Anti-Art Narrative and Human-Machine Property Justice in Machines like Me - Xinling Zhang; Xiaohui Liang (Ilmu Inovasi Nusantara, 2026)
The novel Machines Like Me by Ian McEwan has generated diverse readings concerning artificial intelligence ethics, posthuman subjectivity, and political allegory. However, existing scholarship has largely overlooked a crucial dimension: the ethics of property justice embedded in the narrative. As AI technologies increasingly disrupt traditional labor and ownership frameworks, the novel’s depiction of property distribution crises becomes a pressing real-world concern. To address this gap, the paper systematically critiques and reconceptualizes the property relations presented in the novel, moving beyond moral intuition toward a structured theory of justice. Employing the Frankfurt School’s theory of aesthetic negativity, the analysis traces how the novel’s formal self-negation—particularly through the actions of the AI Adam and the human characters Charlie and Benn—unveils and challenges hidden injustices. The findings reveal three key movements: first, a diagnostic layer where Charlie’s labor is separated from possession, normalized by conventional morality; second, a negative moment where Adam’s act of donation shatters this concealment by benefiting “the least advantaged members of society”; and third, a constructive proposal where Benn’s tax scheme institutionalizes individual justice into an operable legal framework. Through a layered analysis of “diagnosis—concealment—negation—construction,” this paper demonstrates how aesthetic critique does not remain confined to interpretation but offers a viable pathway from literary imagination to institutional reform, providing a practical response to property injustice in the age of intelligent machines