Kalinda Ukanwa

After Haugen’s bombshell testimony about the harm Facebook’s algorithms enact against everyday people, there has been a groundswell of support for congressional action to reduce algorithmic harms by breaking up Big Tech -- the collective of top tech companies that run many aspects of billions of consumer lives. The notion is that breaking up Big Tech companies like Facebook, Google, Apple, and Twitter will free society from the algorithmic echo chambers that endlessly and increasingly circulate harmful content. However, breaking up Big Tech will not eradicate algorithmic harm. Why? Because virtually all algorithms operate on the previously mentioned two guiding principles: 1) optimize an objective, and 2) learn from training data how to best optimize that objective. Hence, the harrowing problems that algorithms perpetuate are not unique to algorithms deployed by Big Tech companies. Algorithms used by small companies, non-profits, and governments operate the same way. While breaking up Big Tech could temporarily reduce the scale of harmful content, doing so will not stop algorithmic bias and echo chamber facilitation in its tracks. This is because other organizations deploying algorithms will fill the vacuum. As long as algorithms, in their current design, operate in the background of daily life, people will continue to suffer from harmful and biased algorithmic outcomes.

This is conditionally accepted.

Marketing theory suggests that when groups of consumers differ in product preferences, they form unique market segments. We use this perspective to illuminate a novel antecedent to racial segregation in the school market. Specifically, the current research reveals that US Black and White parents differ in how they value school attributes. These differences in preferences for school attributes can lead these groups to select different types of schools, resulting in segregation even when the varying preferences have nothing to do with the racial demographic of the school. We theorize that these preference differences stem from motivational differences in pursuit of social status. Given the de facto US racial hierarchy that assigns Black people to a lower social status, Black parents are more motivated to seek schools that signal they can improve their children's status. Simulations of parental school decisions at scale show that preference differences under an unmitigated school choice policy lead to 10.7% more segregated schools -- the equivalent of over 6 million K-12 US children. In contrast, if Black and White parents had similar preferences, unmitigated school choice would reduce racial segregation. This research may inform public policy concerning school choice and school segregation.

If it’s not deployed wisely, artificial intelligence can turn consumers off.

This article addresses two seemingly incompatible claims about identity: (a ) complex, multivalent identities are advantageous because they afford greater flexibility versus (b) simple, focused identities are advantageous because they facilitate valuation. Following Faulkner, it is hypothesized that a focused identity is helpful in gaining entrée into an arena but subsequently leads to increasing limitations. The labor market for feature‐film actors is analyzed via career patterns recorded in the Internet Movie Database and interviews with key informants, allowing the article to distinguish between typecasting effects and those due to underlying skill differences or social networks. Important implications are drawn for research on identity formation in various social arenas, on categorical boundaries in external labor markets, and on the actor‐position interplay inherent in market dynamics.