect outputs in automated decision-making.

3. Data Misuse: Technological advancements have expanded the boundaries of user personal information. Internet platform companies can collect user behaviors such as purchases, favorites, and browsing in real-time, possessing rich computational resources and outstanding algorithm capabilities. If companies do not strictly adhere to laws and regulations while processing and using user data with AI, it may harm user rights due to data misuse.

4. AI Control Risks: The core of AI is algorithms that can self-learn and evolve. If there are issues with the algorithms, AI may become uncontrollable, leading to catastrophic consequences. For example, AI-controlled autonomous vehicles may malfunction, causing traffic accidents. To prevent this, AI must be regulated and controlled to ensure they operate as intended.

5. Unemployment Risks: AI can replace human jobs, potentially leading to mass unemployment. For instance, robots can replace humans in factories for repetitive tasks, and autonomous vehicles can replace taxi drivers.

6. Privacy Leak Risks: AI can collect and analyze vast amounts of personal data, which may lead to privacy breaches. For example, smart homes can track household members’ daily behaviors and preferences, while smartphones can collect users’ locations and communication records. To prevent this, we need to strengthen privacy protection to ensure personal data is not misused.

7. Ethical Risks: AI can simulate human thinking and behavior, but they lack human moral judgment and emotions. For instance, AI can assess whether a person is trustworthy, but they cannot understand human feelings and values.

8. Security Risks: AI may be vulnerable to hacking, leading to data breaches and system failures. For example, hackers can attack autonomous vehicles to take control of their operation.

1. Enhance the Development Level of AI Technology. Although AI technology is widely used in the financial industry, its development is not mature, and many risks are caused by the limitations of the technology itself. Therefore, improving the development level of AI technology can significantly reduce risks. This can be approached in several ways: first, increase investment in talent and funds in the AI field to promote rapid technological innovation; second, strengthen collaborative innovation among government, enterprises, and research institutions to accelerate the development of algorithms and AI products applicable to the financial industry; third, enhance international communication and cooperation, as the healthy development of technologies like AI is a consensus among countries. Strengthening exchanges and cooperation can compensate for shortcomings in AI research in various countries, fostering better development of AI technology.

2. Guide Stakeholders in Financial Technology to Establish Correct Risk Perspectives, such as Ethical and Security Views. The impact of AI applications in the financial industry largely depends on the behaviors of the related parties. Guiding financial institutions, fintech service providers, and fintech practitioners to establish risk awareness can significantly reduce risks arising during application. Education on AI ethics and safety should be provided to professionals in the fintech sector to instill the concept of benefiting humanity in their work. Additionally, it is essential to raise awareness of AI risks among financial consumers through social campaigns and community education to enhance their understanding of AI ethics.

3. Accelerate Legislation Processes and Promote the Implementation of AI Risk Standards in the Financial Industry. Current ethical regulations lack enforceability and rely on the moral self-discipline of AI stakeholders, which is clearly unreliable. Only by integrating these into laws and regulations can we fundamentally suppress illegal misuse of AI technology. Currently, national laws on data privacy protection and data security have been introduced, but there are still gaps in laws regarding algorithm ethics, resulting in regulatory loopholes for AI risks. Relevant departments should expedite legislative research. Moreover, financial regulatory agencies should actively research and establish ethical standards and guidelines related to AI in the financial industry. Furthermore, for existing financial industry standards, efforts should be made to promote their implementation, enhance the dissemination and interpretation of industry standards, and establish accountability mechanisms to admonish and publicly denounce companies that violate standards, thus facilitating the concrete implementation of industry standards.

4. Improve the AI Regulatory System in the Financial Industry. In addition to laws, regulations, and industry standards, a robust AI regulatory mechanism is also essential. Without regulation, laws and industry standards are merely empty documents. A complete regulatory system should be established, including industry oversight and self-regulation by enterprises, strictly regulating the entire lifecycle of AI, severely punishing illegal activities during the process, and promoting the healthy development of AI technology in the financial industry. Financial regulatory agencies need to establish AI risk regulatory teams, creating a regulatory mechanism centered on experts, technical personnel, and decision-makers to ensure scientific and effective oversight of AI risks. Financial enterprises should also establish AI risk oversight departments to strictly monitor potential AI risks in their products.

Managing AI risks is no different from managing risks associated with other types of technologies. AI systems present a series of risks, such as potentially amplifying, perpetuating, or exacerbating unfair outcomes; AI systems may exhibit emergent properties or lead to unforeseen consequences for individuals and communities. Trustworthy AI should be effective and reliable, safe and flexible, accountable and transparent, explainable and justifiable, and privacy-focused.

The National Institute of Standards and Technology in the U.S. has released the “Artificial Intelligence Risk Management Framework” aimed at “enhancing the ability to incorporate trustworthiness considerations into the design, development, use, and assessment of AI products, services, and systems.” AI risk management can help organizations enhance their understanding of how the environments in which AI systems are built and deployed interact with and impact individuals, groups, and communities. Using responsible AI can achieve: assisting AI designers, developers, deployers, assessors, and users in critically thinking about the environments and potential or unforeseen negative and positive impacts; leveraging during the design, development, assessment, and use of influential AI systems; preventing, detecting, reducing, and managing AI risks to maximize the benefits of AI while minimizing risks.