Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions
This work addresses the need for interpretable and auditable AI models, which is crucial for domains requiring transparency, though it appears incremental as it builds on existing kNN and information theory concepts.
The paper tackles the problem of model interpretability in complex machine learning by reviving k-nearest neighbors (kNN) to overcome historical issues, showing applications across multiple domains without sacrificing interpretability.
Machine learning models have become more and more complex in order to better approximate complex functions. Although fruitful in many domains, the added complexity has come at the cost of model interpretability. The once popular k-nearest neighbors (kNN) approach, which finds and uses the most similar data for reasoning, has received much less attention in recent decades due to numerous problems when compared to other techniques. We show that many of these historical problems with kNN can be overcome, and our contribution has applications not only in machine learning but also in online learning, data synthesis, anomaly detection, model compression, and reinforcement learning, without sacrificing interpretability. We introduce a synthesis between kNN and information theory that we hope will provide a clear path towards models that are innately interpretable and auditable. Through this work we hope to gather interest in combining kNN with information theory as a promising path to fully auditable machine learning and artificial intelligence.