Google's AI Explains Itself How Google's xAI tools help evaluate model performance

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Information related to Explainable AI (xAI)

Google's AI platform offers a view into the mind of its machines.

What’s new: Explainable AI (xAI) tools show which features exerted the most influence on a model’s decision, so users can evaluate model performance and potentially mitigate biased results.

How it works: xAI is available to users of Google's Cloud AI platform and its AutoML models and APIs. The core of xAI is a pair of tools that provide graphs or heat maps (depending on the type of data) that show the relative importance of each attribute in a model’s prediction.

  • The tool called Integrated Gradients is for neural networks and other models whose attributes are mathematically differentiated. It assigns each feature a Shapley value (a concept borrowed from game theory that measures how important each player is in a cooperative outcome) that grades its role in the predicted outcome. It’s appropriate for most neural nets, according to Google.
  • Many ensemble neural nets and trees have non-differentiable attributes. The tool called Sample Shapley explains such cases. It grades each attribute after sampling permutations duplicated across the nets in an ensemble.
  • Google Cloud AI also offers free access to its What-If tool, which compares the performance of two models on the same dataset.

Why it matters: The ability to explain how AI models arrive at decisions is becoming a major issue as the technology reaches into high-stakes aspects of life like medicine, finance, and transportation. For instance, self-driving cars would fit more easily into existing regulatory and insurance regimes if they could explain their actions in case of an accident.

Yes, but: Explainability techniques are not a silver bullet when it comes to mitigating bias.

  • In an analysis of Google’s tools, Towards Data Science argues that, given the subtlety of hidden correlations that can lurk in datasets, xAI is too superficial to offer meaningful insight into bias, especially in ensemble neural networks. The industry as a whole would be better off formulating standards to ensure that datasets are as unbiased as possible, writes Tirthajyoti Sarkar, who leads machine learning projects at ON Semiconductor.
  • Google itself acknowledges xAI’s limitations. A white paper details the many ways in which human bias can impinge on techniques that aim to explain AI decisions. For example, human analysts might disregard explanations highlighting attributes that trigger their own biases.

We’re thinking: As machine learning engineers, we need ways to make sure our models are making good decisions. But we should also keep in mind that explainability has limits. After all, human decisions aren’t always explainable either.


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