Explainability and Transparency: The Right to Understand AI Decisions

The Explainability Imperative

Explainability — the capacity to provide a meaningful account of why an AI system produced a specific output — is one of the most contested and commercially consequential dimensions of AI governance. It creates tension between the opacity of high-performing machine learning models (the ‘black box’ problem) and the legitimate expectations of the individuals those models affect, the regulators who oversee them, and the organisations accountable for their outputs.

For Caribbean enterprises, the explainability imperative is both ethical and practical. A customer denied a mortgage, a job applicant rejected by an automated screener, or an insurance claimant whose claim was flagged for investigation — all have a reasonable expectation of understanding why. And if that explanation cannot be provided, the organisation cannot defend the decision to a regulator, a tribunal, or a court.

Two Dimensions of Explainability

Governance frameworks must distinguish between two fundamentally different explainability requirements:

Explainability Type	Definition and Governance Application
Technical Explainability	The ability to understand, in technical terms, what features drove the model’s output for a specific input. Required for model developers, validators, auditors, and risk managers. Techniques include SHAP values, LIME, and attention maps for neural networks.
Communicative Explainability	The ability to provide an intelligible, accurate explanation of an AI decision to a non-technical stakeholder — a customer, employee, or regulator. Required under data protection law, consumer rights frameworks, and emerging AI regulation.

 

A system can be technically explainable without being communicatively explainable — the technical explanation may be incomprehensible to the affected individual. Effective governance requires that both requirements are met for high-risk AI systems. This is a design requirement, not an afterthought.

Regulatory Drivers of Explainability

The right to explanation for automated decisions has explicit legal grounding in several frameworks applicable to Caribbean enterprises:

• GDPR (applicable to enterprises processing data of EU residents): Article 22 provides data subjects with the right not to be subject to solely automated decisions with significant effects, and the right to obtain human intervention, express their view, and receive an explanation
• Jamaica’s Data Protection Act 2020: provides rights in relation to automated decision-making and creates obligations on data controllers to ensure meaningful human review for significant automated decisions
• EU AI Act: imposes specific transparency and explainability requirements on high-risk AI systems, including technical documentation that must be maintained and made available to regulatory authorities
• Financial services regulatory guidance across the Caribbean: regulators are increasingly requiring that algorithmic credit and risk decisions be explainable and auditable

 

Caribbean enterprises that cannot provide adequate explanations for their AI decisions face regulatory sanction, civil liability, and the reputational damage of high-profile enforcement actions. Explainability is not a nice-to-have — it is a compliance requirement.

The XAI Toolkit: Approaches to Explainability

Explainable AI (XAI) encompasses a range of techniques and approaches for producing interpretable AI systems. Key approaches include:

• Inherently interpretable models: where the risk profile permits, using model types that are intrinsically interpretable (decision trees, linear regression, rule-based systems) rather than complex neural networks. For many Caribbean financial services applications, interpretable models can achieve performance comparable to black-box alternatives.
• Post-hoc explanation methods: for complex models where interpretable alternatives are not feasible, applying post-hoc explanation techniques such as SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to produce feature importance attributions for individual predictions.
• Counterfactual explanations: generating explanations of the form ‘if X had been different, the decision would have been Y’ — particularly valuable for customer-facing communications because they provide actionable information about what the individual could change.
• Model cards and system documentation: producing standardised documentation of model purpose, performance, limitations, and appropriate use cases — making the governance context of the model explicit and accessible.

Governance of Explainability

Explainability does not happen by default — it must be built into AI governance through specific requirements and controls. Dawgen Global recommends the following governance provisions:

• Explainability is a design requirement: for any AI system classified as Tier 2, the ability to provide communicative explanations of individual decisions must be specified as a system requirement before development begins
• Explanation quality is audited: internal audit and AI assurance reviews should include testing of explanation quality — presenting specific scenarios and assessing whether the explanations produced are accurate, intelligible, and actionable
• Customer rights procedures are in place: the enterprise must have defined procedures for responding to customer requests for AI decision explanations, including trained staff, defined timelines, and escalation paths
• Regulatory examination readiness: the AI governance team must maintain documentation adequate to respond to regulatory requests for AI decision explanations within defined timeframes

Explainability is the interface between algorithmic intelligence and human accountability. An AI system that cannot explain itself cannot be governed. And an AI system that cannot be governed cannot be trusted.

Next in the Series — Article 8: AI in Financial Services: Governance Imperatives for Caribbean Banks, Insurers, and Credit Unions

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