Buddhism, Consciousness, and the (Im)Possibility of Ethical AI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article explores the relevance of classical Theravāda Buddhist doctrine to the present-day development of artificial general intelligence. Specifically, it addresses the interconnected possibilities of machine consciousness and machine agency. The first section consists of a philosophical exploration of the notion of artificial consciousness in light of ordinary language considerations. This is followed by a Buddhist theoretical account of the conditions necessary for the arising of consciousness, relying in good part upon the medieval Abhidhamma commentary, the Abhidhammattha Saṅgaha. Serious doubts are raised as to whether consciousness could ever be created in a machine environment. The final section examines the possibility of machine agency in relation to Buddhist understandings of action (kamma). Here, it is argued that if conscious machines ever were developed, whatever agency they might demonstrate would be amoral in nature and reflective of the values of their corporate and state programmers. Their development would pose considerable dangers to living beings. While the main argument of the article is made in Buddhist terms, it is supported throughout by more general philosophical considerations and with reference to some of the relevant scientific literature.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it