Tsehaye Haidemariam
Biographic Data
| ID | 9294106 |
|---|---|
| NAME | Tsehaye Haidemariam |
| GIVEN NAMES | Tsehaye |
| FAMILY NAME | Haidemariam |
| SIGNATURE | HAIDEMARIAM T |
| AFFILIATIONS | BI Norwegian Business School |
| ORCID | 0009-0007-1066-6374 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2026 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Digital parasitism
This article investigates the intersection of generative Artificial Intelligence (AI) and the book industry, focusing on the use of copyrighted literary works for training large language models (LLMs). As LLMs require enormous volumes of content for training, published books and other media have become valuable sources of data. Many training datasets appear to have been assembled without authorization or transparency, raising acute legal, ethical…
After generative AI
Generative artificial intelligence is transforming how creativity is organised, attributed, and governed across cultural and creative industries. This study examines how generative systems redistribute creative functions within human-led workflows by comparing AI-generated outputs with live human performances across visual (VNAI) and performative (PEAI) domains. Focusing on systems such as Stable Diffusion and OpenAI’s Sora, the analysis adopts a…
On the problems of training generative AI
This study examines the ethical, legal, and copyright challenges in training generative AI on a large-scale text dataset, using Books3 as a case study. This dataset, used for training foundation models such as GPT, BERT, Meta’s Llama, and StableLM includes pirated works by nearly 200,000 authors from various countries, raising concerns about intellectual property rights, dataset integrity, and transparency. Our analysis of the initial 99 ISBNs re…
No prominent works on this page.
Digital parasitism
This article investigates the intersection of generative Artificial Intelligence (AI) and the book industry, focusing on the use of copyrighted literary works for training large language models (LLMs). As LLMs require enormous volumes of content for training, published books and other media have become valuable sources of data. Many training datasets appear to have been assembled without authorization or transparency, raising acute legal, ethical…
After generative AI
Generative artificial intelligence is transforming how creativity is organised, attributed, and governed across cultural and creative industries. This study examines how generative systems redistribute creative functions within human-led workflows by comparing AI-generated outputs with live human performances across visual (VNAI) and performative (PEAI) domains. Focusing on systems such as Stable Diffusion and OpenAI’s Sora, the analysis adopts a…
On the problems of training generative AI
This study examines the ethical, legal, and copyright challenges in training generative AI on a large-scale text dataset, using Books3 as a case study. This dataset, used for training foundation models such as GPT, BERT, Meta’s Llama, and StableLM includes pirated works by nearly 200,000 authors from various countries, raising concerns about intellectual property rights, dataset integrity, and transparency. Our analysis of the initial 99 ISBNs re…
Ethics and Social Impacts of AI (3 works) · Generative grammar (3 works) · Performing arts (2 works) · Artificial Intelligence (1 work) · Artificial Intelligence in Education (1 work) · Big Data Technologies and Applications (1 work) · Computer Science (1 work) · Corporate governance (1 work) · Cultural diversity (1 work) · Digital Humanities and Scholarship (1 work)