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

arXiv: https://arxiv.org/abs/2103.04314 · HTML: https://mlsanity.com/paper/2103.04314/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 59 references that are in the map.
- First posted on arXiv 7 Mar 2021.

## Builds on (most cited first, 8 of 8)

- [Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss](https://mlsanity.com/paper/2002.04486/) (2020)
- [Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures](https://mlsanity.com/paper/1903.06379/) (2019)
- [Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks](https://mlsanity.com/paper/1912.12716/) (2019)
- [The HSIC Bottleneck: Deep Learning without Back-Propagation](https://mlsanity.com/paper/1908.01580/) (2019)
- [Accelerating Federated Learning via Momentum Gradient Descent](https://mlsanity.com/paper/1910.03197/) (2019)
- [Adaptively Truncating Backpropagation Through Time to Control Gradient Bias](https://mlsanity.com/paper/1905.07473/) (2019)
- [Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent](https://mlsanity.com/paper/2002.07891/) (2020)
- [Artificial Intelligence and Legal Liability](https://mlsanity.com/paper/1802.07782/) (2018)

## Built on by (most cited first, 5 of 5)

- [Extension of the Blackboard Architecture with Common Properties and Generic Rules](https://mlsanity.com/paper/2306.04287/) (2023)
- [Determining Sentencing Recommendations and Patentability Using a Machine Learning Trained Expert System](https://mlsanity.com/paper/2108.04088/) (2021)
- [Fake News and Phishing Detection Using a Machine Learning Trained Expert System](https://mlsanity.com/paper/2108.08264/) (2021)
- [Automating the Design and Development of Gradient Descent Trained Expert System Networks](https://mlsanity.com/paper/2207.02845/) (2022)
- [Implementation and Evaluation of a Gradient Descent-Trained Defensible Blackboard Architecture System](https://mlsanity.com/paper/2404.11714/) (2024)

## 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.