Digital Themes

Explainable AI

AI systems can be famously opaque, and the work of a machine learning (ML) model is often done in the dark.

Explainable AI (XAI) is an answer to the mystery that lies at the heart of most ML models. XAI algorithms are transparent, explainable, and traceable.

Even the most clever domain experts are hard-pressed to locate the source of an AI model’s conclusions, as the black-box quality of deep learning and of deep neural networks (DNNs) does not spare the AI model’s operators or even its creators. Explainable AI, then, champions the processes and methods that lend transparency to the process. It builds trust in the machine learning model, and the black-box models become white-box models. 

Explainable AI does not necessarily refer to an AI model whose processes are immediately visible, as post-hoc explanations can provide insight and transparency after the fact — after a conclusion is reached. 

In 2021, Google Cloud launched Vertex AI, and in 2022, Example-based Explanations from Vertex AI promised to help users “build better models and loop in stakeholders.” On Google Cloud’s Explainable AI page, Google Cloud users can access tools and frameworks to become better acquainted with their machine learning models. The tools boast that they can help users design interpretable and inclusive AI; deploy AI with confidence; and streamline model governance. Features include a model analysis toolkit and actionable explanations.

Future AI success depends on increased transparency within systems and increased trust from users. Per 2022 research from McKinsey, “Organizations that establish digital trust among consumers through practices such as making AI explainable are more likely to see their annual revenue and EBIT grow at rates of 10 percent or more.”

Because its processes point toward increased regulation and transparency, XAI can be particularly helpful to medical professionals, government employees, or others whose work must be traceable and accounted-for.

The business benefits of XAI include the following:

  • Increased trust from employees in AI systems and accompanied increase in adoption of AI technologies
  • Risk mitigation
  • Alignment with regulatory requirements
  • Data-driven decision-making
  • Reassurance for stakeholders that machine learning bias is not present   
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