AI opacity entrenches corporate power: when unaccountable systems evade democratic scrutiny
Original framing: “Can AI discriminate if it can’t justify itself?” — Financial Times
Black feminist scholars (e.g., Safiya Noble) have long documented how AI reproduces misogynoir, from search algorithms to hiring tools, yet their work is sidelined in mainstream tech discourse. Disabled activists argue that 'accessibility' in AI is often an afterthought, with systems designed for abled users first, reinforcing exclusion. Global South researchers (e.g., from the African Centre of Excellence for Information Ethics) highlight how AI’s 'universal' standards erase local contexts, prioritising Western epistemologies over diverse knowledge systems.
The Colorado lawsuit against Musk’s AI systems is not merely a legal dispute but a microcosm of how algorithmic opacity serves as a tool of neoliberal governance, where corporations evade accountability by framing discrimination as an unavoidable side effect of 'progress.
' This dynamic mirrors historical patterns of pseudoscientific racism and colonial data extraction, revealing a throughline from 19th-century craniology to 21st-century facial recognition. Indigenous epistemologies and marginalised voices offer a radical alternative: AI must be reimagined as a relational technology, accountable to communities rather than shareholders. The solution pathways—algorithmic impact assessments, data sovereignty trusts, decolonised curricula, and antitrust enforcement—are not just technical fixes but acts of epistemic justice, challenging the power structures that have long defined 'objective' knowledge. Without these interventions, AI will remain a Trojan horse for corporate and state control, deepening inequalities under the guise of neutrality.