AI Incidents - Crafting a New Playbook for Incident Response

In today's rapidly evolving technological landscape, AI systems are becoming integral to business operations. However, when these systems falter, traditional incident response strategies often fall short. It's imperative to develop a new framework tailored to address AI-specific challenges.

Understanding AI Incidents

Traditional software operates on explicit instructions, making incident responses straightforward. In contrast, AI systems learn from data and make probabilistic decisions, leading to unpredictable outcomes. For instance, in 2015, Google's image recognition AI mistakenly labelled African Americans as gorillas, highlighting the unique challenges AI systems can present.

#ShoryuWill Newsletter #43 By William Zhang

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Defining AI Systems

The first step in crafting a new playbook for AI incident response is clearly defining what constitutes an AI system. Many organisations use AI without fully understanding its scope or boundaries. This ambiguity often leads to confusion about when to apply AI-specific response strategies.

For example, an AI system is typically defined as one that learns from data to make predictions or generate content, such as a machine learning model trained on historical data. In contrast, traditional rule-based systems operate on static logic and are easier to debug.

Why is this important?

• Ambiguity can lead to delays in incident response.
• Over- or underestimating the influence of AI in systems can cause ineffective mitigation strategies.

Real-world relevance: Consider financial institutions deploying AI to detect fraud. Knowing whether the anomalies flagged are the result of AI decision-making or traditional rule-based algorithms determines the response strategy. Misidentifying the source can lead to either wasted resources or unresolved risks.

Identifying Potential Harms

AI systems introduce a variety of risks unique to their design. Unlike software bugs, AI failures can result in social, ethical, and economic harm. For instance:

• In transportation, an autonomous vehicle making flawed decisions could result in accidents.
• In finance, biased training data might lead to discriminatory loan approvals.
• In healthcare, AI diagnostic tools could misdiagnose due to unrepresentative datasets.

Why is this important?

• Businesses must assess industry-specific risks to prioritise resource allocation effectively.
• Misaligned risk assessments can result in overlooked vulnerabilities.

Real-world example: In 2018, an AI hiring tool used by Amazon displayed bias against women applicants because it was trained on historical hiring data that favoured male applicants. Understanding the harm was critical to restructuring the AI system to eliminate gender bias.

Designating Incident Responders

Unlike traditional software failures that IT teams can resolve, AI incidents often require a multidisciplinary approach. The response team should include:

• IT experts to address technical failures.
• Legal advisors to assess compliance and liability.
• Communications specialists to manage public relations during high-profile incidents.
• External consultants with experience in AI incident management.

Why is this important?

• A diverse team ensures that incidents are approached holistically, addressing both technical and non-technical ramifications.
• It prevents bottlenecks caused by over-relying on a single expertise.

Real-world example: When British Airways experienced an AI-related pricing glitch that offered tickets at fractions of the cost, their swift cross-departmental response limited reputational and financial damage by addressing public backlash and technical corrections simultaneously.

Developing Containment Strategies

Swift containment is essential to limit the impact of AI incidents while the root cause is identified. A containment plan involves:

• Modifying system behaviour's to minimise harm temporarily.
• Identifying and isolating affected components to prevent cascading effects.
• Communicating with stakeholders to manage expectations.

Why is this important?

• Containment prevents the escalation of harm, buying time to implement long-term solutions.
• It ensures that broader systems remain operational, minimising business disruptions.

Real-world example: Google's 2015 image recognition issue, where Black individuals were mislabelled as gorillas, could have spiralled into a major PR disaster. The immediate containment strategy? Disabling the system's ability to identify gorillas altogether while addressing the bias in its training data.

Challenges in Identifying AI Incidents

AI incidents can go unnoticed for prolonged periods because they often stem from probabilistic errors rather than explicit code failures. Detection mechanisms must include:

• User feedback channels, such as hotlines or reports.
• Continuous monitoring systems that track anomalies and alert responders.
• Pre-deployment testing, including red-teaming exercises where independent groups simulate attacks or failures.

Why is this important?

• Quick detection can significantly reduce the scope and impact of harm.
• Without proactive mechanisms, businesses risk financial, legal, and reputational damage.

