Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know' (2505.04950v3)

Abstract: Despite AI's impressive achievements, including recent advances in generative and LLMs, there remains a significant gap in the ability of AI systems to handle uncertainty and generalize beyond their training data. AI models consistently fail to make robust enough predictions when facing unfamiliar or adversarial data. Traditional machine learning approaches struggle to address this issue, due to an overemphasis on data fitting, while current uncertainty quantification approaches suffer from serious limitations. This position paper posits a paradigm shift towards epistemic artificial intelligence, emphasizing the need for models to learn from what they know while at the same time acknowledging their ignorance, using the mathematics of second-order uncertainty measures. This approach, which leverages the expressive power of such measures to efficiently manage uncertainty, offers an effective way to improve the resilience and robustness of AI systems, allowing them to better handle unpredictable real-world environments.

Epistemic Artificial Intelligence is Essential for Machine Learning Models to 'Know When They Do Not Know'

The paper "Position: Epistemic Artificial Intelligence is Essential for Machine Learning Models to 'Know When They Do Not Know'" by Shireen Kudukkil Manchingal and Fabio Cuzzolin presents a compelling argument for a paradigm shift in AI towards epistemic artificial intelligence. Despite remarkable advancements in AI, such as generative models and LLMs, current AI systems face challenges in dealing with uncertainty, particularly in making robust predictions with unfamiliar or adversarial data. This position paper advocates for AI models that recognize and manage their ignorance, enhancing resilience and robustness.

Limitations of Current ML Systems

The paper identifies key limitations in existing machine learning systems, which demonstrate overconfidence despite uncertainties. Neural networks often provide inaccurate predictions for out-of-distribution (OOD) samples and adversarial data. Efforts such as overfitting mitigation and domain adaptation have improved some aspects but do not sufficiently address robustness.

Importance of Uncertainty Estimation

Accurate uncertainty estimation enhances AI reliability, especially in safety-critical areas like autonomous driving and medical diagnosis. Distinguishing between aleatoric (irreducible random uncertainty) and epistemic (reducing ignorance through additional data) uncertainty is crucial. Epistemic uncertainty, which arises from insufficient data quantity or quality, represents an opportunity for AI models to improve.

Epistemic AI Approach

Epistemic AI proposes learning from ignorance, emphasizing readiness for unseen data. By prioritizing the recognition of unknowns over observed data, epistemic models guide decision-making through uncertainty measures, preventing catastrophic forgetting and improving adaptability. Models predicting uncertainty measures like credal sets offer a comprehensive view of epistemic knowledge.

Advantages of Epistemic AI

• Explicit Representation: Epistemic AI introduces set-based methods to represent ignorance, enhancing uncertainty management compared to other approaches.
• Mission-Critical Applications: In autonomous vehicles and climate change predictions, epistemic models can guide cautious decision-making, improving safety and adapting to long-term uncertainties.
• Reduction of Bias: LLMs learning epistemically would minimize biases by amplifying training data observations rather than merely replicating them.

Implications and Future Directions

Epistemic AI holds significant promise for improving AI's adaptability and robustness across diverse scenarios. The need for scalable solutions to address large datasets and advanced architectures such as vision transformers and foundation models remains. Further exploration into epistemic AI's application in generative AI and large-scale AI tasks, including uncharted avenues in AI4Science, is anticipated.

The paper's insights on acknowledging and managing ignorance pave the way for future advancements in AI, guiding towards more resilient, adaptive, and reliable machine learning models capable of thriving in uncertain environments.