Does algorithmic content moderation promote democratic discourse? Radical democratic critique of toxic language AI

Algorithmic content moderation is becoming a common practice employed by many social media platforms to regulate ‘toxic’ language and to promote civil and reasonable public conversations. This paper provides a normative critique of politically liberal assumption of civility embedded in algorithmic content moderation, illustrated by Google's Perspective API. From a radical democratic standpoint, this paper normatively and empirically distinguishes between incivility and intolerance because they have different implications for democratic discourse. The paper recognises the potential political, expressive, and symbolic values of incivility, especially for the movements of the socially marginalised. We, therefore, argue against regulation of incivility using AI. There are, however, good reasons to regulate hate speech but it is incumbent upon the users of AI moderation to show that this can be done reliably. The paper emphasises the importance of detecting diverse forms of hate speech that convey intolerant and exclusionary ideas without using explicitly hateful or extremely emotional wording. The paper then empirically evaluates the performance of the current algorithmic content moderation to see whether it can discern incivility and intolerance and whether it can detect diverse forms of intolerance. Empirical findings of this paper reveal that the current algorithmic content moderation does not promote democratic discourse, but rather deters it by silencing the uncivil but pro-democratic voices of the marginalised as well as by failing to detect intolerant messages whose meanings are embedded in nuances and rhetoric. We recommend that the platforms consider new algorithmic moderation that are context sensitive (e.g., taking into account an online speaker's identity in the assessment of 'toxicity') as well as in line with the theories of democracy and democratic public spheres informed by anti-racist, feminist, and other critical theorists rather than moderation based on unexamined, liberal assumptions. We also recommend that the development of algorithmic moderation should focus on the reliable and transparent identification of hate speech. If it is unable to do so, there can be no democratic grounds for adopting such a system of automatic moderation.