The Age of Accountable AI
Have you ever thought about what happens when an AI model makes a decision that affects someone’s future but neither the person impacted nor the people deploying it can explain why?
Imagine a young woman applying for a small business loan. She meets all the visible criteria – solid credit score, steady income, no red flags. Yet, the AI system silently rejects her application. When she asks why, the bank can’t give a clear answer. The algorithm said no, and that’s all anyone knows. The young woman is left confused, and the bank is left questioning its own system, caught between technology’s potential and its ethical responsibility.
As Artificial Intelligence becomes deeply integrated and part of business operations – from automating loan approvals to screening job candidates – the stakes have never been higher. But, when decisions impact human lives with respect to various pillars of life like finances, careers or health, then in that case transparency and accountability become essential.
This growing reliance on AI has sparked urgent questions about how these systems make decisions – and who is responsible when things go wrong.
The EU AI Act is the European Union’s landmark legislation designed to regulate artificial intelligence in a way that ensures it is safe, ethical and aligned with fundamental rights. In this new regulatory era, Explainable AI (XAI) is emerging as a strategic requirement – not just for legal compliance but for building trust and long-term success.
This blog from guest author Gargi Gupta explores how XAI can serve as a bridge between innovation and regulation and why now is the time for Irish businesses to embrace it.
Understanding the EU AI Act: A Call for Responsible AI
The EU AI Act introduces a risk-based framework to regulate AI systems based on their potential impact on individuals and society. It categorises AI into four distinct risk levels:
- Unacceptable Risk: These systems are prohibited outright. Examples include AI used for social scoring or manipulative behaviour, such as exploiting vulnerable groups.
- High Risk: These are tightly regulated AI systems used in sensitive areas where decisions can significantly affect people’s lives – such as credit scoring or biometric identification.
- Limited Risk: These systems are allowed but must meet transparency obligations – such as clearly informing users when they are interacting with AI (e.g. chatbots).
- Minimal Risk: These include everyday AI tools like spam filters or AI in video games, which are largely unregulated under the Act.
Under Annex III of the EU AI Act, high-risk AI systems are defined as those used in critical areas, including:
- Biometric identification and categorisation of individuals
- Education and vocational training, such as student assessments or admissions
- Employment and workforce management, including CV screening and promotion decisions
- Access to essential services, like credit, healthcare, or social welfare eligibility
- Law enforcement, including predictive policing or determining the reliability of evidence
- Migration, asylum and border control technologies
- Administration of justice and democratic processes, such as AI tools supporting legal interpretation or court decisions
Due to their high stakes, these systems are subject to strict requirements – including risk management, human oversight, traceability and robust documentation to ensure transparency and accountability
Now that we understand how the EU AI Act classifies and regulates high-risk systems, let’s take a closer look at a key concept that can help businesses meet these requirements: Explainable AI (XAI). What exactly does it mean and why is it so important?
What is Explainable AI (XAI)?
Explainable AI (XAI) refers to a set of tools and techniques designed to make the decision-making processes of AI systems clear and understandable to humans. In an era where many AI models – particularly deep learning systems – are often described as “black boxes,” XAI plays a crucial role in bringing transparency and trust into the equation.
As defined by Adadi & Berrada (2018): “A system is explainable if its internal mechanics can be translated into human-understandable terms without compromising its predictive power.”
There are two main categories of XAI approaches:
- Intrinsic XAI: These are inherently transparent and easily interpret models, such as decision trees or linear regression.
- Post-hoc XAI: These techniques are applied after a model has been trained, helping interpret more complex models like neural networks. Common examples include SHAP, LIME and Captum.
By integrating XAI into AI systems, businesses and developers can:
- Debug and improve model performance
- Detect and address bias and fairness issues
- Provide documentation for regulatory compliance
- Foster trust among users, stakeholders, and regulators
In the context of the EU AI Act, these benefits aren’t just nice-to-have, they’re quickly becoming essential for high-risk AI applications.
Why the EU AI Act Demands Explainability (Even If It Doesn’t Say So)
While the EU AI Act doesn’t explicitly use the term Explainable AI, its requirements make explainability a necessity especially for high-risk AI systems. These obligations include:
- Justifying model decisions during conformity assessments
- Maintaining audit trails that track decision-making processes
- Enabling human oversight over AI outputs
To meet these standards, businesses must understand and interpret how their AI models work. Furthermore, the responsibility for classifying risk levels lies with the provider. This means organisations must be confident in their model’s behaviour, logic and impact making explainability essential from legal, ethical and operational perspectives.
