The National Data Library
Unlocking AI Potential or a Privacy Minefield?
The UK government’s proposal for a National Data Library (NDL) aims to create a centralised repository of public datasets to support AI research and innovation. The goal is to make high-quality, well-structured data available to businesses, researchers, and developers, accelerating AI advancements across multiple sectors.
But who controls the data, how is privacy protected, and will the public trust this initiative? While access to better data could drive AI progress, concerns around security, consent, and the commercialisation of public assets need to be addressed.
The value of public data – Who benefits?
✅Pros / Opportunities
Level playing field: The NDL could democratise access to high-quality data, particularly benefiting start-ups, SMEs, and academic institutions that can't afford private data sources. The Open Data Institute found that only 36% of UK businesses currently use open data, mostly large firms with the capacity to do so.
Boost to UK AI innovation: AI innovation is heavily data-driven. Access to structured datasets can significantly accelerate model training and experimentation, improving competitiveness.
Economic potential: According to McKinsey, open data could deliver £2-£5 billion in annual economic value to the UK through improved productivity and innovation.
❌ Concerns / Challenges
Unequal gains: Without fair access protocols, big tech firms may dominate usage due to greater computing power and capital, effectively monetising public data more than others.
Exploitation of public assets: If private companies profit from government-collected data (e.g., NHS), there are ethical concerns about public resources being commercialised without return.
In 2021, 91% of AI start-ups cited access to training data as a major barrier (Tech Nation AI Report). However, the NDL datasets will provide varying benefits to different sectors. Whilst some sectors, particularly public services will be well served, it is unlikely that life sciences and creative industries will have access to datasets that will make them competitive with private sector products.
Over 50% of public datasets on gov.uk are either outdated or not machine-readable (ODI audit).
? Questions to Raise ?
Will the NDL include support tools (e.g., APIs, curation, training resources) for smaller players?
Should a public data dividend model be considered (where companies benefiting from public data contribute to a communal innovation fund)?
2. Privacy and security risks – Can we protect sensitive data?
✅Pros / Opportunities
Centralised governance: A well-managed NDL could ensure consistent application of privacy frameworks (e.g., anonymisation standards, audit trails).
❌ Concerns / Challenges
GDPR compliance: The NDL must navigate strict rules on data minimisation, consent, and purpose limitation – even anonymised data can carry re-identification risk. Anonymised data can be re-identified with up to 87% accuracy using only 3 data points (Harvard study).
Cybersecurity risks: A central data repository could become a prime target for attacks (e.g., ransomware, state-level actors). The UK’s National Cyber Security Centre (NCSC) reported a 64% increase in attacks on public sector organisations between 2022 and 2023.
Public distrust: Data initiatives risk failure if citizens don’t feel safe. Trust in government data handling is still low following incidents like the NHS COVID-19 data sharing controversy. In 2023, the UK’s Information Commissioner’s Office (ICO) received over 35,000 data protection complaints – a record high. A YouGov poll (2022) found only 23% of Britons trust the government to use their personal data ethically.
? Questions to Raise ?
Will there be an independent privacy board or ethics review panel for the NDL?
What encryption and decentralisation techniques will be used to mitigate centralisation risks?
3. Balancing transparency with control
✅Pros / Opportunities
Public accountability: Transparent data governance can build trust and allow researchers and watchdogs to check for misuse or bias.
Bias mitigation: A curated library can apply frameworks to reduce algorithmic discrimination, particularly in sensitive datasets (health, policing, etc.).
❌ Concerns / Challenges
Dataset selection bias: Who decides which data is "worth" including? Lack of representation in data can skew model outcomes. 75% of AI systems audited by the Alan Turing Institute had some form of embedded bias due to poor data selection.
Licensing and monetisation: If NHS or census data is licensed to companies, it raises questions about ownership and control
Opaque partnerships: Past initiatives like Palantir’s NHS contract faced criticism for lack of transparency and inadequate public consultation.
? Questions to Raise ?
Will the NDL have public consultations or citizen juries to guide its governance?
How will datasets be validated for bias, completeness, and relevance?
Final Thoughts
The NDL has the potential to be a major AI enabler within the public sector, especially for public-good research and small-scale innovation, but only if access, fairness, and governance are addressed from the start.
Without clear ethical and regulatory frameworks, the NDL risks reinforcing existing inequalities, damaging public trust, or becoming a cybersecurity liability.
Datasets derived from public information address the needs for Large Language Models (LLMs). This is a very competitive commercial area which has received large amounts of private funding.
The realistic commercial outcome for services that make use of such data is that the market will be dominated by multinational providers, just as office applications are currently dominated by Microsoft and geographic services by Google.
The UK’s technical strengths are in scientific applications of AI, the creative arts, and heritage. Start-ups in these markets will gain no comfort from NDL initiatives.
There is no current discussion about quality of service provided by AI applications. In many cases the appropriate metrics to measure whether an LLM response is objectively comprehensive and accurate do not exist. The elimination of bias and absence of facts presented to the user is not measured at the point of delivery.
What’s Next?
In our next blog, we’ll explore whether the UK’s ambitious push to train a new generation of AI professionals is enough to bridge the growing skills gap—or if the challenge runs deeper.
