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TED: Teaching AI to Explain its Decisions (1811.04896v2)

Published 12 Nov 2018 in cs.AI

Abstract: Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However, as many of these systems are opaque in their operation, there is a growing demand for such systems to provide explanations for their decisions. Conventional approaches to this problem attempt to expose or discover the inner workings of a machine learning model with the hope that the resulting explanations will be meaningful to the consumer. In contrast, this paper suggests a new approach to this problem. It introduces a simple, practical framework, called Teaching Explanations for Decisions (TED), that provides meaningful explanations that match the mental model of the consumer. We illustrate the generality and effectiveness of this approach with two different examples, resulting in highly accurate explanations with no loss of prediction accuracy for these two examples.

Citations (109)

Summary

  • The paper introduces the TED framework, training AI to generate decisions with coherent, expert-aligned explanations.
  • It demonstrates high performance with examples achieving over 97% accuracy in decisions and 96% in explanations for tic-tac-toe and nearly 100% for loan repayment cases.
  • The approach enhances transparency and regulatory compliance in high-stakes fields by simplifying the generation of user-understandable AI explanations.

An Expert Overview of the TED Framework for Explainable AI

The paper "TED: Teaching AI to Explain its Decisions" addresses the burgeoning requirement for artificial intelligence systems to provide explanations for their decision-making processes. As AI systems are increasingly deployed across various sectors, their decisions influence critical areas such as healthcare, finance, and judicial settings. This necessitates a focus on explainable AI to not only foster trust in these systems but also to ensure compliance with regulatory demands, such as those highlighted by the European Union's GDPR.

The research introduces the Teaching Explanations for Decisions (TED) framework, proposing a novel approach in contrast to conventional methods that attempt to decipher the complex inner workings of AI models. The TED framework advocates for training AI systems to produce explanations that align with human reasoning processes by leveraging enriched training datasets containing explanations provided by domain experts. This methodology places emphasis on creating explanations that are understandable, relevant, and tailored to the needs of varied consumer groups, ranging from technical developers to non-expert end users.

Contributions and Methodology

The paper's key contributions include:

  1. Highlighting the challenges in offering meaningful explanations for AI decisions.
  2. Presenting the TED framework, which inherently captures both the decision-making process and the explanation simultaneously during the training phase.
  3. Demonstrating the effectiveness of the TED approach through illustrative examples with high accuracy in providing explanations without a reduction in prediction accuracy.
  4. Outlining the implications for regulatory compliance, user trust, and the general pedagogical benefit of the approach.

The TED framework employs a simple Cartesian product instantiation strategy, where two main components, decisions (YY) and explanations (EE), from the training dataset, are combined. This product (YEYE) is used in conjunction with any existing supervised classification algorithm to predict outcomes and provide explanations. Upon generating a prediction, the explanation is decoupled, ensuring that both the decision and the reasoning are communicated effectively.

Results and Analysis

The research illustrates the effectiveness of the TED approach through two examples: the tic-tac-toe game and a loan repayment case paper. In the tic-tac-toe example, the framework was able to predict the correct move with a high accuracy of 97.4% while simultaneously providing correct explanations 96.3% of the time. Similarly, in the loan repayment scenario, the approach achieved a 99.6% accuracy in predictions and a 99.4% accuracy in explanations using a Random Forest classifier.

These results underscore that the TED framework can provide highly accurate, contextually relevant explanations without compromising on the prediction accuracy. The implications of such findings are significant for high-stakes decision-making domains where understanding the rationale behind AI systems' outputs is crucial for user acceptance and regulatory adherence.

Future Directions

The research further discusses extensions to the TED framework, suggesting improvements through adaptations like multi-task learning configurations or employing domain-specific languages for structured explanations. The authors also open avenues for exploring reduced explanation requirements on training datasets by leveraging active learning or external knowledge sources, potentially easing the integration process in existing systems. These future developments point towards creating more robust and universally applicable explainable AI frameworks.

Conclusion

The TED framework represents a pivotal shift in framing explainability as a facet of the training process, emphasizing the importance of aligning AI-generated explanations with human reasoning. The approach simplifies the task of generating meaningful explanations while ensuring that user expectations about AI capabilities are set realistically. The paper sets a promising foundation for further enhancing the transparency and trustworthiness of AI systems.

By redirecting focus from model interpretation to model explanation through example-driven learning, the TED framework aligns with societal and regulatory expectations for AI transparency. The implications for the field of AI are expansive, signaling potential shifts in how AI systems are designed, evaluated, and deployed in decision-critical environments.

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