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建構XAI金融程式交易預測模型
"As with any black box, if you don't know why it works,
you won't realize when it's stopped working. Even a
broken watch is right twice a day." — Mr. Lo of MIT
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Source: Holy Grail of AI for Enterprise — Explainable AI (XAI) by Saurabh Kaushik
XAI 三個主要面向:
AI作的預測人類覺得合理
人類能理解模型如何作決策
人類可以溯源整個預測過程
內
外
信任是由內而外徹底的
清楚 、理解和認同
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People do not expect explanations that cover the actual and
complete list of causes of an event. We are used to selecting
one or three causes from a variety of possible causes.
你希望聽到金融程式交易的背後理由是⾧篇大論嗎?
Even if the world is more complex, AI gives a short
explanation with simply one to three reasons.
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Source: Holy Grail of AI for Enterprise — Explainable AI (XAI) by Saurabh Kaushik
XAI — Major Techniques:
There are two major techniques for XAI.
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Similarity is an organic conceptual framework
for machine learning models because it
describes much of human learning.
There is nothing more basic to thought and language than our sense of
similarity; our sorting things into kinds.
Source: ONTOLOGICAL RELATIVITY AND OTHER ESSAYS (1969) by W.V. QUINE
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Example-Based Explanations（基於實例的解釋）
Example-based explanation methods select particular instances
of the dataset to explain the behavior of machine learning
models or to explain the underlying data distribution.
Example-based explanations are mostly model-agnostic,
because they make any machine learning model more
interpretable. The difference to model-agnostic methods is that
the example-based methods explain a model by selecting
instances of the dataset and not by creating summaries of
features (such as feature importance or partial dependence).
