Responsible AI
AI you can trust. Evidence you can verify.
Recordsure uses artificial intelligence to help organisations review customer interactions more efficiently, identify risks more consistently and uncover valuable insight at scale. But AI should never become a black box.
Our approach to Responsible AI is built on transparency, human oversight, explainability and governance.
Users can see how findings have been generated, access the evidence behind those findings and apply human judgement where decisions, suitability assessments or customer outcomes require it.
Responsible AI means designing, deploying and governing AI systems in ways that are transparent, secure, explainable and accountable.
Our principles
Our Responsible AI principles
Transparency and explainability
AI findings should never appear without context.
Users can navigate directly to the conversations, records, excerpts or evidence that support an AI-generated finding. Where AI classifications, topics or conclusions are generated, Recordsure is designed to make the supporting evidence visible and accessible.
Human judgement remains central
AI can assist with analysis, prioritisation and summarisation, but human expertise remains responsible for important decisions.
For quality assurance, compliance monitoring and customer outcome reviews, AI findings are presented to reviewers who can validate, challenge or override the result before any conclusion is reached.
Use the right AI for the task
Not every problem requires generative AI.
Recordsure uses a combination of deterministic, predictive and generative techniques depending on the use case. Deterministic approaches are preferred where consistency and repeatability are critical, while generative capabilities are deployed within controlled review and approval workflows.
Continuous governance and review
Responsible AI is not achieved through one-off testing.
AI performance, accuracy, drift, security and suitability require ongoing monitoring, governance and review to ensure systems remain effective over time.
AI methodology
How we use AI
Not every problem requires the same AI approach. Recordsure uses a combination of deterministic, predictive and generative techniques depending on the use case and the level of consistency, control and oversight required.
Deterministic AI
Deterministic approaches are used where repeatable and consistent outcomes matter most.
The same inputs should produce the same outputs, allowing organisations to apply controls, validate decisions and demonstrate consistency within regulated environments.
Predictive AI
Predictive models help identify patterns, potential risks and areas that may warrant review.
These capabilities support review prioritisation and operational efficiency but remain within governance frameworks that require human validation where findings influence important decisions.
Generative AI
Generative AI can help summarise information, organise findings and improve accessibility to knowledge.
Where generative outputs are provided, users remain responsible for verifying accuracy as part of their decision-making workflow. Generated content should be reviewed and approved before being relied upon for important business decisions.
Governance and controls
How Recordsure governs and controls AI
Human-in-the-loop controls
AI should support expert judgement, not bypass it. Our products are designed around human-in-the-loop workflows that enable users to review AI findings before conclusions are reached, access the evidence behind classifications and recommendations, challenge or override AI outputs where appropriate, apply subject matter expertise to suitability and outcome assessments, and contribute feedback to improve future model performance.
Human oversight remains critical whenever customer outcomes, compliance decisions, suitability assessments or lower-confidence findings require professional judgement.
Evidence visibility and explainability
Recordsure is designed to help users understand how findings have been produced by providing visibility of the supporting evidence, relevant record sections and contributing factors behind conclusions or scores. Instead of asking users to trust the output, the platform helps them verify it.
Accuracy testing and validation
Before deployment, AI capabilities undergo testing to evaluate performance against the intended use case.
Testing helps establish whether models can produce suitable outcomes across expected variations in data and operational scenarios. Where performance does not meet required standards, models are refined, retrained or not deployed.
Bias and discrimination testing
Recordsure evaluates candidate models for potential bias and discrimination as part of model development and testing.
Where data imbalance introduces bias risk, corrective approaches may include supplementing training data, adjusting weighting or, when appropriate, removing a use case entirely if reliable outcomes cannot be achieved. We believe it is better not to generate a result than to provide one that may be inaccurate or biased.
Drift monitoring and review
AI performance can change over time because customer behaviour, business processes, products, market conditions and regulations evolve.
Recordsure recognises the importance of monitoring model drift and reviewing performance regularly. Organisations should periodically reassess AI performance to ensure models remain aligned with business objectives, customer needs and regulatory expectations.
Suitability and decision boundaries
Recordsure does not use AI to prescribe customer outcomes.
The purpose of our AI is to support review, verification, oversight and analysis rather than make autonomous suitability decisions. Human reviewers remain responsible for final judgements where outcomes may affect customers, compliance assessments or business decisions.
AI security and data protection
The security of client and customer data remains a fundamental requirement of Responsible AI.
Recordsure states that AI system management and data handling are performed within its ISO27001-certified Information Security Management System (ISMS). Security controls apply to AI technologies, infrastructure, governance and data processing practices.
Governance, accountability and ownership
Responsible AI requires clear accountability.
Recordsure states that accountability for Responsible AI sits with company leadership, supported by governance, security, product, science and technology specialists. AI systems are developed, tested and governed within established operational and information security frameworks.
Frequently asked questions
Responsible AI frequently asked questions
Yes. Recordsure may use generative AI capabilities for activities such as summarisation and interpretation of information. These outputs are intended to sit within review and approval workflows, with users responsible for verifying accuracy before relying on them.
For day-to-day quality assurance and review activities, AI findings are presented to review teams and do not determine outcomes without human supervision. Predictive checks and scores provide tested-accurate, confidence scored outputs, passing legal and regulatory scrutiny for straight-through processing of quality-control checks in BAU, as well as a range of straight-through suitability outcome determination in remedation activities.
Recordsure evaluates models for performance and bias, applies corrective techniques where appropriate and may remove a use case altogether if reliable, unbiased outcomes cannot be achieved.
Recordsure recognises that model performance can drift over time and recommends regular re-evaluation and review to maintain effectiveness and reliability.
Recordsure is designed to provide visibility of the content, records and evidence underpinning AI-generated findings, helping users review and validate outcomes before making decisions.
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Build AI oversight on trusted evidence
See how Recordsure combines artificial intelligence, human expertise and evidence-led review to deliver technology that is transparent, explainable and accountable.