Essential Work Sample Exercises for Evaluating AI Testing Skills

Structured testing for AI systems requires a unique blend of technical expertise, methodical thinking, and creative problem-solving. Unlike traditional software testing, AI testing must account for probabilistic outputs, model drift, data biases, and complex system behaviors that can be difficult to predict. Finding candidates who possess these specialized skills presents a significant challenge for organizations developing AI solutions.

Effective AI testers must understand both the technical underpinnings of machine learning models and the practical implications of how these systems will be used in production. They need to design comprehensive test strategies that evaluate not just functionality, but also reliability, fairness, explainability, and safety. This requires a depth of knowledge that can be difficult to assess through traditional interviews alone.

Work sample exercises provide a window into a candidate's actual capabilities when faced with realistic AI testing scenarios. By observing how candidates approach complex testing challenges, organizations can evaluate their methodical thinking, technical knowledge, and problem-solving abilities in context. This practical demonstration reveals far more about a candidate's potential performance than theoretical discussions or resume credentials.

The following exercises are designed to evaluate a candidate's proficiency in structured testing for AI systems across multiple dimensions. Each activity simulates real-world challenges that AI testers face, from planning comprehensive test strategies to identifying biases in training data. By incorporating these exercises into your interview process, you'll gain valuable insights into which candidates possess the specialized skills needed to effectively test and validate AI systems.

Activity #1: AI Test Strategy Development

This activity evaluates a candidate's ability to develop a comprehensive testing strategy for an AI system. It demonstrates their understanding of AI testing principles, their ability to identify potential risks and failure modes, and their skill in creating a structured approach to validate complex AI functionality. This exercise reveals how candidates think about the unique challenges of AI testing compared to traditional software testing.

Directions for the Company:

• Provide the candidate with a one-page description of a fictional AI system (e.g., a recommendation engine, a natural language processing tool, or a computer vision application).
• Include information about the system's purpose, target users, key features, and technical architecture.
• Allow the candidate 45-60 minutes to develop a test strategy document.
• Prepare questions to discuss their approach and reasoning during a follow-up discussion.
• Resources to provide: System description document, a template for the test strategy (optional), and access to a computer with word processing software.
• Best practice: Choose an AI system similar to what your company works with, but simplified enough to be understood quickly.

Directions for the Candidate:

• Review the provided AI system description thoroughly.
• Develop a comprehensive test strategy document that includes:
• Key testing objectives and scope
• Testing approaches for different aspects of the AI system (model performance, data quality, integration, etc.)
• Potential risks and how you would test for them
• Test data requirements
• Metrics and evaluation criteria
• Testing tools and infrastructure needed
• Be prepared to explain your reasoning and discuss alternative approaches.
• Focus on creating a practical, implementable strategy that addresses the unique challenges of testing AI systems.

Feedback Mechanism:

• After reviewing the test strategy, provide feedback on one strength (e.g., "Your approach to testing for data bias was particularly thorough") and one area for improvement (e.g., "The strategy could benefit from more specific performance metrics").
• Ask the candidate to spend 10 minutes revising a specific section of their strategy based on your feedback.
• Observe how receptive they are to feedback and how effectively they incorporate it into their revised approach.

Activity #2: AI Model Evaluation and Bug Reporting

This exercise assesses a candidate's ability to systematically evaluate an AI model's outputs, identify issues, and document them clearly. It demonstrates their attention to detail, critical thinking skills, and ability to communicate technical problems effectively. This skill is essential for ensuring AI systems meet quality standards before deployment.

Directions for the Company:

• Prepare a dataset and a pre-trained AI model with intentionally introduced issues (e.g., a sentiment analysis model that performs poorly on certain types of text).
• Create a simple interface or notebook environment where the candidate can input test cases and observe the model's outputs.
• Allocate 45-60 minutes for this exercise.
• Resources to provide: Access to the model through a simple interface or notebook, documentation on expected model behavior, a template for bug reporting, and a set of sample inputs.
• Best practice: Include a mix of obvious and subtle issues to test different levels of observation skills.

Directions for the Candidate:

• Explore the provided AI model by inputting various test cases and observing the outputs.
• Systematically test the model's behavior across different input types and edge cases.
• Identify at least 3-5 issues or bugs in the model's performance.
• For each issue found, create a detailed bug report that includes:
• A clear description of the issue
• Steps to reproduce the problem
• Expected vs. actual behavior
• Severity assessment
• Potential impact on users
• If possible, hypothesize about the root cause
• Prioritize the issues based on their severity and impact.

Feedback Mechanism:

• Provide feedback on the quality and thoroughness of their bug reports, highlighting one strength (e.g., "Your detailed reproduction steps would make it easy for a developer to address this issue") and one area for improvement (e.g., "Consider including more context about why this issue matters to end users").
• Ask the candidate to revise one of their bug reports based on your feedback.
• Evaluate their ability to incorporate feedback and improve the clarity and usefulness of their documentation.

Activity #3: Test Case Design for Fairness and Bias Detection

This activity evaluates a candidate's ability to design test cases specifically aimed at detecting bias and fairness issues in AI systems. It demonstrates their understanding of ethical AI principles and their skill in translating these principles into concrete testing approaches. This is increasingly critical as organizations face growing scrutiny over AI fairness and responsible deployment.

