Causal AI is the only technology that can reason and make choices like humans do. It utilizes causality to go beyond narrow machine learning predictions and can be directly integrated into human decision-making. It is the only AI system organizations can trust with their biggest challenges – a revolution in enterprise AI.
Predictions only
Limited explainability
Spirals out of control in novel situations
Minimal human-machine interaction
Constrained by historical data
No guarantees on fairness
Needs a lot of data
Causal AI doesn’t just predict the future, it shapes it.
Current AI is limited to making predictions. However, forecasting accounts for a small fraction of the value chain in enterprise AI. The true potential of AI lies with empowering humans to make better decisions. Causal AI autonomously finds interventions that achieve a given strategic goal or that maximize a KPI (autoKPI™).
Consider a telecommunications provider trying to reduce customer churn. Conventional machine learning systems just attempt to predict likely churners. Causal AI recommends the most effective interventions (sales outreach, targeted advertising, price discounts) and the most responsive customer segments that minimize churn. It also factors in the telco’s business model and goals.
Read more on how Causal AI promotes optimal decision-making.
Put the “cause” in “because” with next-generation explainable AI.
If AI is to meet basic business-use, legal and ethical needs, it must be explainable. However, machine learning models are black boxes, and attempts to explain them aren’t suitable for non-technical stakeholders. Causal AI builds human-friendly glass-box models. Humans can scrutinize and alter the assumptions behind models before they are deployed.
Take an AI model used by a bank to approve lending decisions. Causal AI reveals why an applicant might be denied credit and allows the bank to audit the assumptions the model is making. Explanations can be generated before the model is fully trained, reinforcing trust in the model in deployment.
Find out more about how causaLens puts the “cause” in “because”.
Causal AI continuously adapts to real-world dynamics.
87% of machine learning projects are terminated during an experimental phase. The remainder that make it into production are prone to fail as the world changes. This is because current AI systems are not suited to real-world dynamics. Causal AI is robust to changing conditions because it learns invariant causal relationships in data that hold across different contexts.
Consider a healthcare provider predicting demand for hospital services to optimize patient flow. Conventional algorithms cannot adjust to unusual variations in patient demand, and totally break down during crises. Causal AI reliably forecasts ordinary demand spikes and adapts at least 3x faster to crises, leading to improved patient outcomes and reduced healthcare costs.
Read our white paper on how Causal AI adapts to crises.
Human-plus-Causal AI partnership allows organizations to harness the benefits of AI.
Conventional machine learning algorithms permit only very limited human-machine interaction, which oftentimes is limited to feature selection. Causal AI empowers people to seamlessly share knowledge with algorithms — by reshaping the algorithm’s causal model of the world. Users can provide human context, goals and metrics that are necessary for solving challenges.
Take a government forecasting conflict overseas to optimize aid allocation and save lives. Human experts can work with Causal AI to explain how drivers of conflict may vary across regions or how they are dependent on socioeconomic and demographic factors. Further, they can supply context about aid budget and other resource constraints.
Read more on how Causal AI empowers experts.
Causal AI can explore hypothetical worlds, uncovering insights that explain why events happened.
The ability to imagine hypothetical scenarios is critical for understanding the real world. We learn why things happen via imagination. However existing AI solutions are limited to analysing patterns in historical data. Causal AI has human-like imaginative capabilities. It can explore hypothetical worlds, uncovering insights that explain why events happened.
Picture a cleantech manufacturer producing solar panels. They can use machine imagination on IoT data to carry out predictive maintenance. Causal AI evaluates a series of hypothetical questions (for example, “If the warehouse humidity had been 1% greater, would this batch of panels have been defective?”), to investigate the root causes of failures.
Find out more about artificial imagination.
AI has a bias problem and Causal AI is the solution.
Current AI algorithms assume that the future will be very similar to the past — unfortunately, this leads them to perpetuate historical injustices. Causal AI can envision futures that are decoupled from historical data, enabling users to eliminate biases in input data. Causal AI also empowers domain experts to interrogate their biases and impose fairness constraints on algorithms.
Think again of an AI system used in a bank to make lending decisions. Causal AI can ensure that protected characteristics, like gender or race, are not impacting lending decisions. Compliance and audit teams can also check and modify the algorithms’ assumptions before it is fully trained and deployed.
Learn more about how Causal AI promotes algorithmic fairness
70% of organizations are shifting their focus from big to small data — Causal AI can help.
Small data is ubiquitous in government and business, yet conventional machine learning algorithms require big data to produce any results. Causal AI applies causal discovery algorithms to zero in on the information that matters in sparse data environments and is able to absorb human input to fill in the gaps in its knowledge.
Time-series data in real estate investment is typically “low velocity” — with quarterly or annual time steps. Causal AI can model the impact of inflation on real estate, model the future of rental yields in offices and make reliable city-level predictions, while conventional algorithms fail to produce results.
Learn more in our blog.
Trust is the most important but often-overlooked ingredient in successful AI adoption.
As AI plays an increasing role in society and business, it’s critical that humans trust machines. However current machine learning has a trust crisis: businesses and governments don’t have the confidence to use AI for real-world decision-making.
We’ve set out how Causal AI is human-centric, adaptable, explainable, fair and goes beyond predictions to make decisions and generate insights. These capabilities make Causal AI the only AI that humans and organizations can trust in the field.
Learn more about how Causal AI builds trust.
causaLens is committed to building the world’s leading Causal AI research lab — check out the press release. We have channelled our expertise into developing the world’s only Causal AI Platform — learn more.
