No Anchor, No Trust: Inference Journalism and the Empathy–Ethics Paradox in AI Nonfiction

Katharina Bötel-Azzinnaro
Doctor in Philosophy, University of Surrey, UK
Centre for Creative Arts and Technologies,
Department of Music and Media, Faculty of Arts, Business and Social Sciences,
Guildford, Surrey GU2 7XH, U.K.
September 2025
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TL;DR: This PhD thesis investigates how audiences evaluate trust, credibility, emotion, and ethical acceptability in nonfiction films when traditional indexical imagery is contrasted with AI-generated synthetic visuals. It introduces the concept of Inference Journalism as a transparent genre label for AI-based reconstruction in nonfiction media.

  • Examines how the implicit contract of nonfiction $$\text{(that what is shown has an indexical link to reality)}$$ is destabilised as synthetic media enters documentary and journalism.
  • Compares audience responses to a conventionally filmed documentary and a synthetic version generated using machine learning models, without disclosing which version they see.
  • Uses a mixed-methods design: quantitative surveys analysed in Survey Monkey and Excel, and qualitative thematic coding in NVivo across six thematic areas.
  • Identifies indexical anchors as central to trust: tolerated flaws in real footage are read as authenticity, while anomalies in synthetic material are often read as unreality.
  • Develops the empathy–ethics paradox: AI reconstructions of a deceased individual can heighten emotional closeness while simultaneously provoking ethical discomfort.
  • Shows that ethical boundaries are drawn more firmly around people than places: recreating environments with AI is more acceptable (if transparent) than recreating deceased individuals.
  • Proposes Inference Journalism as a professional genre term: the transparent use of AI/ML to infer and reconstruct places, events, or people from real-world anchors such as photographs, recordings, or data traces.

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If you would like access to additional materials such as film excerpts, visual examples of synthetic reconstructions, or interview instruments, please contact the author directly.

Abstract

This thesis examines how audiences evaluate trust, credibility, emotions, and ethical acceptability in nonfiction films when comparing traditional indexical imagery with AI-generated synthetic visuals. The implicit contract of nonfiction, that what is shown has an indexical link to reality, is increasingly unsettled by AI. As synthetic media enters documentary and journalism, a pressing question arises: how do audiences evaluate trust and authenticity when the anchor of recording is replaced by machine inference?

Two short films were produced for the study: a real documentary constructed from conventional footage and a synthetic version generated using machine learning models. Participants did not know whether the film they viewed was real or synthetic, ensuring that their responses were shaped by the viewing experience itself rather than by disclosure. A mixed-methods design was employed. Quantitative survey data were analysed in Survey Monkey and Excel, while qualitative open-text responses were coded and thematically analysed in NVivo. The analysis was organised into six thematic areas: educational value and perceived reliability; emotional response and empathy; perception of ethical boundaries; realism and visual impact; trust and credibility; and viewer awareness and interpretive frames.

Findings highlight three overarching contributions. First, indexical anchors remain central to trust: technical flaws in the real film were tolerated as signs of authenticity, whereas anomalies in the synthetic film were interpreted as evidence of unreality. Second, in the AI-generated film, two deliberately designed reconstructions of a deceased individual produced heightened emotional closeness while also provoking strong ethical discomfort, a tension conceptualised here as the empathy–ethics paradox and one of the study’s most significant findings. Third, ethical boundaries were drawn more firmly around people than places: while AI use to recreate environments was broadly accepted if transparent, recreating deceased individuals was largely rejected.

Building on these insights, the study proposes Inference Journalism as a new professional genre term for nonfiction storytelling. Defined as the transparent use of AI/ML to infer and reconstruct places, events, or people from real-world anchors such as photographs, recordings, or data traces, Inference Journalism frames synthetic material as reconstruction rather than deception. By offering audiences a clear interpretive category, this genre proposal seeks to stabilise expectations and support ethical, accountable uses of AI in nonfiction media.

BibTeX

@phdthesis{BoetelAzzinnaro2025Thesis,
  author    = {Katharina B{\"o}tel-Azzinnaro},
  title     = {No Anchor, No Trust: Inference Journalism and the Empathy--Ethics Paradox in AI Nonfiction},
  school    = {University of Surrey},
  year      = {2025},
  month     = {September},
  address   = {Guildford, United Kingdom},
  note      = {Doctor in Philosophy, Centre for Creative Arts and Technologies, Department of Music and Media, Faculty of Arts, Business and Social Sciences}
}