Proposing a Postcritical AI Literacy: Why We Should Worry Less about Algorithmic Transparency and More about Citizen Empowerment

So-called artificial intelligence (AI) is infiltrating our public and communication structures. The Dutch childcare benefit scandal, revealed in 2019, demonstrates how disadvantageous the opacity of AI can be for already vulnerable groups. In its aftermath, many scholars urged for the need for more explainable AI so that decision-makers can intervene in discriminatory systems. Fostering the explainability of AI (XAI) is a good start to address the issue, but not enough to empower vulnerable groups to fully deal with its repercussions. As a canon in data and computer sciences, XAI aims to illustrate and explain complex AI via simpler models making it more accessible and ethical. The issue being that, in doing so, XAI depoliticises transparency into a remedy for algorithmic opacity, treating transparency as artificially stripped of its ideological meanings. Transparency is presented as an antidote to ideology, though I will show how this is an ideological move with consequences. For instance, it makes us focus too much on algorithmic opacity, rather than explaining the wider power of AI. Second, it hinders us from having debates on who holds the power around AI’s explanations, application or critique. The problem is that those affected by or discriminated against by AI, as in the Dutch case, have little tools to deal with the opacity of AI as a system, while those who focus on data opacity are shaping the literacy discussion. To address these concerns, I suggest moving beyond the focus on algorithmic transparency and towards a post-critical AI literacy to strengthen debates on access, empowerment, and resistance, while not dismissing XAI as a field, nor algorithmic transparency as an intention. What I challenge here is the hegemony of treating transparency as a depoliticised and algorithmic issue and viewing the explainability of AI as the sufficient path to citizen empowerment.