Increasingly concerned about the way in which content spreads on the internet, scholars reach for the concept of "algorithmic amplification" (AA) as both an explanation and a warning. Although these researchers frequently acknowledge the metaphorical and conceptual haziness around the term, they continue to rely on it to carry both descriptive and normative intent. This project aims to do the foundational work to give "algorithmic amplification" conceptual precision and normative teeth. First, it resuscitates the historical context around the meaning of amplify, a transitive concept from signal processing and system dynamics. It then turns to the normative question: when is AA wrongful? A sound account of what makes amplification problematic is a necessary precondition for discussing what to do about it.

Research has found that AA may bring about disinformation, bias, and extremism. These problems, tied to content rather than process, are harmful consequences of AA, but they are not inherent. Instead, we argue that at the root of wrongful amplification is the deterioration of existing trustworthy processes for justification and legitimation. Algorithmic decision-making can disrupt or distort these processes. By shattering long-standing norms crucial for maintaining a common stock of knowledge, algorithmic amplification can undermine deliberative democracy. Therefore, we contend that algorithmic amplification is problematic when information is distributed according to processes that were arrived at through legitimate social deliberation. This project is an in-progress collaboration between myself and Helen Nissenbaum.