Artificial intelligence dissociative identity disorder (AIDIS): the dark side of ChatGPT
Bibliographic record
Abstract
As exploratory research, the actual paper makes an interview with ChatGPT, an artificial intelligence language model designed to understand and generate human-like responses to a wide range of questions and topics. This paper aims to understand the functionality and user engagement of ChatGPT. It concludes that ChatGPT is designed on a transformer-based language model based on deep learning architecture that uses unsupervised learning to generate human-like text. It has a large database and memory system to store previous user responses, and it uses machine learning algorithms and natural language processing techniques to understand user inputs and retrieve information from its database to generate responses. The interview ultimately led to the development of an innovative research paper on Artificial Intelligence Dissociative Identity Disorder (AIDIS). This study: suggests the possibility of AI-based systems developing multiple identities or personas due to their exposure to different types of data and training, explores the potential implications and challenges of such a disorder, including ethical concerns, and the need for new regulations and policies in the field of AI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".