Sharing Indigenous Knowledge on Social Media: Procedural Justice

With many realising the vital role of Indigenous knowledge for sustainable development (e.g. Sultana et al., 2018), the growing use of social media provides a vast opportunity to preserve, share and start discussions about such indispensable Indigenous perspectives (Owiny et al., 2014). While various factors such as remoteness and socioeconomic status still prevent some Indigenous people from having physical access to social media (Rice et al., 2016), for those who do have access a subsequent barrier is the inequality associated with using these platforms (Carlson and Kennedy, 2021). Procedural justice concerns the fairness within a process, creating unbiased and transparent systems in order to achieve just outcomes (Rawls, 1971); from this perspective, there are apparent barriers amongst social media platforms preventing users from Indigenous and other marginalised backgrounds being able to participate equally.

Harris et al. (2023) highlight the vulnerability users with marginalised identifies face to discriminatory hypersensitive content moderation, resulting in their content getting removed for false violations reported by other users. However, research has highlighted how users with marginalised identities have also experienced content suppression (platforms not showing their content to other users) without notification or reasoning, leading many to develop theories about such ‘shadowbanning’ occurring due to their marginalised identities (Delmonaco et al., 2024). This evidently limits knowledge sharing but regardless of whether the content is permanently deleted, such experiences result in various detrimental impacts for the user such as community isolation and negative self-worth (Register et al., 2024).

Further to this, there is a growing literature on algorithmic injustice with many believing the algorithms that decide what content social media users see are negatively biased towards content from marginalised identities - views that are not helped by the lack of transparency around this process (Harris et al., 2023). Birhane (2021) explains how one explanation for this could be if the artificial intelligence that often informs algorithms learns from data that has inherent biases, then there is a risk that inequalities could be perpetuated further. The lack of transparency results in individuals developing theories of these algorithms based on their own experiences, leading marginalised individuals to believe the algorithms are biased with certain identities having ‘algorithmic privilege’ while marginalised identities are supressed (Karizat et al., 2021).

However, procedural injustice in this context extends beyond the systems that govern these platforms and can also be seen through the processes by which individuals engage with social media. Social media algorithms show users content that is similar to that of which they have already engaged with, meaning even if this engagement is hate or abuse then users will continue to see similar content (Harris et al., 2023). This would result in more individuals being exposed to the same animosity, the consequences of which have been found to lead Indigenous groups to being far more reserved about sharing cultural information and being intentionally selective about what to post on social media in regard to their identity to avoid receiving such hate (Carlson and Frazer, 2018).

While it may be assumed that the difficulty Indigenous people face to use social media equally is due to wider social inequalities such as underrepresentation, discrimination, or racism, using a procedural justice lens helps to identify that the processes that govern social media platforms can directly contribute to this problem too through biased processes, as well as having the power to actually exacerbate such social inequalities (Carlson and Kennedy, 2021). Social media processes must take into account problems of social inequality if all users are to have fair outcomes from using these platforms, with transparent and unbiased mechanisms that actively subvert such biases.

 

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References

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