How Organizations Need to Respond to AI Incidents 

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GCG Leadership Development Team

As artificial intelligence (AI) continues to evolve, organizations must adopt new strategies to manage AI incidents. With insights gathered from Luminos.Law, experts in AI liabilities, this blog post seeks to address incident response for AI systems. We will discuss AI incident preparedness, identification, and response by exploring containment, eradication, and recovery.

AI Incident Response vs. Traditional Software Incident Response

Traditional software systems’ incident response has become a mature field over the last two decades with a widely accepted playbook. However, AI incidents demand a different approach and playbook. Incident response frameworks for traditional software systems don’t suffice in tackling AI incidents, which raises the need to develop new strategies for AI incident response.

Preparing for AI Incident Response

To properly address and manage AI incidents, organizations must develop and adopt AI incident response policies with these components:

  1. Create a definition of AI: An accurate definition of AI helps distinguish between AI incidents and traditional software incidents.
  2. Identify the most relevant harms: Understanding potential harms stemming from AI incidents allows organizations to prioritize their response strategies.
  3. Designate incident responders: Assemble a team with diverse expertise, such as IT, cybersecurity, communications, legal, business units, and domain experts, along with external resources.
  4. Develop a short-term containment plan: Generate high-level instructions on how to modify AI systems to limit any potential harm an incident may cause. Ensure that the plan is in place before the deployment of an AI model.

Identifying AI Incidents Quickly

AI incidents can happen without the involvement of malicious actors. To swiftly detect AI incidents, organizations should use tools and approaches like:

  1. Appeal and override systems: Allow users to report issues, concerns, and undesirable outputs through user-friendly channels.
  2. Model-monitoring systems: Configure alerts for anomalous or problematic behavior to ensure AI security.
  3. Pre-deployment testing: Perform tests like “red teaming,” where an independent group attempts to expose vulnerabilities in the AI system.

Containment, Eradication, and Recovery

Once an AI incident has been identified, organizations must first contain the incident to prevent further harm. Then, they must eradicate the source of the problem and, finally, focus on recovering and refining their AI systems. Critical assessment questions include:

  • Who is being harmed?
  • What are the options to modify the AI system’s behavior?
  • What is causing the harm?
  • Can existing harms be addressed or rectified in any way?

After the response activities are concluded, organizations should carry out a post-mortem review to learn from each incident. Documentation of lessons learned, seeking feedback, and acting on it can lead to continuous improvement in AI incident response.

In Summary

Although implementing AI incident response strategies might seem complex and resource-intensive, it is crucial to accelerate AI adoption. Like brakes in a car, knowing how to respond when things go wrong allows organizations to confidently move forward with AI technology. By leveraging insights from Luminos.Law, experts in AI liabilities, and adopting appropriate policies and strategies, organizations can better navigate the ever-evolving landscape of AI incidents.

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