In recent years, Artificial Intelligence has increasingly grown to be the number one tool used to improve the workflows of businesses across many industries. These use-cases to streamline and automate business processes are achieved by focusing on what AI is best at: the handling of repetitive and mundane tasks, processing massive amounts of data and automating simple algorithmic decisions.

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The use of AI in business has sharply increased over the years; the understanding of how the underlying systems “think”, however, has not. AI systems are often described as ‘black boxes’, referring to the lack of introspectibility and the difficulty in seeing how the deep learning systems actually arrive to conclusions. This opaqueness makes it difficult to isolate and change undesirable or biased outputs. It inhibits traceability and makes the decisions taken by the models nearly impossible to explain. For the service providers implementing such models, this creates risk and can erode the trust of the end-users that are directly affected by those decisions.

How can the general population trust companies using technology that they themselves struggle to explain? Trust is a major factor that influences consumer choice.

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‍PwC’s 2024 Trust Survey revealed that the manner in which companies govern and safeguard their use of AI will have a significant impact on stakeholder trust. According to the survey, two thirds of consumers expressed that it is important that companies disclose their AI Governance policy; yet, only 33% of participating companies follow that guideline. This highlights a significant gap in the expectations between consumers and businesses on the transparency and explainability of their automated decision-making systems.

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During training and development, these systems teach themselves to spot patterns in large amounts of data. When looking into the mind of neural networks, decisions are represented as billions of artificial “neurons” represented as matrices of numbers which are too difficult to represent and translate into human-readable and actionable insights. The gargantuan effort involved in making more transparent AI systems and decision-making engines, understandable by users and developers alike, is known as “Explainable AI” (XAI), and is being prioritized by many large players in the industry.

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Institutions such as the European Union understand the need for policies that require transparency from AI models that can impact billions of consumers. Systems following the EU AI Act can mitigate flaws in decision making and task performing algorithms. This implementation of transparent and explainable AI will help companies provide better services and reinforce trust with their clients.