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Found 6 papers, 2 papers with code
Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations
Through an extensive set of experiments, we find that ChatGPT's self-explanations perform on par with traditional ones, but are quite different from them according to various agreement metrics, meanwhile being much cheaper to produce (as they are generated along with the prediction).
Convolutional Neural Network Model for Diabetic Retinopathy Feature Extraction and Classification
Our contribution is an interpretable model with similar accuracy to more complex models.
Anticipatory Thinking Challenges in Open Worlds: Risk Management
StarCraft and Go are closed-world domains whose risks are known and mitigations well documented, ideal for learning through repetition.
"Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI
There is broad agreement that Artificial Intelligence (AI) systems, particularly those using Machine Learning (ML), should be able to "explain" their behavior.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Explaining Explanations to Society
We explore the types of questions that explanatory DNN systems can answer and discuss challenges in building explanatory systems that provide outside explanations for societal requirements and benefit.
Explaining Explanations: An Overview of Interpretability of Machine Learning
There has recently been a surge of work in explanatory artificial intelligence (XAI).
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
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