Roshaan E Ali<\/p>\n
Pakistan is building a framework to govern, protect and make better use of its data. But who is assessing whether that data actually represents Pakistan?
\nThe Ministry of IT and Telecommunication has released the draft of the National Data Governance Policy 2026, while the National Artificial Intelligence Policy was approved in July 2025. The policies recognise the importance of data standards, quality and inclusion towards a digital economy and society.
\nThe unresolved question, however, is what mechanism will ensure that the datasets determining consequential AI systems are sufficiently representative of the people and realities they are intended to serve. Terms such as \u201cfairness\u201d, \u201cethical\u201d and \u201cinclusive\u201d risk becoming little more than politically correct buzzwords devoid of accountability unless they are supported by clear standards and a defined mechanism for assessing compliance.
\nAI systems depend heavily on the data they use and are trained on, and there are consequences if we fail to address the practical implications of the fundamental gaps and limitations within our datasets. Neither policy clearly establishes measurable benchmarks or a mandatory review mechanism to determine whether datasets are sufficiently representative and fit for consequential decision-making.
\nIf data misses important dimensions of representation, such as populations from certain provinces, languages or forms of economic activity, among others, those omissions can become embedded in the systems built on it. The quality and representativeness of data can therefore directly shape how accurately these systems understand, respond to and serve the people and realities they are designed to address.
\nExisting issues in datasets already show why these questions matter. A 2025 World Bank assessment of Pakistan\u2019s National Socio-Economic Registry (NSER), one of the country\u2019s most important databases for identifying vulnerable households, found that while 84 percent of households were registered, approximately 2.2 million households among the poorest 40 percent remained excluded.
\nBeing excluded from the registry has previously meant being excluded from income support, disaster relief, mother and child nutrition programmes and education stipends. The assessment also identified disparities in coverage across provinces, barriers to registration, and concerns around outdated information and data accuracy.
\nWithout oversight, if such data is analysed by AI systems or used to inform AI-driven decision-making, these gaps could potentially be automated and negatively impact populations, particularly in underserved areas and informal sectors of the economy. Citizens whose needs and circumstances are not comprehensively captured in digital records may be invisible to systems shaping access to healthcare, education and other government services.
\nSimilarly, policies based on recorded economic activity may disproportionately reflect formally documented businesses while overlooking, for example, a woman running an unregistered home-based business. This risks further marginalising citizens and economic activity that are already underrepresented in national datasets.
\nThis also creates a risk of a negative feedback loop where populations and segments that are better documented receive better-targeted services and policies, which generates even more data about them, whereas those who are poorly documented receive less, remain underrepresented, and become even harder for data-driven systems to see.
\nWith AI, incomplete national datasets will translate into socio-economic inequalities faster than we think.
\nWithout definitive benchmarks for assessing whether datasets are sufficiently representative and fit for their intended use, we risk making the already underserved and underrepresented even less visible. What lies ahead is the task of deliberating on creating equitable and measurable national standards for data collection and data quality, including clear measures of coverage, completeness, accuracy, timeliness and representativeness that reflect Pakistan\u2019s geographic, political and socioeconomic realities.
\nPakistan can set a stronger precedent in AI and data governance by establishing an accountable mechanism to assess and address gaps in representation before they are embedded in consequential systems. The policies should also ensure that the public and private sectors cannot deploy high-impact AI systems\u2014which can affect millions of lives\u2014without demonstrating that the data on which they rely or use meets clearly defined standards appropriate to their purpose.
\nA country can build excellent systems, but with incomplete data, it will still produce incomplete governance.<\/p>\n
The writer is a graduate of Northwestern University with a BSc in Communication and Media Studies. She can be reached on LinkedIn.<\/p>\n","protected":false},"excerpt":{"rendered":"
Roshaan E Ali Pakistan is building a framework to govern, protect and make better use of its data. But who is assessing whether that data actually represents Pakistan? The Ministry of IT and Telecommunication has released the draft of the National Data Governance Policy 2026, while the National Artificial Intelligence Policy was approved in July […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10],"tags":[13912],"class_list":["post-566447","post","type-post","status-publish","format-standard","category-article","tag-roshaan-e-ali"],"yoast_head":"\n