Beyond Regulatory Compliance: The Imperative of Stakeholder-Centric Explainability in High-Risk AI Systems

With applications in cybersecurity and finance Anchored in EU AI Act Article 13 Design Principles for Explainability Interfaces

As artificial intelligence (AI) systems become increasingly entrenched in critical infrastructure and decision-making processes, particularly in domains such as finance and cybersecurity, the imperative for transparency transcends regulatory adherence. Transparency, construed narrowly as algorithmic disclosure for compliance, is insufficient for the complex sociotechnical systems in which these models operate [1][2].

Stakeholder explainability, an evolved construct of transparency, addresses this gap by ensuring that AI-generated outputs are interpretable and actionable by diverse human actors across organizational and societal boundaries [3].

Legal Foundations: EU AI Act Article 13

“High-risk AI systems shall be designed and developed in such a way to ensure that their operation is sufficiently transparent to enable users to interpret the system’s output and use it appropriately.”— EU AI Act, Article 13

This provision underscores a functional conception of transparency. It is not merely the accessibility of internal mechanics but the extent to which end users, broadly construed, can comprehend and respond to AI outputs in contextually appropriate ways. This mandates a paradigm shift from passive disclosure to active interpretability.

Applied Necessity: Lessons from Cybersecurity and Finance

1. Cybersecurity — AI in Threat Detection and Incident Response

In Security Operations Centers (SOCs), machine learning algorithms increasingly inform anomaly detection and insider threat surveillance. These systems typically yield probabilistic or abstract "risk scores" that flag potentially malicious behavior.

Operational Impediment: In the absence of transparent rationale, human analysts frequently either disregard model outputs or over-rely on them without scrutiny—both scenarios engender systemic vulnerability.

Case Illustration:A multinational bank experienced a substantial data exfiltration incident after SOC analysts dismissed an alert generated by a machine learning model. Post-incident analysis revealed that the alert lacked sufficient explanatory context. Subsequent system redesign incorporated an explainability dashboard enumerating contributory behaviors (e.g., time-of-access deviations, anomalous file transfers). This intervention demonstrably enhanced analyst trust and investigative precision.

2. Finance — Credit Risk Modeling and Consumer Decision-Making

In consumer finance, credit scoring systems increasingly deploy opaque modeling techniques (e.g., ensemble methods, neural networks), challenging both internal oversight and customer-facing accountability.

Regulatory and Ethical Challenge: Regulatory frameworks such as the General Data Protection Regulation (GDPR) enshrine a "right to explanation," yet the operationalization of this principle remains uneven. Moreover, stakeholders such as risk auditors and customer service agents often require tailored intelligibility to uphold institutional standards.

Case Illustration:A European financial technology company integrated SHAP-based interpretability into its credit risk infrastructure. This system offered differential explainability: internal stakeholders accessed feature-level decompositions (e.g., income volatility, debt-to-income ratio), while clients received natural language justifications. The firm recorded a 22% reduction in appeals and greater audit process efficiency.

Toward Stakeholder-Centric Explainability

AI transparency must be reframed as a stakeholder-sensitive capability, in which the epistemic needs of diverse user roles are met through intentional design.

Stakeholder	Primary Concern	Explanatory Requirement
Developers	Debugging and optimization	Visualizations, statistical diagnostics
Security Analysts	Rapid triage and threat analysis	Rule tracing, causal signals
Risk and Compliance	Regulatory validation	Summary metrics, bias audits
End-users / Customers	Procedural fairness	Human-readable reasons, actionable feedback

Design Principles for Explainability Interfaces

To operationalize these insights, we propose the following architectural patterns for explainability systems:

1. Hierarchical Explanation Layers

Provide explanations across stratified levels:

• Surface level: Plain language rationale (e.g., "Application denied due to high revolving credit usage.")

