mlsanityData to 7 Oct 2026

Expert System Gradient Descent Style Training: Development of a Defensible Artificial Intelligence Technique

arXiv 2103.04314 ↗First posted 7 Mar 2021cs.LG cs.AIcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 8 of its 59 references that are in the map. First posted on arXiv 7 Mar 2021.

Builds on

8 of 59 references.

References · 8

  1. 2020Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Losscited by 267
  2. 2019Enabling Spike-based Backpropagation for Training Deep Neural Network Architecturescited by 119
  3. 2019Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attackscited by 79
  4. 2019The HSIC Bottleneck: Deep Learning without Back-Propagationcited by 77
  5. 2019Accelerating Federated Learning via Momentum Gradient Descentcited by 75
  6. 2019Adaptively Truncating Backpropagation Through Time to Control Gradient Biascited by 22
  7. 2020Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descentcited by 18
  8. 2018Artificial Intelligence and Legal Liabilitycited by 5

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Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1.

5 papers.

  1. 2023Extension of the Blackboard Architecture with Common Properties and Generic Rulescited by 1
  2. 2021Determining Sentencing Recommendations and Patentability Using a Machine Learning Trained Expert Systemcited by 0
  3. 2021Fake News and Phishing Detection Using a Machine Learning Trained Expert Systemcited by 0
  4. 2022Automating the Design and Development of Gradient Descent Trained Expert System Networkscited by 0
  5. 2024Implementation and Evaluation of a Gradient Descent-Trained Defensible Blackboard Architecture Systemcited by 0