Deep Learning and Explainable Artificial Intelligence (2nd Edition)

Dear Colleagues,

Breakthroughs in 'deep learning' via the use of intermediate features in multilayer 'neural networks' and generative adversarial networks using neural networks as generative and discriminative models, combined with the massive increase in computing power of GPU chips, have resulted in the widespread popularity and use of 'artificial intelligence' in the past decade. The apostrophes in the previous sentence are inserted intentionally to remind the reader that learning, in the biological sense, which improves survival outcomes via biological nervous systems or intelligent decisions that enhance energy and resource availability, is far beyond what current software can hope to achieve. The first phase of this Special Issue was successful in making AI/ML methods explainable in a variety of applications, ranging from agriculture to fluid mechanics to law enforcement. This second phase aims to go one step further towards the ultimate objective of making the human decision makers who use AI/ML methods accountable for their decisions.  This is possible when AI/ML methods generate outcomes with predictable properties when fed with data satisfying certain conditions. We do not want automatic machines massacring humans or opening dam waters or shutting down power grids or markets with no human being held responsible for negligence, damages, or even chargeable offences.

To this end, this issue calls for articles that lay out the limits of where and how AI in its present form can be used, besides continuing to make it explainable and transparent.

Thus, it is hoped that the Special Issue will stimulate AI that will increase efficiencies while not compromising safety, trust, fairness, predictability, and reliability when applied to systems with large energy use, such as power, water, transport, or financial grids, law, and government policy. As a first step towards this goal of transparency of AI algorithms, we seek papers that document the methods that meet the following criteria:

1. The results are reproducible, at least in the statistical sense;
2. Algorithms are provided in a common language of sequences of vector matrix algebra operations, which also underlies much deep learning;
3. Conditions satisfied by data inputs, objective functions of optimization, or curve fitting are explicitly listed;
4. The propagation of data uncertainty to algorithmic outcomes is documented through sensitivity analysis or Monte Carlo simulation;
5. Show why AI/ML methods are suitable for the application from the perspective of decision-maker accountability.

Potential issues of interest include the following: while there is no repeatability, in general, in the training of weights in deep learning or most neural networks, there is repeatability in approximating functions or decision boundaries for similar sets of input data. Such results also exist in adaptive control, where there is asymptotic tracking without the convergence of parameter estimates. Similarly, a ChatGPT-like AI needs to maintain the consistency of its conclusions, provided the inputs remain consistent. The use of AI in the law can have, for example, quantifiable goals such as the prompt compensation of the victim and long-term reformation of the criminal to higher levels of productivity rather than classical legal outcomes of punishment or retribution, which are subjective. Can a chess or GO GAN handle some level of randomness in the rules of the game? Is the repeatability or predictability of AI/ML high enough in a certain application to permit use in critical areas?

Dr. Kartik B. Ariyur
Guest Editor

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• model interpretability
• model explainability
• transparency in AI
• trustworthy AI
• human-in-the-loop AI
• human/decision maker accountability
• algorithmic accountability
• AI ethics
• causality/causal inference
• fairness, accountability, and transparency (FAT/ML)
• responsible AI

• Deep Learning and Explainable Artificial Intelligence in Computers (14 articles)