From “Why Should I Trust You?” Explaining the Predictions of Any Classifier.īuilding a second model to explain your first model undermines confidenceĮxplanatory models by definition do not produce 100% reliable explanations, because they are approximations. A post hoc explanation model reveals that the black box model is distinguishing huskies from wolves based on a spurious correlation (the presence of snow). They thereby help you to decide whether to trust your model - but they don’t actually make the original model trustworthy. Post hoc explanations can potentially reveal these problems (see the Figure below).
They also absorb biases that are implicit in the training data. Post hoc explanations don’t magically make your original black box model trustworthyĬonventional machine learning algorithms learn lots of spurious and misleading correlations in input data. But these and other similar methods don’t deliver helpful explanations, for many reasons.
LIME and SHAP can help to lift the lid on black box models. What’s wrong with post hoc explainability? Note that SHAP doesn’t look at every possible permutation, because that’s too computationally expensive - it just focuses on the ones that convey the most information. It does this by permuting through the feature space, and looking at how a given feature impacts the model’s predictions when it’s included in each permutation. SHapley Additive exPlanations (SHAP) learns the marginal contribution that each feature makes to a given prediction. LIME then simply trains a white box model, like a linear regression, on this synthetic dataset to explain the original prediction. Local Interpretable Model-agnostic Explanations (LIME) probes the black box model by slightly perturbing the original input data, and then records how the model’s predictions change as a result. Let’s briefly sketch how LIME and SHAP work, at a conceptual level. There are a huge number of XAI approaches, however two are especially popular: LIME and SHAP. The standard approach is “ post hoc” (“after the event”) explainability - this often involves building a second model to approximate the original one. In fact, today’s more sophisticated machine learning models are total black boxes.Ĭurrent XAI solutions take these powerful black box models and attempt to explain them. A drawback of this approach is that humans (even programmers) oftentimes can’t understand how the resulting machine-learnt models work. Today’s AI systems generally acquire knowledge about the world by themselves - this is called “machine learning”. How do machine learning algorithms provide explanations? In a nutshell, explainability enables a wide range of stakeholders to audit, trust, improve, gain insight from, scrutinize and partner with AI systems. The example above relates to mortgage applications, but explainability matters in almost every enterprise AI use case, especially those that involve some element of risk. More broadly, 42 governments have committed to principles of transparency and explainability as part of the OECD’s AI Principles framework.īusiness owners and board members need to ensure that explainable AI systems are compliant, trustworthy and aligned with corporate strategy. The UK government has issued an AI Council Roadmap appealing for greater AI governance. The FTC in the US is clamping down on AI bias and demanding greater transparency. Ĭanada is also issuing legally binding private sector regulations mandating explainability, with fines issued for non-compliance. causaLens have provided expert commentary on the new regulations - read more here.
Incoming regulations in the EU demand explainability for higher risk systems, with fines of up to 4% of annual revenue for non-compliance. Without explanations, if the model makes lots of bad loan recommendations then it remains a mystery as to why.
#White out xai upgrade
Analysts can also learn new insights from an explainable AI system, rather than just blindly following its recommendations.Įxplainability enables tech developers to troubleshoot, debug and upgrade their models, as well as innovate new functionalities. End users have a legal “right to explanation” under the EU’s GDPR and the Equal Credit Opportunity Act in the US.Įxplanations allow underwriters to verify the model’s assumptions, as well as share their expertise with the AI. Sam may want to contest the AI’s decision, or check that it was fair. There are a wide range of stakeholders that have an interest in this model being explainable: A simple example of an AI use case: an AI model decides which mortgages to approve. To illustrate why explainability matters, take as a simple example an AI system used by a bank to vet mortgage applications. Explainable AI (“XAI” for short) is AI that humans can understand.
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