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Building Trust in ML/AI by Communicating Uncertainty
- Communicating the limitations of ML/AI systems is crucial for building trust and support from non-specialists.
- Clearly defined use cases and communication of uncertainty levels in ML/AI applications are key for better handling of results and avoiding backlash.
- Tools like simple scales, confidence intervals, and explaining drivers of uncertainty help in effectively communicating uncertainty to users.
- Understanding uncertainty drivers like epistemic and aleatoric uncertainties is important for conveying uncertainty levels in models.
- Various techniques like model performance statistics, Bayesian Neural Networks, and Conformal Prediction can be used to estimate uncertainty in ML models.
- Open source libraries such as TensorFlow Probability and Pyro offer tools for applying uncertainty estimation techniques in ML/AI.
- Alternative methods like Monte Carlo Dropout and Deep Ensemble can be used for approximating uncertainty in deep learning models.
- Generative AI presents new challenges for uncertainty communication due to its complexity and proprietary nature, requiring techniques like expressing certainty levels and leveraging multiple models.
- Techniques such as Perplexity, multi-token entropy, and adapting classic neural network uncertainty methods can help assess uncertainty in generative AI models.
- Using open source models to estimate uncertainty of proprietary models can bridge the gap and enable the application of advanced uncertainty estimation techniques.
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