Real-world example: In 2020, a European bank identified discrimination in its AI-based credit scoring system only after customers reported inconsistencies. A user feedback loop could have detected the bias earlier, saving the bank from public backlash.

Post-Incident Actions: Eradication and Recovery

Once an incident is contained, businesses need to focus on eradication and recovery. This involves:

• Root cause analysis: Pinpointing whether issues arose from flawed training data, incorrect model architecture, or operational misuse.
• Rectifying harm: Addressing impacts on customers, stakeholders, and affected communities.
• Long-term fixes: Adjusting data, retraining models, or redesigning AI systems to prevent recurrence.

Why is this important?

• Long-term fixes build trust with customers and stakeholders.
• Skipping these steps leaves organisations vulnerable to repeat failures.

Real-world example: After its AI hiring tool fiasco, Amazon invested in developing neutral datasets and improved training methodologies. This proactive approach restored internal and external trust.

Lessons Learned

Every AI incident offers an opportunity to refine response protocols. Conducting thorough post-mortems ensures continuous improvement. Key steps include:

• Documenting successes and failures in the response process.
• Updating policies based on insights.
• Training teams to implement the revised framework.

Why is this important?

• AI is an evolving field; policies that aren't updated will quickly become obsolete.
• Post-incident reviews create organisational resilience.

Real-world example: When Tesla’s autopilot system faced scrutiny after a high-profile crash, the company analysed the incident thoroughly and updated its AI algorithms to improve situational awareness.

The Path Forward: Why This Matters

Implementing robust AI incident response strategies doesn’t just mitigate risks—it positions businesses as responsible and forward-thinking leaders. Companies that take proactive steps today will not only survive the AI era but thrive in it, gaining trust and market share.

AI is reshaping industries, offering incredible opportunities for growth, efficiency, and innovation. But as we've seen, the risks are real and often unpredictable. A robust AI incident response framework does more than just mitigate risks—it fosters trust, ensures ethical AI deployment, and accelerates innovation.

Businesses that take these steps today will position themselves as leaders in the AI-driven future. By crafting a new playbook for AI incident response, you’re not just preparing for potential problems; you’re creating a foundation for long-term success and resilience.

Final Words

I’ve seen first-hand how AI can elevate businesses when done right and how devastating its failures can be when there’s no plan in place. The good news? You don’t have to wait for something to go wrong. Start now—define your systems, assess your risks, assemble your team, and build your playbook.

Think of your AI response framework as your brakes—not a hindrance, but the very thing that gives you the confidence to move faster, push boundaries, and innovate without fear. The faster the world moves with AI, the more essential those brakes become.

The future belongs to the prepared. Are you ready to lead?

3 Book Recommendations

1. "Human Compatible: Artificial Intelligence and the Problem of Control" by Stuart Russell A thoughtful exploration of how we can design AI systems that align with human values. It’s a must-read for leaders navigating the ethical complexities of AI.
2. "Weapons of Math Destruction" by Cathy O’Neil A deep dive into how biased algorithms impact society and practical steps to address these challenges. Perfect for SMB owners and strategic thinkers.
3. "Artificial Intelligence: A Guide for Thinking Humans" by Melanie Mitchell A straightforward and balanced look at what AI can and cannot do, providing clarity for decision-makers in the private sector.

1-2-3 Punch

Quote:

"An investment in knowledge always pays the best interest." – Benjamin Franklin

Questions:

Are your current systems prepared for AI-related risks?

What steps are you taking today to ensure AI enhances, not harms, your business?

Actions:

Audit your organisation to identify all AI systems currently in use.

Assemble a multidisciplinary team and define their roles in an AI incident response framework.

Conduct a simulation of an AI failure and refine your response strategies based on the outcomes.

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Stay tuned for the next edition, where we’ll explore the governance frameworks that make AI safer, smarter, and a force for good.

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About Me: I'm William Zhang—an engineer, creator, and business strategist with a deep passion for AI technology and digital innovation. As a business owner in engineering consulting, I also focus on helping others with personal development, financial awareness, startup coaching, business strategy, AI implementation, and building effective teams and partnerships. I believe strong relationships and the advancement of technology can create a better future, and I'm excited to share my insights with you.

Your friend, William Zhang