Ideas Mined from Trustworthy Tech Dialogues

The Industry of Ideas with Dr. Julia Lane

This episode of Trustworthy Tech Dialogues tackles the urgent questions surrounding public and private investments in innovation and the transformative role of Artificial Intelligence (AI) in reshaping the economic landscape. With a spotlight on the impact of AI on labor markets and the future of work, the discussion highlights how enhanced data infrastructures can help us navigate economic uncertainties, drive smarter decision-making, and foster greater societal trust.

In Conversation:

Dr. Julia Lane offers innovative approaches to data infrastructures as a renowned statistician and economist who has brought her quantitative talents to the public sector. She is a hool of Public Service, and recently served on the Advisory Committee on Data for Evidence Building and the National AI Research Resource Task Force. She currently serves as the Secretary of Labor’s Workforce Innovation Advisory Committee and on the National Science Foundation’s Advisory Committee on Cyberinfrastructure.

Statistics for Public Service

Dr. Lane began her career in public service seeking to understand how investment impacts job training, economic productivity, worker earnings, and firm growth, but found existing survey data insufficient. To fill this gap, she developed the Longitudinal Employer-Household Dynamics (LEHD) program at the Census Bureau, creating a new data infrastructure to track workers and firms over time. This forward-thinking approach to developing foundational data infrastructure as a public good has showcased the transformative impact of strong, data-driven solutions, with the potential to reshape businesses, government functions, and society at scale. Dr. Lane’s deep focus on the strengths and opportunities from robust data infrastructure inspired her recent book, Democratizing Our Data: A Manifesto, where she reimagines public data as a tool to empower diverse stakeholders in making more informed decisions.

Data for Public Policy Decision-Making

Dr. Lane explains how high-quality data can provide meaningful information for more trustworthy decision-making in uncertain and unprecedented circumstances. She highlights the profound impact of the COVID-19 pandemic on the workforce, with unemployment claims skyrocketing from 2% to 25%. However, as she highlights in the conversation, the systems in place to manage unemployment insurance were found outdated, and many government employees lacked access to real-time data, hindering their ability to provide timely insights to policymakers.

During that period, states like Illinois, which had invested in secure data systems through initiatives like the Coleridge Initiative, were able to respond swiftly and decisively. Within days, they delivered actionable insights to workforce boards and governors, showcasing the transformative impact of timely, granular data in shaping effective public policy. Exhibits like this underscore how strategic data investments can empower rapid, informed decision-making at critical moments.

“You need an independent, trusted, data driven resource that is complemented by high quality research but driven by local needs. So, you get finely actionable information that can be used proactively, not just reactively.”

Dr. Lane has been a member of the National Artificial Intelligence Research Resource Task Force to build ‘a roadmap for standing up a national research infrastructure that would broaden access to the resources essential to artificial intelligence (AI) research and development.’ She contends that, to accomplish this, it is essential to first reconcile the conflicting predictions about AI’s future impact.

Dr. Lane highlights a significant gap in meaningful data, pointing out that current reliance on bibliometrics—such as citation counts and academic publications—fails to capture the realities of the workforce. An effective research infrastructure requires a deeper, more accurate understanding of AI’s real-world implications, beyond the narrow scope of traditional metrics. — the need for a robust understanding of the state of the economy has become particularly pertinent.

Dr. Lane articulates that the solution lies in building an infrastructure to collect data on AI adoption, workforce displacement, new job creation, and reskilling—critical insights that are knowable but currently unavailable. Preparing and designing for a better AI future requires a reliable understanding of the evolving field of nascent research and development to forecast where to invest.

Dr. Lane responds to this foundational question by conceptualizing a new form of economic organization. Going back a century, industries were classified by what they produced using systems like Standard Industrial Classification (SIC) codes to classify different industries and enable statistical analyses of businesses. Fifty years ago, as the economy shifted from goods to services, NAICS codes were introduced to reflect how things were being produced.

“Firms are organized around ideas much more than services and industry. And so, the data infrastructure needs to reflect the economic organization.”

Industry of Ideas

The with tracking research and development through the unique IDs of contracts from government funding or grants to firms or universities. Then it captures individuals and ensuing entities involved in the project—from researchers to vendors. This creates a data infrastructure that details the resources and people driving new ideas, enabling real-time insights into the scientific production process. Funding agencies would track how AI-related research moves from universities to the private sector, identifying firms where researchers are hired to drive innovation and value creation. By combining this data with job vacancy and educational records, it becomes possible to monitor workforce trends, skill demands, and the broader impact of AI on employment within those firms.

Building a Novel and Needed Institution – The Center for Data and Evidence

To advance her vision of an observable, measurable, and evaluable economy, Dr. Lane advocates for the creation of a Centre for Data and Evidence. This independent, nonpartisan think tank would prioritize research driven from the grassroots, empowering local researchers to shape and address key questions that matter most to their local economies. The Centre would bring together data from multiple sources, similar to initiatives like Institute for Research on Innovation and Science (IRIS) at the University of Michigan and the multi-state data collaboratives created by the National Association of State Workforce Agencies.

Strategic Use: Confronting the Barriers to Progress

Dr. Lane also underscores the significant risks inherent in blindly relying on data systems without addressing the challenges that can fundamentally compromise their purpose—supporting informed decision-making and advancing societal progress. She cautions that neglecting these issues can result in systems that not only fail to meet their objectives but actively obstruct the very progress they are meant to drive. To avoid these pitfalls, Dr. Lane advocates for a more strategic and integrated approach, one that acknowledges and tackles three critical challenges:

Campbell’s Law: An adage that, “the more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.”
Lag in investment in science and technology and education and value creation: It is essential to acknowledge that investments in social goods, such as education and science and technology (S&T), require significant time to yield meaningful results. Therefore, the assessment timelines should be realistic and aligned with the scale and complexity of these initiatives.
Privacy and Confidentiality: Inadequate protection of privacy and confidentiality, including the risk of exposing intellectual property and national security concerns, could erode trust and discourage knowledge sharing. To prevent this, it will be crucial to establish clear, sensible rules for sharing information about data usage that foster trust and collaboration.

Cultivating Societal Trust: The Vital Role of Data and Statistics

A robust statistical infrastructure serves as the backbone of essential decisions in both the public and private sectors, directing investments and shaping policies. In today’s data-driven world, the need to reinforce this foundation is more urgent than ever. Strengthening it is not merely an opportunity, but a critical necessity to ensure that future decision-making is informed, strategic, and capable of driving meaningful impact.