Essential Work Samples for Evaluating AI Failure Recovery Experience Design Skills

AI systems, despite their sophistication, inevitably encounter limitations and failures. The ability to design thoughtful, user-centered experiences for these failure scenarios is becoming a critical skill as AI becomes more embedded in products and services. AI Failure Recovery Experience Design sits at the intersection of user experience design and AI system understanding, requiring practitioners to anticipate potential failure modes and create graceful paths to recovery.

Companies implementing AI solutions often focus heavily on the "happy path" where everything works as intended, while underinvesting in failure scenarios. Yet these moments of system limitation or failure are precisely when users form lasting impressions about a product's reliability and trustworthiness. A well-designed failure recovery experience can transform a potentially frustrating interaction into one that builds confidence in your product and brand.

Evaluating candidates for AI Failure Recovery Experience Design requires assessing both their technical understanding of AI systems and their user experience design capabilities. The ideal candidate combines empathy for user frustration with technical knowledge of AI limitations, creating experiences that maintain trust even when systems fail to perform as expected.

The following work samples are designed to evaluate a candidate's ability to analyze AI failure modes, design appropriate recovery experiences, communicate effectively with users during system limitations, and plan for implementation across products. These exercises simulate real-world challenges that AI experience designers face, providing valuable insights into how candidates approach this complex interdisciplinary work.

Activity #1: Failure Mode Analysis and Recovery Design

This exercise evaluates the candidate's ability to identify potential AI failure modes and design appropriate recovery experiences. It tests their understanding of AI system limitations, user expectations, and their skill in creating experiences that maintain trust even when systems don't perform as expected.

Directions for the Company:

• Provide the candidate with documentation about a specific AI feature in your product (or create a simplified example if you don't have an AI product yet).
• Include information about the AI model's capabilities, limitations, and current user experience.
• Allow 60-90 minutes for this exercise.
• Provide access to basic design tools (digital or analog) for the candidate to sketch their solutions.
• Have a technical team member and a UX team member present to evaluate both aspects of the candidate's work.

Directions for the Candidate:

• Review the provided AI feature documentation.
• Identify at least three potential failure modes or limitation scenarios for this AI feature.
• For each scenario, design a user experience that:

1. Clearly communicates what's happening to the user
2. Provides alternative paths to accomplish their goal
3. Maintains trust in the system despite the limitation

• Create simple wireframes or sketches illustrating your proposed recovery experiences.
• Prepare a brief explanation of your reasoning for each design decision.

Feedback Mechanism:

• After the candidate presents their work, provide feedback on one aspect they handled well (such as their understanding of AI limitations or their user-centered approach).
• Offer one piece of constructive feedback about an area for improvement (perhaps suggesting a failure mode they missed or a way to enhance user communication).
• Give the candidate 15 minutes to revise one of their recovery experiences based on this feedback.

Activity #2: User Communication for AI Uncertainty

This exercise focuses on the candidate's ability to craft clear, honest, and helpful communications when AI systems are uncertain or unable to complete a task. It tests their skill in balancing technical accuracy with user-friendly language and maintaining appropriate user expectations.

Directions for the Company:

• Create a scenario involving an AI feature that has encountered a limitation or is producing results with low confidence.
• Provide context about the user's goal and the specific limitation the AI is experiencing.
• Include information about what alternatives might be available to the user.
• Allow 45-60 minutes for this exercise.
• Have both content design and AI team members available to evaluate the responses.

Directions for the Candidate:

• Review the scenario and understand both the technical limitation and the user's goal.
• Design the following communication elements:

1. In-product messaging explaining the limitation in user-friendly terms
2. Visual indicators of AI uncertainty or limitations (if applicable)
3. Guidance for the user on alternative approaches or next steps
4. Any follow-up communications to rebuild trust or gather feedback

• Consider how these communications might vary based on user expertise level or context.
• Prepare to explain how your communications maintain honesty about AI limitations while preserving user confidence in the overall product.

Feedback Mechanism:

• Provide feedback on the clarity and helpfulness of the candidate's communications.
• Offer one suggestion for improvement, such as making technical concepts more accessible or better setting user expectations.
• Ask the candidate to revise one communication element based on your feedback, giving them 10-15 minutes to make adjustments.

Activity #3: Collaborative Graceful Degradation Design

This exercise evaluates how well candidates can work with cross-functional teams to design AI experiences that degrade gracefully when full functionality isn't possible. It tests their collaboration skills, technical understanding, and ability to balance ideal versus practical solutions.

Directions for the Company:

• Assemble a small panel (2-3 people) representing different functions: engineering, product management, and design.
• Prepare a scenario about an AI feature that needs to work across varying conditions (low connectivity, different device capabilities, varying data quality).
• Provide information about technical constraints and business priorities.
• Allow 60 minutes for this collaborative exercise.
• Ensure panel members are prepared to engage actively but also observe the candidate's approach.

