Catastrophic forgetting, also known as catastrophic interference, is a phenomenon in artificial neural networks where the model abruptly loses previously learned information upon learning new tasks or new data. This occurs because training on new data involves adjusting the network's weights, which can overwrite the weights associated with earlier learned tasks. Unlike human learning, where new knowledge typically builds upon existing understanding without erasing it, neural networks can struggle to retain prior information when adapting to new inputs. This issue is particularly problematic for AI systems expected to perform effectively across various contexts without sacrificing their ability to recall previously acquired skills or data. Ethical and legal implications include:
- Bias Amplification: Catastrophic forgetting can unintentionally introduce bias into AI models. If a model forgets older training data that includes underrepresented groups or critical past experiences, it may overemphasize newer data. This imbalance can lead to biased or unethical decision-making outcomes.
- Trust and Accountability: In critical sectors like healthcare, law enforcement, and finance, AI models must retain historical knowledge to ensure consistent and fair decisions. Catastrophic forgetting can undermine trust in these systems, as users may doubt whether all relevant data is being considered.
- Data Retention Regulations: Laws such as the General Data Protection Regulation (GDPR) require the maintenance of data integrity, including the consistent use of personal data. If catastrophic forgetting causes an AI system to improperly handle or disregard previously relevant data, it may inadvertently violate such regulations.
Prevention methods include techniques like continual or lifelong learning enable AI systems to retain prior knowledge while assimilating new information. Methods such as regularization techniques, memory-based approaches, and elastic weight consolidation (EWC) help preserve old knowledge by preventing significant changes to important weights associated with previous tasks. Hyprid modesl can also help. Integrating neural networks with symbolic AI can make systems more resilient to forgetting. These hybrid models combine the adaptable learning capabilities of neural networks with the stable, rule-based reasoning of symbolic AI, helping to maintain previously learned information.
References
Goodfellow, I. et al. (2013). "An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks." Proceedings of the International Conference on Machine Learning.
Kirkpatrick, J. et al. (2017). "Overcoming Catastrophic Forgetting in Neural Networks." Proceedings of the National Academy of Sciences.
