Knowledge Check
Answer the following questions to check your understanding of the key players discussed in this module. This activity is for learning and review.
False
Official records often reflect historical inequities and systemic biases. For example, arrest records reflect policing patterns (who gets stopped and arrested) not just who commits crimes. "Objective" data can still encode societal biases.
False
While technical interventions are important, bias in AI is fundamentally a sociotechnical problem. It requires interdisciplinary collaboration—ethicists, domain experts, affected communities, policymakers, and technologists working together. The problem involves decisions about what to measure, who is impacted, and what trade-offs are acceptable.
False
AI systems can become unfair over time even if they started out working well. This happens because:
- The world changes: New populations may use the system who weren't represented in the original training data
- Feedback loops form: The AI's decisions can change the environment it operates in (e.g., a hiring algorithm that favors certain candidates may skew who applies in the future)
- Context shifts: The system may be applied to situations it wasn't designed for
Still not sure? Read more:
Knowledge Check
Emily Sheehy
Created on January 20, 2026
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Transcript
Knowledge Check
Answer the following questions to check your understanding of the key players discussed in this module. This activity is for learning and review.
False
Official records often reflect historical inequities and systemic biases. For example, arrest records reflect policing patterns (who gets stopped and arrested) not just who commits crimes. "Objective" data can still encode societal biases.
False
While technical interventions are important, bias in AI is fundamentally a sociotechnical problem. It requires interdisciplinary collaboration—ethicists, domain experts, affected communities, policymakers, and technologists working together. The problem involves decisions about what to measure, who is impacted, and what trade-offs are acceptable.
False
AI systems can become unfair over time even if they started out working well. This happens because:
- The world changes: New populations may use the system who weren't represented in the original training data
- Feedback loops form: The AI's decisions can change the environment it operates in (e.g., a hiring algorithm that favors certain candidates may skew who applies in the future)
- Context shifts: The system may be applied to situations it wasn't designed for
Still not sure? Read more: