Assess whether AI outputs, decisions and limitations can be understood, explained and trusted by users, leaders, auditors and regulators.
Book ConsultationExplainability reviews assess whether an organisation can clearly explain how an AI system is used, what it does, what its outputs mean and where its limitations sit. This is critical where AI supports decisions, recommendations, prioritisation, classification, risk scoring or customer-facing interactions.
Effective explainability is not just a technical feature. It connects model behaviour, user understanding, documentation, decision logic, human oversight, transparency notices and governance evidence. The level of explanation required should reflect the risk, audience, context and impact of the AI system.
INTAI helps organisations review explainability across AI systems and generative AI use cases. Our approach identifies where explanations are unclear, incomplete or unsuitable for the intended audience, then defines practical improvements that support trust, accountability, audit readiness and responsible AI adoption.
Identify the AI systems, outputs, decisions, audiences and use cases where explanation is required.
Define what users, leaders, reviewers, auditors, customers or regulators need to understand and at what level of detail.
Assess model documentation, input logic, output descriptions, confidence measures, limitations, assumptions and user guidance.
Highlight unclear explanations, missing limitations, weak transparency, unsuitable language or poor alignment with decision impact.
Define practical improvements to documentation, user guidance, reporting, oversight and explanation standards.
Practical explainability outputs that show what needs to be explained, who needs to understand it, where gaps exist and how explanations should be improved.
Take the AI Assurance Assessment™ to understand how effectively your organisation can evidence, explain and challenge AI outputs, limitations and controls, and identify where stronger transparency, oversight or assurance may be needed.
Take Assessment Book ConsultationAn AI explainability review assesses whether the organisation can provide clear, appropriate and defensible explanations of how AI is used, what its outputs mean, what limitations exist and how people should interpret, challenge or act on those outputs.
Explainability supports informed decision-making, effective human oversight and accountability. Where people cannot understand an AI output, its limitations or when it should be challenged, the organisation may struggle to govern the system, identify errors or justify consequential decisions.
Different audiences need different levels of explanation. End users may need clear guidance on outputs and limitations, customers may need understandable reasons for decisions, executives may need risk and accountability information, and assurance or audit teams may require detailed evidence and traceability.
The content should reflect the system and audience, but may include the purpose of the AI, relevant factors influencing the outcome, known limitations, uncertainty, the role of human judgement, appropriate challenge routes and what should happen when an output appears unreliable.
No. Transparency concerns what information is disclosed about an AI system and its use. Explainability focuses on whether the relevant people can meaningfully understand an output, decision or limitation well enough to interpret it, question it and act appropriately.
No. Explainability should be proportionate to risk, impact and audience. A low-risk internal productivity tool may require simple guidance, while AI influencing employment, financial, healthcare, safety or customer outcomes may require more detailed explanation, evidence and governance review.
Potentially, but limited model interpretability increases the importance of other controls. Organisations may need stronger testing, outcome monitoring, documented limitations, human oversight, challenge processes and clear evidence showing why the system remains appropriate for its intended use.
Generative AI often requires a different approach because fluent outputs can create false confidence. Reviews should consider how users are informed about uncertainty, hallucinations, source quality, limitations, prompt context and the need to verify outputs before relying on them.
Explainability should be reconsidered when models, data, prompts, system configuration or intended use change, when new audiences are affected, following material incidents or complaints, or when existing explanations no longer provide sufficient understanding for effective oversight.
Start with the AI Assurance Assessment™ to understand whether your organisation can evidence, explain and challenge AI systems effectively, or book a consultation to scope a dedicated explainability review around a particular system, model or high-impact use case.
Strengthen how AI decisions are explained so users can challenge them and assurance teams can test them with confidence.
Continue strengthening AI assurance with related services covering model governance, explainability, bias and fairness, audit readiness and risk assessment.
