Search for: All records

n this Article, we focus on a key dimension of commercial surveillance by data-intensive digital platforms that is too often treated as a supporting cast member instead of a star of the show: the concept of engagement. Engagement is, simply put, a measure of time, attention, and other interactions with a service. The economic logic of engagement is simple: more engagement equals more ads watched equals more revenue. Engagement is a lucrative digital business model, but it is problematic in several ways that lurk beneath the happy sloganeering of a “free” internet Our goal in this Article is to isolate engagement as a distinct and dangerous concept that should be specifically regulated. There is a benefit to seeing past the glib justificatory rhetoric and taking a hard look at engagement-based, surveillance-advertising-funded models as potentially problematic. Unfettered engagement strategies bear significant and under-appreciated costs that are endangering our privacy, our democracy, and our culture itself. It’s time that wrongful engagement, and the asserted “free” business models it generates, started to bear the burden of those costs. 

Industry will take everything it can in developing artificial intelligence (AI) systems. We will get used to it. This will be done for our benefit. Two of these things are true and one of them is a lie. It is critical that lawmakers identify them correctly. In this Essay, I argue that no matter how AI systems develop, if lawmakers do not address the dynamics of dangerous extraction, harmful normalization, and adversarial self-dealing, then AI systems will likely be used to do more harm than good. Given these inevitabilities, lawmakers will need to change their usual approach to regulating technology. Procedural approaches requiring transparency and consent will not be enough. Merely regulating use of data ignores how information collection and the affordances of tools bestow and exercise power. A better approach involves duties, design rules, defaults, and data dead ends. This layered approach will more squarely address dangerous extraction, harmful normalization, and adversarial self-dealing to better ensure that AI deployments advance the public good.