Essential Work Samples for Evaluating AI Risk Identification and Mitigation Planning Skills

As artificial intelligence systems become increasingly integrated into critical business operations and decision-making processes, the ability to identify and mitigate AI-related risks has become a crucial skill for organizations. Professionals with expertise in AI risk identification and mitigation planning help companies navigate the complex landscape of potential harms, regulatory requirements, and ethical considerations that accompany AI deployment.

Evaluating candidates for roles requiring AI risk management skills presents unique challenges. Traditional interviews often fail to reveal a candidate's practical ability to spot potential issues, develop mitigation strategies, and implement safeguards for AI systems. Without proper assessment, organizations risk hiring individuals who understand AI risk concepts theoretically but lack the practical skills to protect the organization from real-world AI failures.

Work samples provide a window into how candidates approach AI risk scenarios, revealing their analytical thinking, technical knowledge, and problem-solving abilities. By observing candidates tackle realistic challenges, hiring managers can better assess whether they possess the right combination of technical understanding, ethical awareness, and strategic thinking required for effective AI risk management.

The following work samples are designed to evaluate a candidate's proficiency in identifying AI risks across various dimensions—technical, ethical, regulatory, and operational—and their ability to develop practical mitigation strategies. These exercises simulate real-world scenarios that AI risk professionals encounter, providing valuable insights into how candidates would perform in the actual role.

Activity #1: AI System Risk Assessment

This exercise evaluates a candidate's ability to systematically identify potential risks in an AI system across multiple dimensions, including technical failures, ethical concerns, regulatory compliance issues, and operational vulnerabilities. Strong candidates will demonstrate a structured approach to risk identification, technical understanding of AI systems, and awareness of the broader implications of AI deployment.

Directions for the Company:

• Prepare a detailed description of a hypothetical AI system that your organization might deploy (e.g., a customer service chatbot, an automated loan approval system, an employee performance evaluation tool).
• Include information about the system's purpose, the data it uses, how it makes decisions, and its integration with other systems.
• Provide relevant context about your organization's industry, regulatory environment, and user base.
• Allow 45-60 minutes for this exercise.
• Have a technical team member and a business stakeholder present to evaluate the candidate's assessment.

Directions for the Candidate:

• Review the AI system description provided.
• Create a comprehensive risk assessment document that identifies potential risks across the following categories:
• Technical risks (e.g., model drift, adversarial attacks, data quality issues)
• Ethical risks (e.g., bias, fairness concerns, transparency issues)
• Regulatory/compliance risks (e.g., privacy violations, sector-specific regulations)
• Operational risks (e.g., integration failures, performance issues)
• For each identified risk, provide:
• A clear description of the risk
• Potential impact (severity and scope)
• Likelihood of occurrence
• Early warning indicators that might signal the risk is materializing
• Prioritize the top 3-5 risks that require immediate attention.

Feedback Mechanism:

• After the candidate presents their risk assessment, provide feedback on one risk category they analyzed thoroughly and effectively.
• Offer constructive feedback on one area where their risk identification could be improved or expanded.
• Ask the candidate to spend 10 minutes revising their assessment of the area needing improvement, incorporating your feedback.

Activity #2: AI Incident Response Simulation

This exercise tests a candidate's ability to respond effectively to an AI system failure or incident. It evaluates their technical troubleshooting skills, decision-making under pressure, and ability to balance technical, business, and ethical considerations when an AI system produces harmful outcomes.

Directions for the Company:

• Create a scenario describing an AI incident that has just occurred (e.g., a recommendation algorithm promoting harmful content, a facial recognition system showing significant bias, a predictive maintenance system missing critical equipment failures).
• Provide relevant technical details about the system, including its architecture, data sources, and deployment environment.
• Include information about stakeholders affected by the incident and business impact.
• Prepare a set of evolving "updates" to introduce throughout the exercise (e.g., "We've just discovered the model was trained on outdated data").
• Allow 60 minutes for this exercise.
• Have a technical team member and a business stakeholder present to evaluate the candidate's response.

Directions for the Candidate:

• Review the AI incident scenario provided.
• Develop and present an incident response plan that includes:
• Immediate actions to mitigate harm
• Technical investigation steps to identify root causes
• Communication strategy for affected stakeholders
• Decision criteria for whether to take the system offline
• As new information is provided during the exercise, adapt your response plan accordingly.
• Prepare a brief post-incident analysis outlining:
• Likely root causes of the incident
• Recommendations to prevent similar incidents in the future
• Lessons learned for improving AI governance

Feedback Mechanism:

• Provide feedback on the candidate's ability to balance technical investigation with harm reduction.
• Offer one specific suggestion for improving their incident response approach.
• Ask the candidate to spend 10 minutes revising their communication strategy based on your feedback.

Activity #3: AI Risk Mitigation Planning

This exercise evaluates a candidate's ability to develop comprehensive mitigation strategies for identified AI risks. It tests their knowledge of risk controls, governance frameworks, and technical safeguards, as well as their ability to create practical implementation plans that balance risk reduction with business objectives.

