Open-source artificial intelligence tools are increasingly recognized as a backbone of ethical, transparent and trustworthy AI systems — and recent developments around explainability frameworks and maintainer support are helping push this movement forward. As AI models grow in complexity, the need to understand how they arrive at decisions — often described as explainable AI (XAI) — is now a key priority for researchers, enterprises and governments alike.
Explainable AI refers to techniques and tools that help users and developers interpret the behavior of machine learning systems, especially those based on deep learning or large language models that typically behave like “black boxes.” By revealing how models weigh features and make predictions, explainability tools aim to build trust, support regulatory compliance and reduce risks such as unfair bias or unexpected errors.
A notable recent initiative in the open-source XAI landscape is Facet, an interpretability library developed by BCG X. Facet helps data scientists and AI engineers understand how variable interactions affect model outcomes by uncovering the relationships between different inputs and predictions. Tools like Facet can provide deeper insight into complex algorithms, allowing organizations to validate models with clear reasoning — an important step for sectors like healthcare, finance and regulatory reporting.
Open-source explainability tools are part of a broader ecosystem that also includes widely used interpretability libraries such as AI Explainability 360 (AIX360), which offers a toolkit covering different explainability methods and metrics across data types and model types. By making such tools freely available, the open-source community enables organizations of all sizes to implement responsible AI practices without prohibitive licensing costs.
Despite the value these tools bring, the sustainability of open-source AI projects has been a concern. Many maintainers — the developers who write, update and support open-source code — often do so without direct financial compensation. Efforts to support these contributors have grown more vocal as the industry matures: maintainers frequently perform critical work that hundreds or thousands of companies and researchers rely on, yet struggle to secure steady funding for long-term development.
Platforms like GitHub Sponsors, corporate sponsorship programs and new funding frameworks are helping to change this dynamic by providing financial backing to open-source contributors. Despite progress, broader funding challenges remain, and some community leaders argue that a lack of funding could put vital open-source projects at risk — especially those focused on AI explainability, security and infrastructure.
The urgency around explainability is tied to real-world needs. As AI systems are increasingly deployed in high-stakes settings — including medical diagnostics, loan approvals, hiring systems and autonomous vehicles — stakeholders demand transparency into how and why these models make specific recommendations. Without such clarity, even highly accurate models can produce outcomes that are difficult to justify or audit. Experts believe that open, explainable tools help address both ethical and technical requirements for modern AI.
This trend is reflected in investment patterns as well. While proprietary AI systems still attract the bulk of venture capital, publicly available data shows that many startups focused on explainable and transparent AI have secured significant funding. For instance, a range of XAI-related startups — from model monitoring platforms to interpretability engines — have collectively raised hundreds of millions of dollars in recent years, underscoring growing investor confidence in responsible AI tooling.
Explainability isn’t just beneficial for enterprises; it’s also critical for developers and researchers who need to debug, improve and trust their models. Open-source libraries like Facet and AIX360 help make that possible by offering transparent interfaces for model behavior analysis — whether assessing feature importance or visualizing decision pathways. When these tools are collaboratively developed and widely shared, they democratize access to AI governance practices that would otherwise be confined to larger organizations with big budgets.
Furthermore, explainability connects with broader industry and regulatory momentum. Regions like the European Union have proposed sovereign tech funding mechanisms aimed at supporting open-source infrastructure, highlighting the strategic importance of transparent technology in national and regional digital economies.
In a world where AI systems influence more aspects of daily life, open-source explainability tools and the funding structures that sustain them are becoming essential. By improving transparency, reducing risk and supporting ethical AI deployment, explainable AI initiatives promise to play an even bigger role in how models are developed, regulated and trusted globally.
