Why understanding AI matters
Phil Donelson Phil Donelson
February 18 4 min

Why understanding AI matters

As artificial intelligence (AI) becomes more prominent in our lives, we are increasingly reliant on its decision-making. Understanding AI and its reasoning is becoming more crucial to how we trust such a potentially powerful technology. But, as AI grows in sophistication, the complexity and abstraction behind its decisions takes us further away from that understanding. This is why many governing bodies, including the EU and British government, are pushing for greater AI explainability.

In this blog, we look at why understanding AI matters, exploring the concept of “the black box of AI” and why AI explainability is so important.

The black box of AI

The function of AI is defined by its ability to find patterns in enormous data sets, and to solve problems faster and more accurately than humans can.

Conclusions made from these patterns are often too complex for us to understand. This is referred to as “the black box of AI”. The more complex problems that AI can solve, the more difficult it becomes for us to comprehend how it has solved them. A dilemma arises: as AI becomes more accurate for complex, and more interesting, problems, it also becomes more difficult to explain.

Modeled on the mystery of the brain

Neural networks are the key to the success of technologies like machine learning and deep learning. It is what allows AI to ‘learn’, as it were. All you have to do is supply the data as learning material. Modeled on how the human brain works, neural networks process information by passing it through numerous layers. Mathematical rules are used to score each input at each layer. As it passes through multiple layers, a structured set of relationships is created, until it produces an output or prediction.

These connections between the layers are hard to decipher: that’s the “black box”. The black box of AI is the complexity or the lack of clarity of this multi-layered learning-to-output process.

AI explainability

AI explainability, then, is the concept of how we make AI more transparent, how we “open the black box” so we can understand why an outcome has been arrived at.

The importance of building user trust

The more results-driven of you out there might ask: “Why is it so important to know how AI has come to a decision? Surely, it’s the results that matter.” Here are some examples which illustrate why AI explainability is so important.

Diagnosing decisions

AI is increasingly being used successfully in healthcare, with AI models demonstrating incredible accuracy at predicting diagnoses at an earlier stage than previously possible. The potential for this technology to transform people’s lives is vast. But what happens when we can’t explain why a diagnosis has been made?

Yes, it might not seem to matter when the goal is saving lives, if lives are saved. But what happens when the AI model gets it wrong, and the diagnosis is incorrect; what are the legal, medical, and insurance implications of scenarios like this? How can this mistake be explained to governing bodies, healthcare professionals, and, most importantly, the patients? Another good example is autonomous cars. If an accident occurs and the car manufacturer cannot explain why its vehicle made the decision that it did, how do we proceed?

AI safeguarding

Situations like this have the potential to lead to people losing trust in AI models. It’s crucial to understand AI decisions, so that you can build user trust. You must be able to understand why AI has made a prediction if you want to safeguard against the scenarios above and to prevent your AI models developing undesirable results or biases.

Understanding AI with human-centric solutions

Trust and transparency are essential, if we are going to achieve what’s possible with AI — a world where AI is a driving force for improving human lives. At Element AI, we have developed a solution to the problem of the AI “black box”.

AI explained

Our explainability API is designed to remove any reliance on “black-box” models by providing clear explanations tailored to users with different technical and business knowledge.

The solution provides interpretable interfaces and visualizations to make it much easier for users to understand AI outcomes. Fairness analysis, which explores, identifies, and mitigates for inaccurate or unfair models that have learned biases from poorly constructed datasets, helps build user trust and prevent undesirable biases from occurring.

The key to success with AI explainability is to make your AI-solutions as human-centric as possible. Because it is only when AI solutions can be easily used and understood by those who use the technology, that it can begin to solve business problems and transform lives.