Why data quality is ultimately about people
Discussions about Artificial Intelligence often focus on algorithms and impressive demonstrations, but the true foundation of AI is quieter and more fundamental, data. The quality of data is not a purely technical concern, it is a deeply human one. Poor data can lead to unfair decisions, hidden risks, unequal access to services, and eventually a collapse of trust.
The Open Data Institute proposes a comprehensive framework for “AI-ready data”, a set of practices that ensure data is not only technically sound but also socially responsible. In other words, data that supports AI systems we can genuinely trust.
What makes data “AI-ready”?
In the ODI framework, AI-readiness is built on four interconnected components:
- technical suitability for machine learning,
- quality and consistency,
- legal and regulatory compliance,
- responsible and ethical collection.
These are not isolated checkboxes. Without transparency and responsible sourcing, technical optimisation is meaningless. Without quality and consistent standards, even the most advanced models will produce unreliable and potentially harmful outcomes.
Beyond FAIR, The need for practical guidance
The FAIR principles, Findable, Accessible, Interoperable and Reusable, have shaped the global conversation on data management. Yet for many organisations such as public bodies, NGOs, research groups or small companies, FAIR often feels too abstract. They need guidance that is concrete, operational and tuned to the realities of publishing and maintaining data.
The ODI framework builds on FAIR and follow-up initiatives such as FAIR-R, AIDRIN and Bridge2AI, but goes a step further by offering clear, usable recommendations, for example,
• adopt recognised standards such as ISO codes or domain vocabularies like SNOMED,
• maintain semantic consistency in labels and avoid ambiguous terms,
• be transparent about class imbalance and potential bias in aggregated datasets,
• publish data in ML-friendly formats such as CSVW or Parquet,
• attach machine-readable metadata that tells the full story of the dataset,
• enable API access without artificial bottlenecks such as aggressive pagination,
• support version control so that changes and updates are fully traceable.
This kind of practical clarity is the missing link in many existing frameworks, and it is essential if we want AI systems that genuinely serve the public interest.
Metadata as an act of transparency and accountability
Metadata is more than documentation, it is a public signal of integrity. Rich metadata allows AI practitioners and researchers to understand not only what the data contains, but also how it was collected, by whom and under what conditions. The ODI emphasises the need to include elements such as,
• provenance and supply-chain information,
• socioeconomic context of data workers where relevant,
• the presence of synthetic or machine-generated components,
• explicit bias statements,
• licensing terms and privacy protections.
In this way, metadata becomes a tool for ethical governance and public oversight rather than a technical appendix.
Infrastructure that respects users and supports learning
AI-ready data also depends on supportive infrastructure, user-friendly data portals, RESTful APIs, data spaces for secure sharing and robust version-control systems. These elements shape how easily developers, public servants, journalists or students can explore and understand data. When they work well, they lower barriers to meaningful participation in the digital ecosystem and make it possible for more people to contribute, critique and reuse data.
A culture shift, from publishing to stewardship
One of the strongest insights from the ODI report is that the boundary between data publishers and data users is fading. Sustainable, trustworthy AI ecosystems depend on ongoing dialogue between those who generate data and those who build systems with it. This feedback loop allows datasets to improve over time, technically, ethically and socially.
AI-ready data as a public responsibility
Preparing data for AI is not just a technical task, it is a cultural and civic commitment to transparency, responsibility and public value. AI-ready data is the backbone of trustworthy AI systems, but it is also the foundation of a healthier information ecosystem.
In an age where AI shapes everything from education to healthcare and governance, making data AI-ready is ultimately about something bigger, building a digital future that protects people, empowers communities and strengthens democracy.
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Source of this article: theodi.hacdn.io