What is Explainable AI?
Artificial intelligence can transform any organization. That’s why 37% of companies already use AI, with nine in ten big businesses investing in AI technology. However, not everyone can appreciate the benefits of AI. One of the major hurdles to AI adoption is that people struggle to understand how AI models work. They can see the recommendations but can’t see why they make sense.
What is Explainable AI?
Explainable artificial intelligence (or XAI, for short) is a process that helps people understand an AI model’s output. The explanations show how an AI model works, the expected impact, and any potential human biases. Doing so builds trust in the model’s accuracy and fairness. And the transparency encourages AI-powered decision-making.
Why Do We Need Explainable AI for Business?
Artificial intelligence is somewhat of a black box. What we mean by that is you can’t see what’s happening under the hood. You feed data in, get a result — and you’re meant to trust that everything worked as expected. However, people struggle to trust the opaque process. That’s why we need explainable AI, both in business and many other domains.
What Can You Do with Explainable Artificial Intelligence?
Explainable AI helps everyday users understand AI models. And that’s crucial if we want more people to use and trust AI. You can use explainable AI in pretty much any context, with healthcare and finance being two strong examples.
Explainable AI in Healthcare and Finance
Let’s look at healthcare first. When dealing with a person’s health, you need to feel confident you’re making the right decision. Equally, practitioners want to be able to explain why they suggest treatment or surgery to their patients. Without explainability, this could be impossible. But with explainable AI, healthcare professionals can be clear and transparent across the decision-making process.
In domains such as finance, there are strict regulations. As a result, companies must be able to explain how their systems work in order to meet regulatory requirements. At the same time, analysts often have to take high-risk, potentially costly decisions. Blindly following an algorithm over a cliff isn’t a wise move. That is — unless you can audit why the algorithm suggested you take that step in the first place.
Explainable AI: Two Popular Techniques
There are several techniques to help us explain AI. But at a high level, explainable AI falls into two categories: global interpretations and local interpretations.
Benefits of Explainable AI
Explainable AI offers benefits to developers and end-users. Here are the three biggest benefits of embracing it.
Check Your AI Model Works as Expected
From a developer’s side, it can be hard to know if a model produces accurate results. The most effective way to check is to build in a level of explainability. Doing so allows humans to analyze how an algorithm drew its conclusions. We can then spot if shortcomings are undermining the model’s recommendations.
Build Stakeholder Trust in Your AI Recommendations
Organizations use artificial intelligence to help with decision-making. But there’s no way AI can help if stakeholders don’t trust the recommendations. After all — you wouldn’t take advice from someone you don’t trust, much less likely a machine you can’t understand. In contrast, if you show a stakeholder why a recommendation makes sense, they’re much more likely to agree.
Meet Regulatory Requirements
Every industry has regulations to follow. Some are more stringent than others, but nearly all have an audit process, especially concerning sensitive data. Take the EU’s GDPR and the UK’s Data Protection Bill, which both grant users the ‘right to explanation’ as to how an algorithm uses their data. Suppose you run a small business that uses AI for marketing purposes. If a customer wanted to understand your AI models, would you be able to show them? If you used explainable artificial intelligence, doing so would be simple.
Case Study: Explainable AI in EdTech
As we mentioned earlier, explainable AI can benefit all manner of industries. Case in point: our team recently applied explainable AI to a project for a global EdTech platform. We used the SHAP package to build an explainable recommendation engine that matches students with university courses they might like. And the explainability continues to help us tweak how the system works.
Conclusion
Explainable artificial intelligence promises to revolutionize how organizations worldwide perceive AI. In place of distrusting black-box solutions, stakeholders will be able to see precisely why a computer model has suggested a course of action. In turn, they’ll feel confident following a model’s recommendation. On top of this, developers will be able to constantly optimize algorithms based on real-time feedback, spotting faults or human bias in logic and correcting course. Thanks to all this, we expect more and more businesses to adopt AI over the next twelve months.
Frequently Asked Questions
- What is explainable AI?
Explainable AI is a process that helps people understand an AI model’s output. - Why do we need explainable AI?
We need explainable AI because people struggle to trust opaque AI models. - Can I use explainable AI in my business?
Yes, you can use explainable AI in almost any context, including healthcare and finance. - What are the benefits of explainable AI?
The benefits of explainable AI include checking if a model works as expected, building stakeholder trust, and meeting regulatory requirements.
