Inductive Models for Artificial Intelligence Systems are Insufficient without Good Explanations
This addresses the issue of interpretability and reliability in AI systems for researchers and practitioners, but it is incremental as it builds on existing critiques without proposing a new solution.
The paper tackles the problem of machine learning models, especially deep neural networks, lacking transparency and explanatory power, arguing that they are insufficient without good explanations to address the 'problem of induction' and improve AI progress.
This paper discusses the limitations of machine learning (ML), particularly deep artificial neural networks (ANNs), which are effective at approximating complex functions but often lack transparency and explanatory power. It highlights the `problem of induction' : the philosophical issue that past observations may not necessarily predict future events, a challenge that ML models face when encountering new, unseen data. The paper argues for the importance of not just making predictions but also providing good explanations, a feature that current models often fail to deliver. It suggests that for AI to progress, we must seek models that offer insights and explanations, not just predictions.