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Trustworthy AI

Causal AI is more transparent, reliable and fairer than other AI systems. It interacts with humans in ways no other technology can. Organizations trust Causal AI to help with their highest-stakes decisions.

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Trust matters...

It’s fundamental for social networks, businesses, and governments to function and thrive.

High-trust societies have higher GDP, stronger public institutions, and are more democratic. High-trust businesses are more profitable, and their employees are more satisfied. 

 

... and so does Trustworthy AI

Just as trust matters between human team members, AI systems must be trustworthy to establish successful human-machine partnerships. The human-machine relationship may be the most important bilateral relationship of our era.

Trust is top of the AI agenda. It’s the cornerstone of the OECD’s AI Principles and the key concept in the EU’s AI ethics regulations. And trustworthy AI isn’t just a compliance issue. Businesses that foster trusting human-machine partnerships can adopt and scale AI more effectively.

When AI is mistrusted, it remains confined to innovation labs. This is the state of play for most organizations. They are frustrated that their algorithms lack transparency, have biases, and routinely break down in production. AI has a trust crisis. 

Causal AI facilitates trust

Trustworthy AI has four key dimensions.

1. Explainability

Causal AI can explain why it made a decision, in human-friendly terms. Because causal models are intrinsically interpretable, these explanations are highly reliable. And the assumptions behind the model can be scrutinized and corrected by humans before the model is fully developed. Transparency and auditability are essential conditions for establishing trust.

“84% of CEOs agree that AI-based decisions need to be explainable to be trusted.”
PwC CEO survey

Find out more about how Causal AI promotes explainability here.

2. Fairness

Machine learning assumes the future will be very similar to the past — this can lead to algorithms perpetuating 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 impose fairness constraints before models are deployed and increases confidence that algorithms are fair.

“Causal models have a unique combination of capabilities that could allow us to better align decision systems with society’s values.”
– DeepMind

Find out more about how Causal AI promotes algorithmic fairness.

3. Reliability

For AI to be trustworthy it must be reliable, even as the world changes. Conventional machine learning models break down in the real world because they wrongly assume the superficial correlations in today’s data will still hold in the future. Causal AI continuously discovers invariant relationships that tend to hold over time, and so it can be relied on as the world changes.

“Generalizing well out the [training] setting requires learning not mere statistical associations between variables, but an underlying causal model.”
– Google Research and the Montreal Institute for Learning Algorithms

Read our white paper showing how Causal AI adapts faster than conventional algorithms as conditions change.

4. Human-machine interface

A final fundamental aspect of trust is communication. Causal AI keeps humans in the loop — users can communicate background information and business context to the AI, shaping the way the algorithm “thinks” via a shared language of causality. 

“We can construct robots that learn to communicate in our mother tongue — the language of cause and effect.”
– Judea Pearl

Read our article setting out how Causal AI opens new and improved communication channels between experts and AI.

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