How to Lie with AI: Understanding Bias, Ethics, and the Hidden Risks in ML - Clarissa Rodrigues
The presentation explores how Machine Learning models can exhibit bias and 'lie' unintentionally due to flawed or non-representative training data. While AI is rapidly integrating into daily life (e.g., pricing, search, criminal justice), the speaker emphasizes that developers must maintain vigilance, prioritize explainable models, and ensure that model complexity aligns with problem complexity to mitigate ethical risks and unintended bias.
Key takeaways
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ML Models are not inherently predictable like humans.
2:00
Unlike traditional algorithms where input/output is predictable, ML models can produce varied outputs based on internal weights and parameters. This lack of inherent transparency requires developers to be critically aware of model decisions.
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Bias originates from data, not the algorithm itself (Garbage In, Garbage Out).
8:30
To build a robust model, it is crucial that the training data is representative and free from historical or aggregation biases. Simply having more data does not guarantee accuracy; representativeness is key.
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The importance of Explainable AI (XAI).
3:25
Developers must strive for transparency by using explainable models to understand what the system is doing behind the scenes, rather than relying solely on complex black-box architectures.