We've read a lot lately about how artificial intelligence (AI) is changing the face of higher education.In this column, Benjamin Outar, an undergraduate student at the University of Toronto, explores how the advent of modern AI tools such as ChatGPT collided with the COVID-19 pandemic and the temporary switch to remote schooling, specifically to affect student writing.Defining his cohort as a "straddle generation" who learned the basics of writing prior to the generative AI explosion, he contrasts their attitudes towards writing with those of students just a couple of years younger-a generational shift in thinking.How has AI affected your schooling and your attitudes?Consider writing a column for Minding the Future and share your experiences.

One of the methods of increased privacy protection is the creation of synthetic data sets. In this article we consider the differences that emerge between synthetic and nonsynthetic data in one of the very few attempts to create a synthetic data file for a major household survey data set, the Survey of Income and Program Participation (SIPP). The data we use are nonsynthetic and synthetic versions of the SIPP linked to administrative earnings histories, known as the SIPP Gold Standard File (GSF) and the SIPP Synthetic Beta (SSB), respectively. We present a set of results on short-run earnings dynamics estimated on both the SSB and the GSF, focusing on earnings volatility—the variance of short-run earnings growth rates—as well as an error components decomposition of inequality into permanent and transitory components. We find that short-run instability—both volatility and the transitory component of earnings inequality—is higher in the SSB than the GSF although the differences are somewhat dependent on modeling choices and the treatment of low earnings. Differences between the two data sets emerge both because cross-sectional inequality is higher in the SSB than in the GSF and because the dynamics of earnings over both shorter and longer periods appear to be different.

Through the lens of the system of differential privacy specifications developed in Part I of a trio of articles, this second paper examines two statistical disclosure control (SDC) methods for the United States Decennial Census: the Permutation Swapping Algorithm (PSA), which is similar to the 2010 Census’s disclosure avoidance system (DAS), and the TopDown Algorithm (TDA), which was used in the 2020 DAS. To varying degrees, both methods leave unaltered some statistics of the confidential data—which are called the method’s invariants—and hence neither can be readily reconciled with differential privacy (DP), at least as it was originally conceived. Nevertheless, we establish that the PSA satisfies -DP subject to the invariants it necessarily induces, thereby showing that this traditional SDC method can in fact still be understood within our more-general system of DP specifications. By a similar modification to -zero concentrated DP, we also provide a DP specification for the TDA. Finally, as a point of comparison, we consider the counterfactual scenario in which the PSA was adopted for the 2020 Census, resulting in a reduction in the nominal privacy loss, but at the cost of releasing many more invariants. Therefore, while our results explicate the mathematical guarantees of SDC provided by the PSA, the TDA and the 2020 DAS in general, care must be taken in their translation to actual privacy protection—just as is the case for any DP deployment.

The conceptualization and operationalization of privacy protection are continuously evolving in response to advances in technology and shifts in societal values. This paper addresses a tripartite set of concerns linked to the Canadian context from the perspective of Statistics Canada: essential criteria for privacy protection models from a methodological standpoint, prevailing societal attitudes toward privacy, and potential policy frameworks to address these concerns. In the Canadian milieu, policy makers and advocates from various horizons increasingly request greater engagement as well as participative public policy dialogues on privacy protection, especially within the context of how it is applied within the Canadian National Statistical System. This paper undertakes a critical examination of evolving governance and privacy protection regimes at Statistics Canada, with a focus on where citizen engagement and policy discussions have gained notable traction. The objective is to catalyze academic and civil society discourses based on Statistics Canada’s experiences, aiming to better align the nuanced requirements of privacy protection with the practical demands of various stakeholders.