Why Explainable AI Matters in Critical Decision-Making?

Artificial intelligence is transforming the way decisions are made across healthcare, manufacturing, transportation, public administration, and other critical sectors. As AI systems become increasingly integrated into decision-making processes, a fundamental question arises: how can we ensure that their recommendations are reliable, transparent, and worthy of human trust?

Explainability is one of the key pillars of the TANGO Horizon Europe project, which brings together experts from academia, industry, and the public sector to advance trustworthy AI. By developing methods that improve the transparency and interpretability of AI systems, TANGO helps lay the foundation for their responsible adoption in domains where critical decisions require both accuracy and human trust.

Why Explainability Matters?

Many of today’s most powerful AI models deliver highly accurate predictions, yet often provide little insight into how those predictions are made. This lack of transparency, the so-called black-box problem, can become a significant obstacle in applications where decisions directly affect people’s lives, safety, or livelihoods.

Explainable Artificial Intelligence (XAI) addresses this challenge by enabling AI systems to communicate the reasoning behind their outputs. Rather than presenting users with a prediction alone, explainable models provide meaningful information about the factors that influenced a decision, allowing experts to validate results, identify potential biases, and make informed judgments.

Public authorities and policymakers require transparent AI systems whose recommendations can be justified, audited, and aligned with legal and ethical requirements. In healthcare, clinicians need to understand why an AI system identifies a patient as high risk before incorporating its recommendation into clinical practice. In manufacturing, engineers must be able to interpret AI-generated maintenance predictions before taking action that affects production and safety.

Trustworthy AI Requires More Than Accuracy?

Performance remains an important measure of AI systems, but accuracy alone is no longer sufficient. For AI to be widely adopted in high-impact sectors, it must also demonstrate transparency, robustness, fairness, accountability, and appropriate human oversight. These principles are increasingly reflected in the European regulatory landscape, including the EU’s approach to trustworthy AI, where explainability is recognized as a key enabler of responsible AI deployment.

Trust is not created solely through advanced algorithms—it is built when people understand how AI reaches its conclusions and remain confident that human expertise stays at the center of the decision-making process.

IVI’s Contribution to Trustworthy AI

At the Institute for Artificial Intelligence Research & Development of Serbia, researchers are developing AI methods that combine scientific excellence with real-world usability. Our work spans machine learning, explainable AI, decision-support systems, and AI governance, with applications in healthcare, smart industry, intelligent infrastructure, and public services.

Because trustworthy AI extends beyond technical challenges, our research brings together experts from computer science, engineering, social sciences, and public policy. This interdisciplinary approach helps ensure that AI systems are not only innovative but also aligned with societal needs, ethical principles, and the expectations of those who ultimately rely on them.

Within the TANGO project, IVI contributes to advancing AI technologies that place transparency, human oversight, and trust at the forefront. By collaborating with partners across Europe, we are helping develop methods and tools that support responsible AI adoption while enabling organizations to better understand, evaluate, and confidently use AI in practice.

Looking Ahead

The future of artificial intelligence will be shaped not only by increasingly capable models but also by society’s willingness to trust them. Explainability serves as a bridge between technological innovation and human confidence, making AI systems easier to understand, evaluate, and responsibly integrate into critical decision-making.