 Additional ethical concerns have arisen surrounding the large scale data collection practices connected to machine learning and the development of artificial intelligence. For example, negative public attention have surrounded algorithms designed to infer sexual orientation from photographs and facial recognition algorithms trained on videos of transgender people. In both cases, ethical concerns have been raised about both the purpose of these algorithms and the fact that the data that trained them (dating profile photos and YouTube videos, respectively) was “public” but collected from potentially vulnerable populations without consent (Metcalf 2017; Keyes 2019). While those building AI systems cannot always control the conditions under which the data they utilize is collected, their increased use of big datasets captured from social media or related sources raises a number of concerns beyond what typically is considered part of the growing focus on AI ethics: fairness, accountability and transparency in AI can only be fully possible when data collection is achieved in a fair, ethical, and just manner (Stahl &amp; Wright 2018; Kerry 2020).