Setting the Standard: Statistical Agencies’ Unique Role in Building Trustworthy AI

As our national statistical agencies grapple with new challenges posed by artificial intelligence (AI), many agencies face intense pressure to embrace generative AI as a way to reach new audiences and demonstrate technological relevance. However, the rush to implement generative AI applications risks undermining these agencies' fundamental role as authoritative data sources. Statistical agencies’ foundational mission—producing and disseminating high-quality, authoritative statistical information—requires a more measured approach to AI adoption.

Statistical agencies occupy a unique and vital position in our data ecosystem, entrusted with creating the reliable statistics that form the backbone of policy decisions, economic planning, and social research. The work of these agencies demands exceptional precision, transparency, and methodological rigor. Implementation of generative AI interfaces, while technologically impressive, could inadvertently compromise the very trust and accuracy that make these agencies indispensable.

While public-facing interfaces play a valuable role in democratizing access to statistical information, statistical agencies need not—and often should not—rely on generative AI to be effective in that effort. For statistical agencies, an extractive AI approach - which retrieves and presents existing information from verified databases rather than generating new content - offers a more appropriate path forward. By pulling from verified, structured datasets and providing precise, accurate responses, extractive AI systems can maintain the high standards of accuracy required while making statistical information more accessible to users who may find traditional databases overwhelming. An extractive, rather than generative,  approach allows agencies to modernize data delivery while preserving their core mission of providing reliable, verifiable statistical information.

The Role of Official Statistics in AI Development

The publication of official statistics plays a pivotal role in enabling private-sector AI development by providing high-quality, standardized training data that can validate and improve model performance. When government agencies release comprehensive datasets on demographics, economic indicators, public health metrics, and other societal measures, AI companies can train their models on reliable ground truth data – information known to be real or true when training AI models – rather than potentially biased or incomplete private sources.

High-quality and reliable data is particularly crucial for generative AI systems, which require accurate baseline information to generate truthful and contextually appropriate outputs about real-world conditions. Official statistics also create standardized benchmarks against which companies can evaluate their AI systems' accuracy and identify potential biases or gaps in performance. Beyond training and evaluation, these authoritative data sources help AI developers understand broader societal trends and needs, enabling them to build more relevant and impactful applications.

But what about the potential role of AI within the statistical agencies themselves? 

Understanding AI Approaches in Statistical Work

In the evolving landscape of AI, two distinct approaches have emerged that are particularly relevant to statistical work: generative AI and extractive AI. While both technologies offer powerful capabilities, the two approaches serve fundamentally different purposes and come with their own sets of considerations for statistical agencies and data-driven organizations.

Extractive AI: Enhanced Capabilities While Preserving Statistical Rigor

Extractive AI represents a natural evolution of traditional statistical methodologies. At its core, extractive AI technology excels at processing and analyzing existing data, drawing precise information from validated sources. Think of extractive AI as a highly sophisticated search and analysis engine that operates within clearly defined boundaries of verified information.

Key characteristics of extractive AI include:

• Operates solely with existing, validated data
• Provides clear traceability back to source information
• Maintains strict alignment with original context
• Delivers consistent, reproducible results
• Features built-in validation pathways

For statistical agencies, extractive AI's value proposition is compelling: it enhances core operations while maintaining unwavering commitment to accuracy. Whether matching datasets, cleaning data, or detecting patterns, extractive AI builds upon established statistical frameworks rather than generating new, potentially uncertain information.

Consider, for example, a public-facing statistical chatbot built on extractive AI principles. Rather than generating responses that might inadvertently misrepresent official statistics, an extractive AI system would function as a precise navigation tool through verified statistical data. When asked about unemployment trends in a specific region, the extractive AI chatbot would pull exact figures from official databases, cite sources, and offer direct links to relevant statistical tables. If users pose questions that require inference or interpretation beyond the available data, the system would clearly indicate these boundaries, maintaining transparency about what official statistics can and cannot tell us. An extractive  approach transforms complex statistical databases into accessible information while preserving the integrity and authority of official statistics.

Generative AI: Innovation with Careful Boundaries

While generative AI offers intriguing possibilities for data dissemination and user engagement, its implementation by statistical agencies—particularly in public-facing interfaces—carries significant risks to the fundamental role of statistical agencies as authoritative data sources. 

Unlike extractive AI, generative AI, as the name implies, creates synthetic information based on patterns learned from its training data. Capabilities of generative AI are particularly useful to support idea generation and internal productivity improvements within statistical agencies. Generative AI technology's tendency to generate plausible but potentially incorrect information poses a particular challenge for organizations whose reputation depends on accuracy.

For statistical agencies, generative AI's appropriate applications lie primarily in internal processes where outputs can be rigorously validated before use. Generative AI could be used by statistical agencies to support survey design or research, develop and test code, or draft internal documents, for example.

Behind-the-scenes applications leverage generative AI's capabilities, without risk of compromising public-facing products of the statistical agencies, maintaining the essential trust and accuracy that define statistical agencies' work. By contrast, public-facing generative AI interfaces may introduce uncertainty into official statistics, blurring the line between verified data and AI-generated contents and creating confusion about data reliability and provenance.

