Handling uncertainty and biases
Uncertainty and biases are inherent in AI systems, including generative AI models. Uncertainty might arise due to various reasons, such as incompleteness or ambiguity in data, inherently unpredictable events, or limitations in the model’s knowledge or training process.
In the travel agent scenario, consider a generative AI system that recommends personalized travel itineraries based on user preferences and historical data. Uncertainty can arise from ambiguous or vague user inputs, incomplete or outdated travel information in the training data, or unforeseen events such as weather disruptions or local conflicts.
To handle uncertainty, developers could consider probabilistic modeling, Bayesian inference, and uncertainty quantification approaches in generative AI systems. These techniques allow the models to yield probabilities or confidence intervals instead of deterministic outputs, update beliefs as new data arrives, and estimate uncertainties...
