Being recognized as human by AI: algorithmic disrespect and the crisis of recognition in the digital age

Artificial intelligence increasingly does more than identify individuals; in many institutional contexts, it helps determine who is seen, trusted, and included. This paper argues that algorithmic systems increasingly participate in practices of social recognition, and that their failures can become forms of structural disrespect when classifications acquire institutional authority and shape how persons are treated. Drawing from Hegelian and Honnethian recognition theory, the paper introduces the concept of algorithmic disrespect, manifesting across three dimensions: embodied (when certain bodies are excluded from visibility), agential (when predictive models preempt human action), and symbolic (when value is indexed through computational relevance). These mechanisms indicate a crisis in the social grammar of recognition, where data replaces dialogue and visibility becomes a tool of both inclusion and domination. Rather than treating algorithmic bias as a problem of fairness alone, the study reframes it as a philosophical and political issue, rooted in how AI systems mediate identity, agency, and esteem. The paper concludes by proposing a relational ethics of recognition grounded in justification, accountability, and institutional responsiveness, shifting attention from model-level corrections to the infrastructural conditions under which recognition is distributed. In doing so, it repositions recognition as both a site of harm and a potential locus of democratic struggle in algorithmic society.