Explainable AI (XAI) is an emerging field in artificial intelligence that focuses on making AI systems more transparent, interpretable, and understandable to humans. The rise of XAI is driven by the growing need for transparency and accountability in AI decision-making processes, especially as AI systems become more complex and are deployed in high-stakes domains like finance, legal, or healthcare. 

What is XAI? AI often arrives at a result using a machine learning (ML) algorithm, but the architects of the AI systems do not fully understand how the algorithm reached that result. XAI implements specific techniques and methods to ensure that each decision made during the ML process can be traced and explained.

According to a case study published by IBM, the US Open used watsonx. governance to remove bias from tournament data to increase tennis court fairness from 71% to 82%. 

Explainable AI systems are more likely to be used and trusted by employees and stakeholders. Implementing XAI can also help mitigate regulatory risks, particularly in light of emerging AI laws like the EU AI Act.

Top Explainable AI Companies

• Microsoft (US)
• IBM (US)
• Temenos (Switzerland)
• Seldon (London)
• Squirro (Switzerland)

2025 will see more organizations like IBM supporting and investing in the development of frameworks for explainability, and collaborating with open-source developers to create robust libraries for models, tools, and methodologies that support explainability. Businesses that opt to overlook this issue or take shortcuts in 2025 should anticipate negative publicity, scrutiny from regulators, and a lack of support from their customers. The adoption is going to be particularly strong in highly regulated sectors where accountability and transparency are crucial.