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Truly Explainable AI

If AI is to meet basic business-use, legal and ethical needs, humans must be able to comprehend how it works. Unlike conventional AI systems, Causal AI is intrinsically explainable and human-compatible. causaLens leverages Causal AI for the enterprise, putting the “cause” in “because”.

What is Explainable AI?

Explainable AI (“XAI” for short) is AI humans can understand.

XAI sometimes encompasses important questions about algorithmic bias, fairness and discrimination. Find out how Causal AI addresses algorithmic bias here.

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STAKEHOLDERS

Managers & Board members

Board members and business leaders can ensure that explainable AI systems are compliant, trustworthy and aligned with corporate strategy.

Regulators & governments

Incoming regulations in the EU demand explainability for higher risk systems, with fines of up to 4% of annual revenue for non-compliance. causaLens has provided expert commentary on the new regulations — read more here.  Canada is also issuing legally binding private sector regulations mandating explainability, with fines issued for non-compliance. The FTC in the US is clamping down on AI bias and demanding greater transparency. The UK government has issued an AI Council Roadmap appealing for greater AI governance. More broadly, 42 governments have committed to principles of transparency as part of the OECD’s AI Principles framework.

End users affected by AI

Sam may want to contest the AI’s decision or check that it was fair. They have a legal “right to explanation” under the EU’s GDPR and the Equal Credit Opportunity Act in the US.

Domain experts & business analysts

Explanations allow underwriters to verify the model’s assumptions, as well as share their expertise with the AI. Analysts can also learn new insights from an AI system that they understand, rather than just blindly following its recommendations.

Data scientists & product managers

Explainability enables tech developers and data scientists to troubleshoot, debug and upgrade their models, as well as innovate new functionalities. Without explanations, if the model makes lots of bad loan recommendations, then it remains a mystery as to why.

  • 1. Sam applies for a mortgage

  • 2. Sam’s application is inputted into an AI

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  • 3. AI estimates Sam's probability of defaulting

  • 4. Sam’s mortgage application is rejected

Truly Explainable AI: Putting the "Cause" in "Because"

Most powerful machine learning models are too complicated for anyone to comprehend or explain, and “overfit” to the past in complex, dynamic environments. Causal AI solves both problems, building models that are highly accurate and inherently explainable.

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Two kinds of XAI

There are two distinct approaches to explainability, each with very different technical and commercial implications.

POST HOC EXPLAINABILITY

Post hoc (“after the fact”) approaches attempt to make black box models comprehensible, often by approximating them with a second model.

2 Models — a black box model and an approximation algorithm — erodes trust

Explanations based on an approximation model that is not 100% reliable, resulting in false confidence, fragility and fairwashing.

Explanations only after model building

You only find out whether your model is safe, fair and compliant, after you’ve invested resources in building it.

Static system only

Post hoc explanations only explain how models have already behaved in previously encountered circumstances. When the world radically changes (think COVID-19) and your model breaks, you only find out after the fact.

Not suited to non-technical stakeholders

Known to be unusable by most stakeholders, including end users and regulators.
INTRINSIC EXPLAINABILITY

Intrinsically explainable models are already transparent and don’t require further explanation.

1 Model reinforces trust

Explanations are guaranteed to be faithful to your model, promoting trust and transparency.

Explanations before model building

Domain experts can select features to include or exclude before model building, helping reduce algo risk.

Dynamic systems

You can guarantee to your stakeholders, including regulators and risk teams, how the model will behave in all circumstances, including ones that haven’t happened yet.

Human-friendly

Non-technical users can easily interpret and interface with models.

Causal AI: True XAI

There are clear advantages to intrinsically explainable AI. However not all intrinsically explainable models are equally good. Causal AI has several critical advantages compared to vanilla interpretable machine learning models.

CONVENTIONAL INTRINSIC EXPLAINABILITY

Intrinsically explainable machine learning models include linear models and decision trees.

Explainability/accuracy tradeoff

Intrinsic explainability comes at the expense of model flexibility and performance in conventional machine learning.

Minimal human-machine interaction

Users are limited to feature selection.

Predictions only

Explanations are ultimately correlation-based. This means they can be used only for predictions, and can be seriously misleading if naively applied to business decision-making.

Limited actionable information

Explanations don’t provide easily actionable information — if you are unhappy with a prediction you need to fit an entirely new model. This may change the model in unexpected ways, requiring you to generate new explanations and reiterate.
CAUSAL AI

Causal AI builds intrinsically explainable models that improve on standard models across several critical dimensions.

Explainability/accuracy synergy

More realistic causal models, that describe the world more accurately, also produce better explanations.

Symbiotic human-machine partnership

Domain experts can fully interact with the model specifying causal relationships between variables and manipulating causal graphs with complete freedom. Models can be interrogated and probed by users, to simulate business experiments, address why-questions, and explore “what if” scenarios.

Beyond predictions

Causal graphs are also used for predictions. But beyond this, causal explanations contain information about the expected consequences of taking various business actions as well as diagnosing why certain events happened.

Actionable insights

Models can be easily manipulated on the basis of explanations, without re-running the data science pipeline. Models gracefully evolve, with new models retaining everything that was good about old versions.

Causal explainability complements model performance

There’s a tradeoff between prediction accuracy and explainability in conventional machine learning that doesn’t apply to Causal AI. Our technology produces models that are more accurate and that everyone can understand. Mobilize explainable AI in your organization.

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Explainable AI doesn't explain enough

Learn more about how and why Causal AI generates a superior kind of explainability.

Read the blog

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