Directions for the Company:

• Provide a description of an AI system that makes decisions affecting people (e.g., a loan approval system, hiring tool, or content moderation system).
• Include information about the system's purpose, the data it uses, and the decisions it makes.
• Allow 45-60 minutes for this exercise.
• Resources to provide: System description, demographic information relevant to the application domain, and a template for organizing test cases.
• Best practice: Choose a scenario that has clear potential for bias issues but isn't overly simplistic.

Directions for the Candidate:

• Review the AI system description and identify potential areas where bias or fairness issues might arise.
• Design a comprehensive set of test cases specifically aimed at detecting:
• Demographic biases across different user groups
• Edge cases where the system might perform poorly
• Potential disparate impacts of system decisions
• For each test case, specify:
• The test objective (what bias or fairness issue you're testing for)
• Test inputs and expected outputs
• Evaluation criteria to determine if bias exists
• Data requirements to execute the test
• Organize your test cases in a logical structure, prioritizing those that address the most critical fairness concerns.
• Be prepared to explain your reasoning for each test case and how it relates to responsible AI principles.

Feedback Mechanism:

• Provide feedback on the comprehensiveness and practicality of their test cases, highlighting one strength (e.g., "Your approach to testing intersectional biases was particularly thoughtful") and one area for improvement (e.g., "Consider how you might test for more subtle forms of bias in the system's outputs").
• Ask the candidate to develop 1-2 additional test cases based on your feedback.
• Evaluate their ability to incorporate feedback and their understanding of nuanced fairness considerations.

Activity #4: AI Testing Automation Planning

This exercise assesses a candidate's ability to design an automation strategy for ongoing testing of AI systems. It demonstrates their understanding of CI/CD practices for AI, their knowledge of testing tools and frameworks, and their ability to balance manual and automated testing approaches. This skill is crucial for maintaining AI quality at scale and throughout the model lifecycle.

Directions for the Company:

• Provide a scenario describing an organization that needs to implement automated testing for their AI systems.
• Include details about their current development workflow, release cadence, and the types of AI models they deploy.
• Allow 45-60 minutes for this exercise.
• Resources to provide: Scenario description, a list of commonly used AI testing tools (optional), and a template for the automation plan.
• Best practice: Include specific constraints or requirements that will force the candidate to make thoughtful tradeoffs in their automation strategy.

Directions for the Candidate:

• Review the scenario and identify opportunities for automating different aspects of AI testing.
• Develop a comprehensive automation plan that includes:
• Which testing activities should be automated vs. performed manually
• Recommended tools and frameworks for different testing needs
• Integration points with the existing CI/CD pipeline
• Monitoring and alerting strategies for deployed models
• Approaches for handling model drift and data quality issues
• Resource requirements and implementation timeline
• Consider both the technical and organizational factors that would influence the success of the automation strategy.
• Be prepared to explain the rationale behind your recommendations and discuss alternative approaches.

Feedback Mechanism:

• Provide feedback on the practicality and completeness of their automation plan, highlighting one strength (e.g., "Your approach to automating data quality checks is particularly well-thought-out") and one area for improvement (e.g., "Consider how you might phase the implementation to deliver value incrementally").
• Ask the candidate to revise a specific section of their plan based on your feedback.
• Evaluate their ability to adapt their approach based on new considerations and their understanding of the practical challenges of implementing AI testing automation.

Frequently Asked Questions

How long should we allocate for these work sample exercises?

Each exercise is designed to take 45-60 minutes, plus additional time for feedback and discussion. If you're incorporating multiple exercises into your interview process, consider spreading them across different interview stages or selecting the 1-2 most relevant to your specific needs.

Should candidates be allowed to use external resources during these exercises?

Yes, allowing candidates to consult documentation, look up information, or use reference materials more closely simulates real-world working conditions. However, be clear about what resources are permitted and consider time limits to ensure the exercise remains challenging.

How should we evaluate candidates who approach the exercises differently than expected?

Different approaches can be equally valid in AI testing. Focus on evaluating whether their approach is systematic, thorough, and demonstrates good reasoning—not whether it matches a predetermined solution. Ask candidates to explain their thinking to understand the rationale behind unexpected approaches.

Can these exercises be adapted for remote interviews?

Absolutely. All of these exercises can be conducted remotely using screen sharing, collaborative documents, and video conferencing. For the model evaluation exercise, consider using cloud-based notebooks or providing remote access to the testing environment.

How technical should candidates be to complete these exercises successfully?

These exercises are designed for candidates with technical knowledge of AI systems, but the level can be adjusted. For more technical roles (like ML engineers doing testing), you might emphasize coding and debugging skills. For QA specialists transitioning to AI, you might focus more on test strategy and quality assurance principles.

Should we provide candidates with these exercises in advance?

For complex exercises like the test strategy development, giving candidates the system description in advance can allow for more thoughtful responses. However, the model evaluation exercise might be more effective as an on-the-spot assessment of problem-solving skills. Consider your specific goals for each exercise.

Finding the right talent for structured testing of AI systems is crucial for organizations developing reliable, fair, and robust AI solutions. The work sample exercises outlined above provide a practical way to evaluate candidates' abilities across the key dimensions of AI testing: strategic planning, systematic evaluation, bias detection, and automation implementation.

By incorporating these exercises into your interview process, you'll gain deeper insights into candidates' practical skills than traditional interviews alone can provide. This approach not only helps you identify the most qualified candidates but also demonstrates your organization's commitment to quality and responsible AI development.

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