Algorithmic Bias: The Invisible Hand Shaping Queer Content

Algorithms dictate the digital spaces where queer identities are made visible, shaping the narratives we engage with and the identities we consume. These invisible systems, presented as neutral, reflect societal biases and enforce norms that align with technocapitalist imperatives. As Timnit Gebru notes, “The lack of representation among those who have the power to build this technology” reinforces these structural inequalities. This article investigates how algorithmic bias limits queer visibility, stabilises fluid identities, and commodifies difference for profit, challenging notions of neutrality in technology.

The Illusion of Neutrality in Algorithms

Algorithms are often portrayed as impartial tools that optimise our online experiences. However, as Safiya Noble asserts in Algorithms of Oppression (2018), these systems embed the biases of their creators and the societies in which they operate. For queer communities, this manifests as algorithmic misrepresentation, where non-normative identities are either sidelined or flattened into digestible, commodifiable categories.

Instagram’s shift from an interaction-based platform to a shopping-driven model exemplifies this bias. As Andalibi and Buss (2020) argue, the platform’s algorithms prioritise profit-driven visibility over genuine interaction. Queer creators are forced to conform to marketable aesthetics, with algorithms amplifying content that aligns with capitalist expectations while suppressing expressions deemed “risky” or “controversial.”

Stabilising Queer Identities for Profit

Queerness resists fixed definitions, thriving on fluidity and instability. Yet, as Judith Butler’s theory of performativity highlights, systems of power stabilise identities to maintain control. Digital platforms employ this strategy by categorising queer identities into predictable, monetisable traits, effectively stripping them of their radical potential.

Algorithms programmed to maximise engagement reinforce normative ideals of queerness. For example, queer influencers who align with conventional beauty standards or aspirational lifestyles are amplified, while those challenging aesthetic or political norms are sidelined. As Gebru’s work emphasises, this stabilisation reflects a broader systemic issue: algorithms rely on hegemonic power structures, failing to account for the plasticity and intersectionality inherent in queerness.

The Role of Surveillance in Shaping Visibility

Surveillance is an intrinsic feature of technocapitalism. As algorithms learn from user behaviour, they categorise identities based on normative assumptions. These processes, as Galloway (2012) observes, create “cybertypes” that reduce queer bodies to stereotypes for easier commodification. For queer users, this means their visibility is often shaped by the expectations of advertisers rather than their authentic expressions.

The dangers of this surveillance extend beyond content suppression. In some cases, as highlighted by Buolamwini and Gebru’s Gender Shades study, facial recognition technologies struggle to accurately process non-binary or gender non-conforming individuals. These failures expose the limitations of algorithms designed within rigid, binary frameworks and the risks they pose to marginalised communities.

Resistance Through Disruption and Creativity

Despite these challenges, queer communities continue to resist algorithmic bias, leveraging the same platforms that surveil them to subvert norms and reclaim visibility. The concept of glitch art, as described by Shabbar (2018), offers a framework for resistance: by intentionally disrupting digital systems, queer creators expose the biases embedded within them. Manipulating media to create unexpected outcomes becomes a form of protest against the algorithms’ attempts to stabilise and commodify identities.

Posting non-conforming imagery, embracing fluid representations of gender and sexuality, or using subversive hashtags challenges the algorithms’ attempts to categorise queer bodies. These acts disrupt the colonial heteronormative frameworks embedded in technocapitalism, forcing platforms to confront the limits of their binary-driven systems.

Conclusion

Algorithmic bias is not merely a technological issue; it is a reflection of societal power structures that stabilise identities for profit while erasing diversity. By embedding hegemonic values into their systems, platforms perpetuate exclusionary practices that marginalise non-normative identities. However, through acts of resistance and the creation of alternative narratives, queer communities continue to challenge these forces, asserting the fluidity and complexity of their existence.

As Noble reminds us, algorithms are not neutral; they are “a reflection of the priorities of those who create them.” Addressing these biases requires more than technical fixes—it demands a rethinking of who holds power in the design and implementation of digital systems. Only then can we begin to create digital spaces that celebrate, rather than constrain, queer visibility.