Industrialized heartbreak: how generative AI enables romance fraud at scale
Dominguez Castillo, Lorena (2026) — AI and Ethics
Type:
Journal Article
Country:
Global
Synopsis (AI-Generated)
Industrialized heartbreak describes a trend in which generative AI tools amplify romance fraud by enabling scalable creation of convincing digital personas and outreach. Proponents and critics alike discuss how text-based chatbots, synthetic images, and deepfakes can be deployed to simulate intimacy, solicit funds, or misrepresent identity. At scale, operators can manage many concurrent profiles, automate initial contact, sustain long-running conversations, and tailor messaging to perceived vulnerabilities. The topic sits at the intersection of technology capabilities, social dynamics, and the ethics of deception in online relationships. The landscape encompasses modalities of deception, workflows of fraud operations, and measurement of impact. Fraudulent actors may exploit trust, loneliness, or financial incentives, raise concerns about user safety, financial security, and digital
Identified Gaps (AI-Generated)
The article identifies gaps in AI ethics frameworks and platform governance that neglect interpersonal AI-enabled exploitation. It notes that harms within relationships are often treated as private misfortune and not prioritized in regulation, while safety testing targets high-salience harms. It argues for a formal harm taxonomy including romance fraud and AI-facilitated abuse, enhanced fraud-training content, and robust detection of AI-generated content on platforms. Governance failures are attributed to divided responsibility among AI developers, social-media platforms, and dating services, weak accountability, under-reporting, and a lack of political constituencies to advocate for victims. An attention gap persists due to demographic targeting and stigma around reporting.
Methods (AI-Generated)
This correspondence synthesizes empirical findings (e.g., Gressel et al.’s large-scale experiment showing 46% compliance for LLM-powered scam agents vs 18% for humans) with real-world loss data (FBI/FTC figures) and technology-trade observations on deepfakes, voice cloning, and AI-generated imagery. It identifies two converging operational models (West African Yahoo Boys and Southeast Asian compounds) and describes how LLMs preserve narrative consistency, adapt tone, and sustain dozens to hundreds of simultaneous deceptions. It further analyzes platform governance failures, safety-filter gaps, and the proliferation of open training materials, arguing for intervention through governance reforms, fraud-content controls, and enhanced detection across social and dating platforms.
Limitations (AI-Generated)
The analysis relies on secondary sources and incident reports rather than primary victim data; figures reflect reported losses and may undercount true harm. It synthesizes studies (e.g., one major experiment on LLM-based romance baiting) that may not generalize across cultures or languages. The article acknowledges reporting biases, under-reporting by victims, and the challenge of measuring psychological harm and platform harms that are private or diffuse. It also notes safety-filter testing is incomplete, with limited visibility into open training materials and real-world abuse patterns.
Future Work (AI-Generated)
Develop formal harm taxonomy for AI-enabled interpersonal abuse; study efficacy of platform-level interventions; design and evaluate fraud-detection and AI-generated-content moderation across social and dating platforms; align with EU AI Act risk-based regulation; build victim-support and reporting mechanisms; investigate cross-border enforcement and crypto-laundering links; monitor adoption of defense measures and changes in attacker tactics; foster victim-centered governance with stakeholder coalitions.
AI-Generated Content Notice
The synopsis and research notes on this page were generated with AI from available publication information and, when available, the uploaded paper text. They may contain errors, omissions, or interpretation issues. Readers should follow the DOI or source link, review the original publication, and make their own judgment about the content.