Risk Management in AI
- (The Louvre Museum, France - Ching-Fuh Lin)
- Overview
AI risk management is the process of identifying, assessing, and managing the risks associated with using artificial intelligence (AI) technologies:- Goal: To minimize the potential negative impacts of AI while maximizing its benefits.
- Tools and practices: Includes formal AI risk management frameworks, tools for analyzing and assessing risk, and processes for monitoring and responding to changes
- Risks: Includes technical risks like security vulnerabilities and algorithmic bias, and non-technical risks like ethical considerations and regulatory compliance
- Benefits: Can include reducing the incidence of fraud, enhancing customer trust, and saving millions in reputational and market risk
AI risk management is the process of identifying, assessing, and reducing the potential risks of AI technologies. The goal is to minimize negative impacts while maximizing the benefits of AI.
AI risk management involves:
- Identifying risks: This includes technical risks like security vulnerabilities and algorithmic bias, as well as non-technical risks like ethical considerations and regulatory compliance.
- Assessing risks: This can include measuring the probability of an event occurring and the consequences of that event.
- Developing strategies: This includes creating policies and processes to mitigate risks, and ensuring compliance with legal and ethical standards.
- Monitoring and responding: This includes responding to changes in the AI environment, and communicating information about incidents.
Some ways to manage AI risks include:
- Incorporating human rights considerations: This includes establishing processes for evaluating human rights risks, and incorporating them into risk mapping and stakeholder consultations.
- Being aware of laws and regulations: This includes understanding the laws and regulations that apply to the model, based on where it will be deployed and in what sector.
- Considering organizational culture: This includes considering how the organization's culture and risk maturity might affect how the model is used.
- Using contractual and insurance guarantees: This includes ensuring that service-level agreements include parameters around model performance and delivery.
AI risk management focuses specifically on identifying and addressing vulnerabilities and threats to keep AI systems safe from harm. AI governance establishes the frameworks, rules and standards that direct AI research, development and application to ensure safety, fairness and respect for human rights.
[More to come ...]
