This essay contends that the ethics around the use of spy technology to gather intelligence (TECHINT) during espionage and counterintelligence operations is ambiguous. To build this argument, the essay critically scrutinizes Cécile Fabre's recent and excellent book Spying through a Glass Darkly, which argues that there are no ethical differences between the use of human intelligence (HUMINT) obtained from or by human assets and TECHINT in these operations. As the essay explains, Fabre arrives at this position by treating TECHINT as a like-for-like replacement for HUMINT. The essay argues instead that TECHINT is unlikely to act as a like-for-like replacement for HUMINT. As such, TECHINT might transform existing practices of espionage and counterintelligence, giving rise to new ethical challenges not captured in Fabre's analysis. To illustrate the point, the essay builds an analogy between TECHINT and recent armed conflicts in which precision weapons have been deployed. Although precision weapons seem ethically desirable, their availability has created new practices of waging war that are ethically problematic. By analogy, TECHINT, though not intrinsically undesirable, has the capacity to generate new practices of intelligence gathering that are ethically problematic—potentially more than HUMINT. Ultimately, recent negative experiences with the use of precision weaponry should caution against an overly positive assessment of TECHINT's ethical desirability.

Transitional justice refers to the process of dealing with widespread wrongdoing characteristically committed during the course of conflict and/or repression. Examples of such processes include criminal trials, truth commissions, reparations, and memorials. Technology is altering the forms that widespread wrongdoing takes. Technology is also altering the form of processes of transitional justice themselves. This essay provides a map of these changes and their normative implications.

In this essay, I explore ethical considerations that might arise from the use of collaborative filtering algorithms on dating apps. Collaborative filtering algorithms can predict the preferences of a target user by looking at the past behavior of similar users. By recommending products through this process, they can influence the news we read, the movies we watch, and more. They are extremely powerful and effective on platforms like Amazon and Google. Recommender systems on dating apps are likely to group people by race, since they exhibit similar patterns of behavior: users on dating platforms seem to segregate themselves based on race, exclude certain races from romantic and sexual consideration (except their own), and generally show a preference for white men and women. As collaborative filtering algorithms learn from these patterns to predict preferences and build recommendations, they can homogenize the behavior of dating app users and exacerbate biased sexual and romantic behavior.