AI can accelerate insight, but the risks scale with it.
AI can unlock better insight from qualitative evidence, but only if it’s demonstrably safe, compliant, and fit for purpose.
Data Conscious helps organisations deploy and govern AI with confidence by stress-testing risk, bias, and privacy, and establishing practical controls that stand up to scrutiny.
Book a 15-minute call to see how we can strengthen your AI governance and reduce risk in real-world use.
BEFORE YOU USE AI
We developed a rapid assurance check for teams planning to use LLMs in an evaluation workflow (e.g., transcription support, translation, summarizing, qualitative analysis/coding support, drafting). Using the checklist gives you a clear view of your risk level and a short list of practical safeguards to put in place before you run any data through an LLM. It reflects the most common concerns raised by Data Conscious’ AI assurance clients, especially data sensitivity, output reliability, bias, and defensibility in public-facing findings and recommendations. It is based on a synthesis of widely used governance and sector frameworks, including GDPR, the UN Principles, the EU AI Act, NTEN, CDAC’s SAFE AI, and others. Click on the button to download for free.
AI IN RESEARCH & EVALUATION
In January 2026, Data Conscious ran a short survey on LinkedIn through our network and M&E professional groups. We gathered 124 completed responses from a mix of evaluators and commissioners within NGOs, UN agencies, research and academia, and consultancies. The results were clear: people use AI but worry about the risks. And most users are unaware of the main assurance frameworks applicable to their work. For a full overview of the survey results, and how Data Conscious can help you and your teams, click on button to download for free.
We make AI use in qualitative research safer and more defensible
We do this through data protection, clear guardrails, and documentation aligned with GDPR, UN principles, and the AI Act.
1. SCOPE & RISK TRIAGE
What we do: Clarify the AI system(s), context of use, stakeholders, and what “harm” would look like in practice.
Outputs: Scope note, risk hypotheses, priority use-case list, initial evidence requirements.
2. MAP THE SYSTEM & DATA
What we do: Document how the system works end-to-end: data sources, model(s), prompts, vendors, integrations, human review points.
Outputs: System map, data flow diagram, inventory of datasets/models/tools, accountability/RACI draft.
3. DEFINE CONTROLS & GUARDRAILS
What we do: Translate risks into concrete controls:
policies, process checks, human oversight, access controls, logging,
red-teaming, escalation paths.
Outputs: Controls checklist, governance operating
model, oversight and sign-off workflow, “minimum safe use” guardrails.
4. TEST & STRESS-TEST
What we do: Test the system against realistic
scenarios: bias/fairness concerns, hallucination and error modes,
privacy/security exposure, misuse risk, failure handling.
Outputs: Test plan, test results summary, risk
register (updated), remediation backlog.
5. DOCUMENT FOR DEFENSIBILITY
What we do: Produce the evidence trail that makes
responsible use auditable and explainable – internally and externally.
Outputs: Assurance memo, compliance mapping (as
relevant), decision log template, monitoring and incident-response plan.
6. EMBED & IMPROVE
What we do: Support rollout and continuous assurance:
training, KPI/monitoring, periodic reviews, supplier management.
Outputs: Training pack, monitoring dashboard spec,
review cadence, “assurance-as-routine” playbook.
OPTIONAL: INDEPENDENT AUDIT MODE
If you need separation between implementation and assurance, we can deliver this as an independent review with documented findings and recommendations (no system changes performed by us).
Book a 15-minute call to see how we can strengthen your AI governance and reduce risk in real-world use.