As noted in the previous section, the Evidence Act makes it clear that agencies must define clear user needs and public benefits, the use of minimally intrusive data and tools, and access to data to ensure accountability and transparency in methods, while also ensuring security (1). The Act also emphasizes that access is central to developing trust in how data are combined, identifying coverage issues – especially for underrepresented or vulnerable groups, particularly in the absence of common identifiers and usable metadata. Transparency and accountability are particularly critical in this context.

Paul Romer, the 2018 Nobel Laureate in Economics summarizes the importance of openness with great clarity.

"PR: We must promote open and transparent information sharing, including data production, and finance it. We need to change the incentive structure for information from one driven by pure profit to one driven by something more amenable to the academic model (people doing research because they are interested in sharing ideas). We should de-privatize the information industry—even go as far as putting Google out of business so that we can reduce concentrations of economic and political power as a result." (2)

In his view, data are now and will be the mechanism whereby firms keep a proprietary market and proprietary advantage. If you force disclosure of data, the rate of productivity growth is higher because firms are able to learn from each other: publicly available information is extremely valuable for the public good.

In keeping with this vision, ACDEB report’s Recommendation 1.6 suggests adopting a risk-utility framework as the basis for standards on sensitivity levels, access tiers, and risk evaluations (see Figure 1). The report recommends that the risk and utility of data access should be separately measured but the tradeoff should be jointly determined (3).

Figure 1: Source: https://www.bea.gov/system/files/2022-10/acdeb-year-2-report.pdf

Navigating the intricate balance between privacy and utility has historically been a point of contention in the realm of data science and public policy. However, the two concepts do not need to be fundamentally antagonistic. Instead, privacy and utility can, and should, coexist harmoniously. It is not easy to craft policies that ensure data privacy, and also harness the power of data-driven insights. Creating a framework that both respects individual privacy and champions the broader societal benefits of data usage requires ongoing collaboration between policymakers, technologists, and the general public. Such a framework acknowledges that privacy is not an end in itself (3,4). The Advisory Committee highlighted the Five Safes - safe projects, safe people, safe settings, safe data and safe export - as one successful approach (1; p29). The utility of data access stems from making sure that any project meets a societal need (a safe project), and that the only people who access the data are approved and only access the data to meet that societal need (safe people). The privacy protection stems from restricted access in a secure environment (safe settings), deidentified or hashed entity level data (safe data), and disclosure limitation methods that minimize the risk of reidentifying information about individual entities. (1)

References:

1. Advisory Committee on Data for Evidence Building. Advisory Committee on Data for Evidence Building: Year 2 Report Washington DC. 2022 Recommendations 1.2-1.9.

2. Romer, P., & Lane, J. (2022). Interview With Paul Romer. Harvard Data Science Review, 4(2). https://doi.org/10.1162/99608f92.cc0da717

3. Hand DJ. Aspects of data ethics in a changing world: Where are we now? Big data. 2018;6(3):176-90.

4. Hand DJ, Babb P, Zhang L-C, Allin P, Wallgren A, Wallgren B, et al. Statistical challenges of administrative and transaction data. Journal of the Royal Statistical Society Series A (Statistics in Society). 2018;181(3):555-605.