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An introduction to Explainable Artificial Intelligence or xAI

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A few years ago, when I was still working for IBM, I managed an AI project for a bank. During the final phase, my team and I went to the steering committee to present the results. Proud as the project leader, I have shown that the model has achieved 98 percent accuracy in detecting fraudulent transactions. In my manager's eyes, I could see a general panic when I explained that we used an artificial neural network, that it worked with a synapse system and weight adjustments. Although very efficient, there was no way to understand its logic objectively.


Explainable Artificial Intelligence

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Dramatic success in machine learning has led to a torrent of Artificial Intelligence (AI) applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by the machine's current inability to explain their decisions and actions to human users. The Department of Defense is facing challenges that demand more intelligent, autonomous, and symbiotic systems. Explainable AI--especially explainable machine learning--will be essential if future warfighters are to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners.


Explainable Artificial Intelligence

#artificialintelligence

Dramatic success in machine learning has led to a torrent of Artificial Intelligence (AI) applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by the machine's current inability to explain their decisions and actions to human users. The Department of Defense is facing challenges that demand more intelligent, autonomous, and symbiotic systems. Explainable AI--especially explainable machine learning--will be essential if future warfighters are to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners.


Hardware Acceleration of Explainable Machine Learning using Tensor Processing Units

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Machine learning (ML) is successful in achieving human-level performance in various fields. However, it lacks the ability to explain an outcome due to its black-box nature. While existing explainable ML is promising, almost all of these methods focus on formatting interpretability as an optimization problem. Such a mapping leads to numerous iterations of time-consuming complex computations, which limits their applicability in real-time applications. In this paper, we propose a novel framework for accelerating explainable ML using Tensor Processing Units (TPUs).


The How of Explainable AI: Explainable Modelling

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Achieving explainable modelling is sometimes considered synonymous with restricting the choice of AI model to specific family of models that are considered inherently explainable. We will review this family of AI models. However, our discussion goes far beyond the conventional explainable model families and includes more recent and novel approaches such as joint prediction and explanation, hybrid models, and more. Ideally we can avoid the black-box problem from the beginning by developing a model that is explainable by design. The traditional approach to achieve explainable modelling is to adopt from a specific family of models that are considered explainable.