Building Trust and Driving Business Value with Explainable AI

Artificial Intelligence is a hot topic as companies and governments explore ways that computers can crunch data and perform tasks faster and better than humans.

But do we really know how AI “thinks” and whether it’s reliable?

The answer comes from Explainable AI, which brings transparency and accountability to how AI models analyse data and make decisions.

Read on for our breakdown of the white paper on Explainable AI (XAI) recently published by Element AI, a Canadian software company with offices in Montreal, Toronto, London, Seoul and Singapore.

Building trust in AI is key to increasing enterprise adoption and to nurture an inclusive ecosystem for all participants,” said Luis Gonzalez, Element AI’s managing director for Asia, who will be speaking at CABC’s Innovate Canada-ASEAN event on Sept 9 in Bangkok.

“Explainable AI is one of the critical aspects of responsible AI and an essential tool with which to build trust.”

What is Explainable AI?

Developing a solution is one thing but the biggest challenge is getting users to adopt it. Within the AI community, there is still not enough recognition that people need to trust AI to put it to use.

XAI is an evolving research area that aims to make machine decision-making understandable to humans. It is about showing the reasoning within an AI model – from its inner workings through to its output.

Business Value of XAI

XAI encourages trust. Trust drives adoption. Adoption drives business value.

For companies, governments and other organisations, the major aspects of XAI include:

  • Regulatory compliance
  • Speed of deployment
  • Detecting bias
  • Protection against adversarial techniques

In other words, XAI could be a competitive differentiator for those who integrate it into their AI systems.

Explainability is the Future

Pursuing XAI now could open up opportunities for a greater range of AI applications in regulated industries and beyond.

Healthcare, financial services and other similarly data-rich and regulated sectors offer some of the most promising applications for AI decision-making. Right now, the law requires explainability in only a small subset of decisions, even within those regulated industries.

As governments and regulators study AI and its impacts, explainability could play a part in compliance with emerging regulation.

Most AI practice now puts performance first, with explainability dealt with as an afterthought or ignored completely. Instead of focusing on performance over explainability, AI scientists should work towards both.

To find out more about how XAI can drive business value, click here to download Element AI’s white paper “Opening the Black Box”.