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Students work in small groups to analyze real-world cases of AI bias (e.g., facial recognition systems, hiring algorithms, credit scoring systems). Each group researches their assigned case, identifying the types of bias present, their causes, impacts on different populations, and potential solutions. Groups present their findings and lead a class discussion on mitigation strategies.

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Biased Technology: The Automated Discrimination of Facial Recognition

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Studies show that facial recognition is least reliable for people of color, women, and nonbinary individuals. And that can be life-threatening when the technology is in the hands of law enforcement.

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Students examine sample datasets used for machine learning and identify potential sources of bias. They analyze factors such as data collection methods, representation of different demographics, and hidden variables. Teams then propose methods to make the dataset more inclusive and representative.

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Organize a structured debate where teams argue different perspectives on AI bias issues. Topics might include: 'Should AI systems be required to meet specific fairness metrics before deployment?' or 'Who should be held accountable for AI bias - developers, companies, or regulators?' Students research their positions and present evidence-based arguments.

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Students create a simple flowchart or simulation demonstrating how biased AI decisions can compound over time and affect different communities. They track hypothetical outcomes for different demographic groups through multiple decision points (e.g., loan applications, job recruitment, healthcare access) and discuss the cumulative effects.

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Students learn about different mathematical measures of AI fairness (e.g., demographic parity, equal opportunity, disparate impact). Using provided datasets and simple tools, they calculate these metrics and discuss the trade-offs between different definitions of fairness.

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Working in teams, students develop comprehensive strategies to address bias in a specific AI application. They must consider technical solutions (data collection, algorithm design), organizational policies, and regulatory frameworks. Teams present their strategies and receive peer feedback.

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Students conduct an audit of an existing AI system or service (e.g., image recognition APIs, language models, recommendation systems). They design and run tests to detect potential biases, document their findings, and propose improvements. This can include testing with diverse inputs and analyzing output patterns.

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Students practice inclusive design principles by creating specifications for an AI system that actively considers diverse user needs and potential bias issues from the start. They must consider data collection, algorithm design, user interface, and testing procedures that promote fairness and accessibility.

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Students role-play interviews with different stakeholders affected by AI bias (e.g., users, developers, policymakers, advocacy groups). This helps develop empathy and understanding of various perspectives on AI bias issues. They document insights and use them to inform bias mitigation strategies.

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Teams compete to create the most comprehensive and user-friendly documentation for addressing bias in AI systems. They must include sections on bias detection, testing procedures, mitigation strategies, and monitoring methods. Entries are judged on completeness, clarity, and practicality.

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Students research historical examples of societal bias and discrimination, then analyze how these patterns might be reproduced in AI systems. They create timelines showing the evolution of bias from historical practices to current AI challenges, highlighting the importance of understanding historical context in addressing AI bias.

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Students take on the role of tech journalists, investigating and writing articles about AI bias issues. They conduct research, interview experts (or role-play interviews), and create compelling narratives that explain technical concepts to a general audience while highlighting the social implications of AI bias.