Expert System Gradient Descent Style Training: Development of a Defensible Artificial Intelligence Technique
This addresses the issue of legal liability and sub-optimal decisions in AI systems used for societal applications like loan screening and sentencing, though it appears incremental as it builds on existing expert system concepts.
The paper tackles the problem of AI systems making indefensible decisions due to learning non-causal correlations, by developing a machine learning expert system with meaning-assigned nodes and rules. It evaluates multiple implementations under various conditions, comparing performance to random and fully connected networks, but does not provide concrete numerical results.
Artificial intelligence systems, which are designed with a capability to learn from the data presented to them, are used throughout society. These systems are used to screen loan applicants, make sentencing recommendations for criminal defendants, scan social media posts for disallowed content and more. Because these systems don't assign meaning to their complex learned correlation network, they can learn associations that don't equate to causality, resulting in non-optimal and indefensible decisions being made. In addition to making decisions that are sub-optimal, these systems may create legal liability for their designers and operators by learning correlations that violate anti-discrimination and other laws regarding what factors can be used in different types of decision making. This paper presents the use of a machine learning expert system, which is developed with meaning-assigned nodes (facts) and correlations (rules). Multiple potential implementations are considered and evaluated under different conditions, including different network error and augmentation levels and different training levels. The performance of these systems is compared to random and fully connected networks.