Strategic Implementation of AI for Statistical Agencies

The key to successful AI integration lies not in choosing between these approaches, but in understanding their appropriate applications. Extractive AI serves as a natural extension of statistical agencies' core mission, offering enhanced efficiency without compromising methodological integrity. Generative AI, while powerful, requires careful implementation with robust safeguards to ensure the outputs of the AI aligns with the fundamental requirement for statistical accuracy.

For statistical agencies, the optimal approach involves:

1. Leveraging extractive AI for core operational processes
2. Carefully constraining generative AI applications to appropriate use cases
3. Maintaining clear validation pathways for all AI-driven outputs
4. Establishing robust governance frameworks for both technologies

The Path Forward: Strategic Priorities for Statistical Agencies

As statistical agencies navigate the distinction between extractive and generative AI applications, the agencies need a comprehensive framework to guide implementation decisions. The Data Foundation's "Data Policy in the Age of AI: A Guide to Using Data for Artificial Intelligence" establishes a framework for responsible data use in AI systems, organized around three essential components: high quality data, effective governance principles, and technical capacity. Building on the previous analysis of appropriate AI applications in statistical work, the following recommendations provide specific guidance for statistical agencies that align with the Data Foundation's AI framework, ensuring statistical agencies maintain their position as trusted data stewards while leveraging AI capabilities effectively.

High-Quality Data Recommendations

Statistical agencies must adapt existing data quality frameworks to address the unique challenges and opportunities presented by AI implementation. Statistical agencies should:

• Establish clear data quality standards specifically for AI applications, This includes developing protocols for validating data used in AI systems and ensuring that AI implementation maintains high standards of accuracy and reliability that define official statistics.
• Create comprehensive documentation requirements for data used in AI applications, ensuring transparency about data quality, limitations, and appropriate uses. This documentation should clearly articulate how AI applications interact with statistical data and what measures are in place to maintain data integrity.

Effective Governance Principles Recommendations

Statistical agencies need to develop governance frameworks that specifically address AI implementation while maintaining their role as trusted data stewards. Statistical agencies should:

• Establish clear protocols for determining appropriate AI use cases, with particular emphasis on distinguishing between situations where extractive AI can enhance core statistical functions and where generative AI might introduce unnecessary risks. These protocols should be grounded in the agencies' statutory responsibilities and ethical obligations.
• Develop comprehensive privacy and security frameworks that account for AI's unique capabilities to process and analyze data. These frameworks should address not only traditional privacy concerns but also emerging challenges related to AI's ability to combine and analyze data in novel ways.
• Create transparency mechanisms that clearly communicate how AI is being used in statistical operations, what safeguards are in place, and how the agency ensures continued quality and reliability of its statistical products.

Technical Capacity Recommendations

Statistical agencies must build robust institutional capacity for evaluating, implementing, and managing AI systems. Statistical agencies should:

• Develop clear technical standards for AI implementation that align with their core mission of producing authoritative statistics. These standards should address both the technical infrastructure needed for AI systems and the protocols for ensuring these systems maintain statistical integrity.
• Create comprehensive training programs to build internal expertise in AI implementation and governance. This includes not only technical training but also development of expertise in evaluating AI applications and understanding their implications for statistical work.
• Establish innovation frameworks that allow for controlled experimentation with AI technologies while maintaining rigorous standards for statistical quality. These frameworks should include clear criteria for evaluating potential AI applications and mechanisms for scaling successful implementations.

Implementation Approach

The key to successful AI implementation in statistical agencies is thoughtful integration that enhances rather than compromises their core mission.

Implementation of these recommendations should follow a measured, strategic approach that allows agencies to build capability and confidence over time. Rather than rushing to adopt the latest AI technologies, agencies should focus on building a strong foundation for AI implementation that aligns with their core mission and values.

Success in implementing these recommendations requires ongoing coordination between statistical agencies, clear communication with stakeholders, and regular evaluation of progress. Agencies will need to establish metrics for measuring the effectiveness of AI implementation in their agency and create mechanisms for adjusting their approaches based on such evidence and emerging best practices.

Conclusion

Statistical agencies' enduring value in the AI age lies in their ability to provide authoritative data, ensure statistical validity, maintain data quality, and set standards for the broader AI ecosystem. By focusing on these core strengths while developing new capabilities in AI validation and governance, statistical agencies can continue to serve as the foundation for evidence-based decision-making in an increasingly AI-driven world. The role of statistical agencies becomes even more critical as AI systems become more sophisticated and widespread, highlighting the essential nature of having a trusted, authoritative source of statistical truth.

Strategic implementation of both extractive and generative AI technologies, guided by clear principles and robust validation frameworks, will ensure statistical agencies remain the bedrock of trustworthy AI while providing reliable, authoritative statistical information.

The success of statistical agencies in the AI era will be measured not just by the accuracy of their data, but by their ability to make this information accessible, understandable, and useful to all members of society while maintaining the highest standards of statistical integrity.

Disclaimer: This blog post was created with the assistance of a generative AI tool. The AI tool did not independently write or publish this post. The author takes full responsibility for the irony of that, as well as reviewing, editing, and approving the final content.