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XAI: Increasing Transparency and Boosting Trust in AI

As artificial intelligence (AI) becomes increasingly sophisticated, it’s playing an ever-greater part in our day-to-day lives – powering systems and services ranging from virtual assistants to self-driving cars. But while the results delivered by AI are impressive, it’s not always easy to understand how they’re reached. This lack of transparency is still a major barrier to trust in and acceptance of these solutions.

Explainable AI (XAI) is a new approach designed to enhance digital trust by tackling these issues. In this blog, I’ll look at what XAI is, how it helps make AI more transparent and understandable, and some of the benefits it offers.

Explainable AI: What’s Is It Exactly?

If AI is to achieve broad acceptance and gain the trust of all stakeholders within organizations, it must be transparent. In other words, if we want people to trust the tech, we must be able to explain how and why AI came to a particular decision.

XAI is essentially a combination of various techniques and procedures that enable humans to understand and have confidence in outputs generated by machine learning (ML) algorithms as well as the outcomes resulting from these outputs.

To reliably assess whether AI systems are fair, unbiased, and non-discriminatory, we must be able to understand them. And it’s precisely this understanding that XAI is designed to deliver, bridging the gap between AI experts and laypersons by explaining just how an AI system came to a particular result.

Shining a Light into the Black Box of AI

While trust is a key factor here, there are other reasons why XAI is urgently needed. Over time, ever more data is fed into the models that underpin AI solutions. However, this information can influence the models in ways that were never intended – leading to inconsistent or unreliable outputs. So, to prevent such glitches, we have to understand why the AI is making certain decisions.

Because many conventional AI solutions are black boxes, whose inner workings are hidden from view, it’s often impossible for organizations to interpret or explain what exactly their models are doing. And as our reliance on AI grows, models that don’t function properly are likely to have even greater negative impacts – with potentially grave consequences.

Making AI Explainable: Three Approaches

From a technology point of view, there are various ways that XAI can be implemented. One of these involves developing AI models based on existing interpretable (for example, Bayesian) models and adjusting them, as required. With this approach, the inherent transparency of the underlying model allows us to follow how the input is transformed into the output.

Then there’s what’s known as the post-hoc approach. This deploys specialized techniques such as SHAP (SHapley Additive exPlanations) to explain how AI arrives at its predictions. And finally, XAI can be built on rule-based methods, which use rules or decision trees to explain the AI model’s decision-making process.

Enhanced Monitoring and Maintenance. Greater Trust

Having considered the broader background and tech aspects, let’s look at some of the benefits that XAI has to offer. In the MLOps space, making AI systems more transparent enables teams to pinpoint errors and identify opportunities for improvement – streamlining the monitoring and maintenance of AI systems.

As already mentioned, widespread acceptance of AI ultimately hinges on people’s trust in the tech. By delivering clear, comprehensible explanations of outputs, XAI enables non-experts to better understand how the system reaches its decisions – thus building trust in and acceptance of AI.

Ensure Business Value. Reduce Bias. Drive Growth.

XAI also has a number of other benefits. By making the functioning of AI systems transparent, it also allows technical and business teams to validate intended business objects so that the AI application delivers the expected value.

One common criticism of AI is that its results can be skewed owing to bias in the data fed into ML models. XAI also combats this issue by enabling organizations to trace errors and assess bias and data integrity, helping reduce bias.

And finally, XAI may also have a positive impact on companies’ financials: Experts predict that businesses that prioritize digital trust – for example, by enhancing the explicability of their AI – are likely to experience annual revenue and EBIT growth rates of 10 percent or more.

Laying a Firm Foundation for XAI

As you might expect, implementing Explainable AI is no walk in the park. However, there are ways that you can effectively gear up for initiatives of this kind. If you’re seriously thinking about adopting XAI, you should first take stock of any decisions that AI currently takes in your organization – and any that it’s likely to take going forward. Then, consider carefully where explanations may be needed.

If you already use quantitative and qualitative models to explain decisions reached by AI, it makes sense to take a long hard look at the design principles underlying your AI. Here, it pays to focus on whether there are ways of shaping the AI decision-making process so that it’s more human-centric and understandable.

The Growing Importance of XAI

As artificial intelligence continues to transform industries, XAI will be key to tapping AI’s full potential. If organizations are to develop effective AI systems, adopting Explainable AI will be increasingly important. And if CXOs are to make informed decisions about developing and deploying AI systems, they need to understand the benefits of XAI and the different approaches available.

Questions? Comments?

If you’re interested in taking a deeper dive into XAI and potential paths to its adoption, feel free to reach out to me. And if you’d like to share your experience and ideas relating to the understandability of AI outputs, please leave a comment below.

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