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Explainable AI and interpretability: Finding value in next-generation technology


Johannesburg, 02 Oct 2023
Trusting artificial intelligence with explainable AI.
Trusting artificial intelligence with explainable AI.

Artificial intelligence (AI) and machine learning (ML) have, it is fair to say, screamed into the global consciousness in a blaze of hype that has yet to die down. While these technologies have been available for a long time, their adoption and capabilities were a slow burn. They crept into systems and solutions and provided organisations with insights and value, but they didn’t turn tables and create fear quite as effectively as ChatGPT. Now the digital cat is out the proverbial bag, organisations are looking for ways to harness this technology so it delivers its immense value to their insights, systems and bottom lines.

This is where explainable AI and interpretability come in. IBM defines explainable AI (XAI) as a "set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms". It’s designed to help you understand how your AI models are thinking, analysing and assessing your data to come to its decisions, and the reasoning behind those decisions. XAI is a critical step towards ensuring any decisions made with AI are fair, unbiased, transparent and valid.

It is also very much a check and a balance that’s key for any business investing into AI, especially as the technology gains significant ground.

XAI is the digital equivalent of a dam wall. It stands in front of the torrent of AI applications, solutions, innovations, advancements and analytics and forces them to think. Where did the data come from? How is this data being interpreted? What factors could contribute to this data not providing the right levels of insight or perhaps introducing a bias that may reflect further down the line?

Explainable AI uses technologies and techniques that provide companies with transparency and insights into how their AI models are working. It allows for your teams to assess the inner workings of AI models so your stakeholders and decision-makers can trust in the results because there are checks and balances in place to validate the results.

And trust is the absolute key here.

Already, AI solutions and systems have engendered significant mistrust across multiple roles and layers of society. Many people are concerned that AI is poised to steal their jobs and take over their lives, leaving them at the mercy of a Terminator-esque intelligence that adds little value or quality to their lives. It’s a problem that McKinsey had already unpacked way back in its ‘The State of AI in 2020’ report, where it emphasised the importance of trust in ensuring AI succeeds, and the value of XAI in ensuring that this trust can be won.

If employees, partners, customers, stakeholders and decision-makers all have visibility into AI models and AI behaviours, then they are far more comfortable with how the AI interprets the data and the results it delivers. ChatGPT may have carved new ground with its exceptional abilities, but it also showed the world how capricious and untrustworthy AI can be without the right balances and processes in place. Its results are built on biases inherent within the internet and the data sources it uses, and this is a risk if anyone uses the information in a way that implies it is valid or true.

XAI allows for your business to realise the true potential of your AI and analytics investments but within a framework that wraps that potential in trust, visibility and transparency.  

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