Bridging Regulation and Business Value: Why Action is Urgent
These aren’t abstract compliance goals, they directly shape how AI should be designed, validated and deployed. For companies, this represents a shift from reactive to proactive governance.
One example is Stratyfy, a US-based fintech that uses interpretable machine learning to enhance credit risk management. Their models are built to explain decisions clearly, helping financial institutions spot biases, ensure fairness and communicate decisions confidently to regulators and customers.
Alongside such global pioneers, Ireland’s National AI Strategy also calls for building trustworthy, human-centric AI systems, a vision closely aligned with the EU AI Act. With core obligations expected to come into force by 2026, Irish businesses should begin adopting explainability practices now, not just to stay compliant but to lead the next wave of responsible AI innovation.
The Business Case for XAI: Beyond Just Compliance
Explainable AI is not only about regulatory readiness, it also delivers concrete business value across multiple dimensions:
1. Compliance and Accountability
XAI provides clear, traceable documentation for how AI systems make decisions. This is crucial in regulated sectors like finance, insurance and healthcare.
2. Trust and Transparency
Transparent decision-making builds credibility with customers, employees, partners and regulators, especially in high-stakes or sensitive use cases.
3. Risk Mitigation
XAI helps identify and correct:
- Biases in training data
- Unintended behaviours or edge cases
- Model drift in production environments
This enables organisations to address issues before they escalate into liabilities.
4. Innovation and Iteration
Explainability makes it easier for cross-functional teams including compliance officers, developers and business leaders, to understand AI performance and collaborate effectively. This accelerates model iteration, improvement and scaling.
Preparing for the Future: Embracing XAI
Real-World Applications of XAI
- Finance: Builds interpretable models to enhance fair lending and regulatory trust.
- Healthcare: XAI tools help explain why an AI model suggests a specific diagnosis or treatment.
- Insurance: Fraud detection models use XAI to validate why certain claims are flagged.
- HR Tech: Recruitment platforms apply XAI to ensure candidate scoring and hiring fairness.
Best Practices for Getting Started
- Use leading XAI libraries like SHAP, AIX360 and Captum.
- Integrate explainability early in your model development lifecycle.
- Maintain explainability documentation such as model cards and decision logs.
- Invest in AI literacy training across teams – not just for technical staff, but also compliance and business leaders.
EU AI Act Timeline for Businesses
To prepare for compliance, it’s crucial for organisations to understand when different parts of the EU AI Act come into effect. The image below outlines the official implementation timeline, as published by EU institutions.
Key Milestones
- 12 July 2024: The AI Act is published in the Official Journal of the European Union.
- 1 August 2024: The Act officially enters into force.
- 2 February 2025: The ban on prohibited AI systems takes effect.
- 2 May 2025: The AI Office begins developing Codes of Practice for General Purpose AI (GPAI).
- 2 August 2025: GPAI governance rules and national authority obligations are activated.
- 2 February 2026: Detailed guidelines for high-risk AI systems and use case classification are released.
- 2 August 2026: The AI Act becomes generally applicable – especially obligations tied to high-risk systems listed in Annex III.
- 2 August 2027: The Act applies to GPAI systems placed on the market before 2 August 2025 and certain third-party evaluated systems.
Implication for Businesses
Irish and European businesses deploying AI, particularly those in high-risk sectors, must start aligning their AI systems with these milestones now to avoid regulatory and reputational risks.
Conclusion: A Transparent Future for European AI
As AI becomes more embedded in our lives and economies, explainability is no longer a luxury – it’s a necessity. The EU AI Act isn’t just another piece of legislation – it’s a catalyst for building better, fairer, and more trustworthy AI systems.
“By embedding explainability into the AI lifecycle, we not only comply with regulation – we build AI that earns trust, scales responsibly, and creates lasting value.”
The future of AI in Ireland and Europe will be shaped not just by what AI can do, but by how clearly we can explain why it does it.
Guest Author
Gargi Gupta is a PhD researcher in Artificial Intelligence at SFI Center for Machine Learning (Technological University Dublin), specialising in Explainable AI (XAI) and regulatory compliance. Her work focuses on developing interpretable machine-learning methods aligned with the EU AI Act. She actively contributes towards AI advocacy through Women in AI Ireland.
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