Suffering Without Perpetrators: The Humanitarian Passive in AI-Generated Conflict Discourse

This paper introduces the humanitarian passive as a machine-mediated syntactic pattern through which civilian suffering remains visible while responsibility becomes grammatically optional. Focusing on Palestine, Iran, and platform moderation, it defines responsibility loss as the measurable weakening of grammatical traceability between harm and responsible agency. The article proposes the Responsibility Loss Index (RLI) to evaluate whether AI-generated summaries, headlines, reports, and moderation notices preserve or erase agents responsible for violence, sanctions, restriction, censorship, or humanitarian harm. Its central contribution is to shift AI ethics from bias detection alone toward responsibility detection.  

Suffering Without Perpetrators: The Humanitarian Passive in AI-Generated Conflict Discourse

This paper introduces the humanitarian passive as a machine-mediated syntactic pattern through which civilian suffering remains visible while responsibility becomes grammatically optional. Focusing on Palestine, Iran, and platform moderation, it defines responsibility loss as the measurable weakening of grammatical traceability between harm and responsible agency. The article proposes the Responsibility Loss Index (RLI) to evaluate whether AI-generated summaries, headlines, reports, and moderation notices preserve or erase agents responsible for violence, sanctions, restriction, censorship, or humanitarian harm. Its central contribution is to shift AI ethics from bias detection alone toward responsibility detection.  

Plagiarism Ex Machina: Structural Appropriation in Large Language Models

The transformation of human-authored textual corpora into predictive generative capacity without transparent source attribution or recoverable provenance. The paper shifts the AI plagiarism debate from copying and memorization toward structural appropriation, recombinative authorship, and generative provenance. Large language models introduce a form of plagiarism that cannot be reduced to verbatim copying or copyright infringement. Their central operation is structural appropriation: the absorption, recombination, and redeployment of human intellectual labor under conditions of referential opacity and attribution collapse. Structural appropriation; Recombinative plagiarism; Referential opacity; Attribution collapse; Synthetic originality; Predictive authorship; Latent intellectual debt; Corpus parasitism; Invisible intellectual labor; Generative provenance.  

HackerNoon 2.0

Plagiarism Ex Machina: Structural Appropriation in Large Language Models

The transformation of human-authored textual corpora into predictive generative capacity without transparent source attribution or recoverable provenance. The paper shifts the AI plagiarism debate from copying and memorization toward structural appropriation, recombinative authorship, and generative provenance. Large language models introduce a form of plagiarism that cannot be reduced to verbatim copying or copyright infringement. Their central operation is structural appropriation: the absorption, recombination, and redeployment of human intellectual labor under conditions of referential opacity and attribution collapse. Structural appropriation; Recombinative plagiarism; Referential opacity; Attribution collapse; Synthetic originality; Predictive authorship; Latent intellectual debt; Corpus parasitism; Invisible intellectual labor; Generative provenance.  

Medicare ACCESS model launches AI payment revolution as tech sector misses 10-year windfall

The federal government is about to flip the switch on a massive financial engine for healthcare technology, but most of Silicon Valley hasn't even noticed the

HackerNoon 2.0

Suffering Without Perpetrators: The Humanitarian Passive in AI-Generated Conflict Discourse

This paper introduces the humanitarian passive as a machine-mediated syntactic pattern through which civilian suffering remains visible while responsibility becomes grammatically optional. Focusing on Palestine, Iran, and platform moderation, it defines responsibility loss as the measurable weakening of grammatical traceability between harm and responsible agency. The article proposes the Responsibility Loss Index (RLI) to evaluate whether AI-generated summaries, headlines, reports, and moderation notices preserve or erase agents responsible for violence, sanctions, restriction, censorship, or humanitarian harm. Its central contribution is to shift AI ethics from bias detection alone toward responsibility detection.