Bias in Data and Algorithms
Students will investigate how biases in data collection and algorithmic design can lead to unfair or discriminatory outcomes.
Key Questions
- Critique examples of biased algorithms and their real-world consequences.
- Explain how unconscious human biases can be embedded into data and AI systems.
- Design strategies to mitigate bias in data collection and algorithmic development.
ACARA Content Descriptions
Suggested Methodologies
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