Directions for the Company:

• Prepare a document outlining 3-4 significant risks associated with an AI system your organization uses or plans to implement.
• For each risk, provide:
• A detailed description
• Potential impact on users, the business, and other stakeholders
• Current controls (if any)
• Include relevant constraints (e.g., budget limitations, technical infrastructure, regulatory requirements).
• Allow 60-75 minutes for this exercise.
• Have both technical and business stakeholders present to evaluate the mitigation plan.

Directions for the Candidate:

• Review the AI risks document provided.
• Develop a comprehensive mitigation plan that addresses each identified risk.
• For each risk, your plan should include:
• Specific mitigation measures (technical, procedural, and governance)
• Implementation timeline and resource requirements
• Success metrics and monitoring approach
• Residual risk assessment after mitigation
• Consider trade-offs between risk reduction and business impact in your recommendations.
• Prepare a brief presentation of your mitigation plan, highlighting key strategies and implementation considerations.

Feedback Mechanism:

• Provide feedback on the practicality and effectiveness of one mitigation strategy the candidate proposed.
• Offer constructive feedback on one area where their mitigation approach could be strengthened.
• Ask the candidate to spend 15 minutes refining the mitigation strategy for the risk you provided feedback on.

Activity #4: AI Governance Framework Design

This exercise assesses a candidate's ability to design organizational structures, processes, and policies for responsible AI development and deployment. It evaluates their understanding of AI governance best practices, regulatory requirements, and change management considerations necessary for effective implementation.

Directions for the Company:

• Prepare a brief on your organization's current AI development practices, including:
• Types of AI systems being developed or used
• Current approval processes (if any)
• Stakeholders involved in AI decisions
• Regulatory environment and compliance requirements
• Include information about organizational structure and culture.
• Provide any existing governance documentation or policies.
• Allow 90 minutes for this exercise.
• Have representatives from legal/compliance, data science, and business units present.

Directions for the Candidate:

• Review the organizational brief provided.
• Design a comprehensive AI governance framework that includes:
• Roles and responsibilities for AI risk management
• Decision-making processes for AI development and deployment
• Risk assessment protocols at different stages of the AI lifecycle
• Documentation requirements and templates
• Monitoring and audit procedures
• Training and awareness programs
• Create a high-level implementation roadmap with key milestones.
• Prepare a presentation explaining your governance framework and implementation approach.
• Address how your framework balances risk management with innovation and business objectives.

Feedback Mechanism:

• Provide feedback on the comprehensiveness and practicality of the governance framework.
• Offer one specific suggestion for improving the implementation roadmap or change management approach.
• Ask the candidate to spend 15 minutes revising one component of their framework based on your feedback.

Frequently Asked Questions

How should we adapt these exercises for candidates with different levels of experience?

For junior candidates, consider providing more structure and guidance in the exercise instructions. You might focus on their ability to identify risks rather than develop comprehensive mitigation strategies. For senior candidates, increase the complexity of the scenarios and place greater emphasis on strategic thinking, implementation planning, and organizational considerations.

Should we provide real examples from our organization for these exercises?

While using real examples provides authentic context, it may raise confidentiality concerns. A good approach is to create realistic scenarios based on your actual systems but with modified details. Alternatively, you can use anonymized versions of past incidents or risks your organization has encountered.

How do we evaluate candidates who take different approaches to these exercises?

Focus on the reasoning behind their approach rather than expecting a specific "correct" answer. Strong candidates should be able to articulate why they prioritized certain risks or chose particular mitigation strategies. Look for structured thinking, comprehensive risk consideration, practical implementation plans, and adaptability when receiving feedback.

What if our organization doesn't have advanced AI systems yet?

These exercises can still be valuable even if your organization is early in its AI journey. In that case, frame the exercises around AI systems you're considering implementing or use generic examples relevant to your industry. The candidate's approach to identifying and mitigating potential risks before implementation demonstrates foresight that's particularly valuable for organizations just beginning with AI.

How can we ensure these exercises don't take too much of the candidate's time?

Consider breaking the assessment into multiple stages. For example, you might ask candidates to complete the risk identification exercise as a take-home assignment, then conduct the mitigation planning exercise during an in-person interview. You can also scale the scope of each exercise to fit your interview timeline.

Should technical team members be present during these exercises?

Ideally, yes. Having both technical and business stakeholders present ensures you can evaluate the candidate's technical understanding as well as their ability to communicate complex AI risks to non-technical audiences. This multi-perspective evaluation is crucial for roles that bridge technical and business considerations.

As AI systems become more prevalent and powerful, the ability to identify and mitigate associated risks becomes increasingly critical for organizations. By incorporating these work samples into your hiring process, you can more effectively evaluate candidates' practical skills in AI risk management and identify those who will help your organization deploy AI responsibly and safely.

For more resources to improve your hiring process, check out Yardstick's AI Job Descriptions, AI Interview Question Generator, and AI Interview Guide Generator.

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