Can Algorithms be Racist?

As artificial intelligence (A.I.) continues to rapidly integrate within everyday life, there are a few ethical dilemmas that have arisen synchronously and their impact on use cases have become the subject of much debate (Kilbertus et al., 2017; Hardt et al., 2016; Pazzanese, 2020). One such predicament that this paper hinges on has to do with inclusivity and marginalization (Bender et al., 2021). How are notions of participation affected by training data that reinforce hegemonic power in the formation of algorithmic models? Accordingly, this article will seek to spotlight ethical challenges within A.I. via a grounded interpretivist viewpoint gained by qualitatively investigating the literature in order to discuss bias amplifications. As outlined by Bender et al., (2021), there are several juristic and social dilemmas regarding the growth and utilization of language models.