If AI is to meet basic business-use, legal and ethical needs, it must be explainable. Many stakeholders, from regulators to data scientists to end users, need to know how AI models work. Unlike conventional AI systems, Causal AI is intrinsically explainable and human-compatible. causaLens leverages Causal AI for the enterprise, putting the “cause” in “because”.
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.
Many advanced machine learning algorithms are “black boxes” that lack explainability.
While AI can generate enormous benefits, black box models lead to project failures and place businesses at needless risk.
Businesses need explainable AI to harness the technology’s true potential. Explainability enables a wide range of business stakeholders to audit, trust, de-bias, improve, gain insight from and partner with AI systems.
To illustrate why explainability matters, take as a simple example an AI system used by a bank to vet mortgage applications. There are a wide range of stakeholders that have an interest in this model being explainable.
Sam applies for a mortgage
Sam’s application is inputed into an AI
AI estimates Sams’ probability of defaulting
Sam’s mortgage application is rejected
Post hoc (“after the fact”) approaches attempt to make black box models comprehensible, often by aproximating them with a second model.
Intrinsically explainable models are already transparent and don’t require further explanation.
Intrinsically explainable machine learning models include linear models and decision trees.
Causal AI builds intrinsically explainable models that improve on standard models across several critical dimensions.
There’s a tradeoff between prediction accuracy and explainability in conventional machine learning that doesn’t apply to Causal AI
The causaLens dashboard produces causal diagrams behind its forecasts. Business stakeholders can intuitively understand these diagrams. They can also easily share knowledge with the AI, and partner with it to explore hypothetical scenarios and design business interventions. Here we show a lending model, with a specifically tailored module for evaluating model fairness. We have dashboards tailored to many areas of business, banking and finance.