• Analytical level: Feature importance visualizations, thresholds

• Technical level: SHAP values, decision paths, uncertainty quantification

  
  

2. Interactive What-If Simulators

Empower users to manipulate input variables and observe resultant changes in predictions:

“If declared monthly income increases by €300, does eligibility status change?”

3. Counterfactual Demonstrations

Present hypothetical adjustments that would have yielded a different outcome:

“Approval would have been granted had late payments decreased by two instances.”

4. Persistent Audit Trails

Maintain immutable records of model behavior, inputs, and explanatory data to enable retrospective scrutiny:

• Model metadata

• Input state at decision time

• Corresponding explanation snapshot

Conclusion

In critical domains, regulatory transparency represents the minimum threshold for responsible AI deployment. Effective governance demands systems designed not merely to disclose, but to explain intelligibly, across constituencies and use cases.

By embedding stakeholder-specific explainability as a core design criterion, organizations can reconcile compliance with usability, thereby fostering trust, improving outcomes, and mitigating institutional risk.

Opaque AI systems are not only hard to regulate, hey are difficult to defend.

References

[1] Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512

[2] Selbst, A. D., & Barocas, S. (2018). The intuitive appeal of explainable machines. Fordham Law Review, 87(3), 1085–1139.

[3] Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

Example

CreditSHAP Explainer: Overview

This app is an explainable AI (XAI) tool designed to make credit scoring decisions transparent and understandable. It uses SHAP (SHapley Additive exPlanations) values, which is a method from game theory that explains how each feature in a model contributes to its prediction.

Key Features:

1. Dataset Tab

• Dataset Upload: Allows users to upload their own credit scoring dataset or use the pre-loaded German Credit Dataset (1,000 records with credit risk information).

• Dataset Explorer: Visualizes the dataset in a table format and shows distributions of key features like credit history and loan purposes.

• Dataset Information: Provides context about the German Credit Dataset, including the number of records, features, and class distribution.

  
  

2. Prediction Tab

• Credit Score Prediction: Allows users to input customer data to generate credit risk predictions.

• Interactive Form: Users can select different values for features like credit history, loan amount, and purpose to see how they affect the prediction.

3. Technical Tab

• SHAP Dashboard: Provides technical visualizations of model explanations using SHAP values.

• Visualization Options:

  • Waterfall Plot: Shows how each feature contributes to push the model output from the base value to the final prediction.

  • Force Plot: Displays how each feature pushes the prediction higher or lower.

  • Feature Importance: Ranks features by their absolute impact on predictions.

4. Customer View Tab

• User-Friendly Explanations: Simplifies complex model decisions into easy-to-understand explanations for customers.

• Score Visualization: Shows credit scores with color-coding and meaningful status messages.

• Key Factors: Highlights the most important positive and negative factors affecting the credit decision.

• Improvement Tips: Provides actionable suggestions on how customers can improve their credit scores.

How It All Works Together:

1. The app starts with either uploaded or demo data

2. Users can explore the data and make predictions

3. The model explains its decisions using SHAP values

4. Users can view both technical explanations and simplified customer-friendly versions

   
   

The main value proposition is making "black box" credit decisions transparent and understandable for both technical users (like data scientists or loan officers) and non-technical users (like customers applying for credit).

dataset which was used for this example can be found here

Standards and Compliance Frameworks Legal & Regulatory Requirements

(Risk-Driven AI Regulation)

EU AI Act

GDPR (General Data Protection Regulation)

U.S. Fair Credit Reporting Act (FCRA)

Financial Sector-Specific Standards

ISO 38505-1 (Governance of Data in Financial Services)

OECD AI PrinciplesModel Explainability & Fairness Standards

Model Explainability and Firness Standards

 ISO/IEC 42001: Artificial Intelligence Management System (AIMS)

IEEE 7003: Algorithmic Bias Considerations

NIST AI Risk Management Framework (RMF)

Security, Ethics, and Infrastructure

 ISO/IEC 27001 & 27701

AI Ethics Guidelines from the European Commission’s High-Level Expert Group