Directions for the Candidate:

• Work with the panel to design a graceful degradation approach for the AI feature.
• Lead a structured discussion covering:

1. Identifying critical vs. nice-to-have functionality
2. Designing user experiences for different capability levels
3. Setting appropriate user expectations at each level
4. Transitions between different states as conditions change

• Document the approach using simple diagrams or notes on a shared surface.
• Ensure all panel members' perspectives are incorporated into the final approach.
• Prepare to present a summary of the collaborative solution.

Feedback Mechanism:

• Have one panel member provide positive feedback on an aspect of the candidate's collaboration or solution design.
• Have another panel member suggest one area for improvement in either the approach or the solution.
• Give the candidate 15 minutes to revise one aspect of the degradation design based on this feedback, consulting with panel members as needed.

Activity #4: AI Failure Recovery Implementation Planning

This exercise tests the candidate's ability to plan for implementing AI failure recovery experiences across a product. It evaluates their strategic thinking, prioritization skills, and understanding of the technical and organizational challenges involved in creating robust AI experiences.

Directions for the Company:

• Provide an overview of a product with multiple AI-powered features (or a simplified version if you don't have such a product).
• Include information about current user pain points related to AI limitations or failures.
• Share relevant constraints (technical, resource, timeline) that would affect implementation.
• Allow 90 minutes for this exercise.
• Have a product leader available to answer questions and evaluate the plan.

Directions for the Candidate:

• Review the product information and identify areas where AI failure recovery experiences could have the highest impact on user trust and satisfaction.
• Create an implementation plan that includes:

1. Prioritized list of failure recovery experiences to implement
2. Key stakeholders and teams needed for each implementation
3. Suggested metrics to evaluate the effectiveness of recovery experiences
4. Timeline and phasing approach for implementation
5. Potential challenges and mitigation strategies

• Consider both quick wins and longer-term structural improvements.
• Prepare to present and defend your prioritization and approach.

Feedback Mechanism:

• Provide feedback on the candidate's prioritization approach and overall strategic thinking.
• Suggest one area where their implementation plan could be strengthened or made more practical.
• Give the candidate 20 minutes to revise their implementation plan based on this feedback, focusing particularly on addressing the improvement area you identified.

Frequently Asked Questions

How long should we allocate for these exercises in our interview process?

Each exercise requires 45-90 minutes to complete, plus time for feedback and revision. We recommend selecting 1-2 exercises most relevant to your needs rather than attempting all four in a single interview day. Consider spreading them across different interview stages or combining a shorter exercise with traditional interviews.

What if we don't have an AI product yet but are hiring for future AI initiatives?

You can adapt these exercises using simplified hypothetical AI features that represent your future direction. Focus on exercises that test fundamental skills like user-centered design thinking and clear communication rather than specific technical implementations. The collaborative exercise (#3) can be particularly valuable in this scenario.

Should we provide real product documentation or create simplified versions?

For confidentiality and time management, we recommend creating simplified versions of your product documentation that highlight the essential elements candidates need to understand. This approach also levels the playing field for candidates who may not be familiar with your specific product.

How should we evaluate candidates who have strong UX backgrounds but limited AI experience?

Look for candidates who demonstrate strong learning orientation and ask insightful questions about AI limitations. Their UX expertise may compensate for technical knowledge gaps if they show they can collaborate effectively with AI engineers and quickly grasp key concepts. Consider pairing Activity #2 (communications) with a technical discussion to assess their ability to translate complex concepts.

What if the candidate proposes solutions that aren't technically feasible?

This provides an excellent opportunity to assess how they respond to constraints. During the feedback portion, gently explain the technical limitations and observe how they adapt their thinking. Strong candidates will pivot quickly and find creative alternatives within the constraints rather than becoming attached to their original solution.

How should we weight technical understanding versus user empathy in our evaluation?

The balance depends on your team's current composition and needs. If you have strong technical AI expertise but lack user-centered design thinking, you might weight empathy and communication skills more heavily. Conversely, if your team has strong design capabilities but struggles with understanding AI limitations, you might prioritize technical understanding.

AI Failure Recovery Experience Design is becoming increasingly critical as AI systems become more prevalent in everyday products and services. By using these work samples, you can identify candidates who not only understand the technical limitations of AI but can also design experiences that maintain user trust even when systems don't perform perfectly. The best candidates will demonstrate a rare combination of technical understanding, user empathy, clear communication, and strategic thinking.

At Yardstick, we're committed to helping companies build exceptional teams through better hiring practices. For more resources to improve your hiring process, check out our AI Job Descriptions, AI Interview Question Generator, and AI Interview Guide Generator.

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