Synthetic data have become inseparable from the generative AI landscape as well as our algorithmic societies more broadly. The generation and deployment of synthetic data in society still remains replete with unresolved tensions and uncertainties, especially around the relationship between synthetic data and so-called "real" data. In this article I seek to make a conceptual contribution to the social science studies of synthetic data by proposing the notion of the generative gap. Here, synthetic data are defined and understood in relation to the generative gap-the productive and ongoing tensions that machine learning and AI practitioners have to negotiate between synthetic data and real data. Moreover, the generative gap simultaneously creates the problematics of synthetic data, which is explored via two sets of tension: (1) the gap between data points and (2) the gap data distributions. Ultimately, the gap between real and synthetic data is necessary and cannot be closed; rather, the generation of synthetic data is only possible insofar as it is an ongoing and contingent play of proximities and distances, a creative tension between the "too close" and the "too far away".
Machine unlearning has emerged as a technical response to demands that AI systems remove the influence of unwanted data. Drawing on critical readings of technical literature and theories of cognitive assemblages, archives, and forgetting, in this article we examine the technical promise of machine unlearning as an ethico-political practice of epistemic reconfiguration. We identify three interwoven logics: influence mitigation through parameter reweighting, embedding-space reconfiguration, and output suppression. Across these logics, information is attenuated, relocated, or rendered inaccessible rather than made nonexistent. These operations redistribute what models can retrieve and express, with consequences not only for privacy, accountability and bias, but also deeper questions about justice, accountability and the ethics of cultural memory. We argue that machine unlearning should therefore be understood not as neutral technical deletion but as a form of world-unmaking that reorganizes knowledge and ignorance. Cultural theories of memory, archival silence, and critical unlearning help expose the political stakes of who decides what becomes reachable, suppressed, or forgotten in AI systems.
In this article, we focus on the conceptualization of Artificial Intelligence (AI) as a literary author. Although it has received consideration in other disciplines, the conceptual issues remain comparatively underexplored in literary studies. In an era that challenges the hegemony of the human, how should we posit AI as author? What possibilities, or impossibilities, could be offered by way of literary approaches to generative AI? Our exploration engages with an array of theoretical frameworks on authorship, text and textuality, readership, and interpretation. We revisit critical perspectives from the first and second halves of the twentieth century as well as more contemporary turns in literary theory to reflect on computer-generated text and ensuing interpretive methods. In doing so, we place the rethinking of authorship within a history of thought from which AI scholarship can still learn.
One of the grand challenges of the digital society is safeguarding data disclosure. Despite protective regulations, most end-users are unable to manage their individual consent properly due to the overwhelming complexity of disclosure interactions. Design research could help resolve these issues, but doing so requires a shift from siloed research perspectives and detailed recommendations on the design of consent notices toward consent models and relational design. In this article we deconstruct the solution space of consent and disclosure interactions by conducting a critical review on the literature at the intersection of ethics and society, policy management and legislation, and design and computer sciences. We synthesize existing models of consent (i.e., moral consent, protective consent, and human-centered consent) and deconstruct two critical barriers (i.e., regulatory noncompliance and consent malpractices) that need to be resolved to successfully redesign current consent practices and disclosure interactions. For undertaking new efforts of redesigning consent, we formulate a novel approach we term relational consent, along with accompanying design principles and guidance on how to redesign consent practices and disclosure interactions.
