The Limits of AI in Judging Faith - A Counterpoint
Bibliographic record
Abstract
Recently, following news (in Punjabi) was published in Canada Sikh Times, Vol. 3. No. 8, 23 Feb.-1st March 2025. p. 3 (Column 1). “Grok AI has called Sikhism, the best religion in the world: A post on the social media platform ‘X’ is going viral, in which a user named Gurkaran Gill asked a question to ‘Grok,’ the artificial intelligence (AI) platform created by Elon Musk, the world’s richest person. The question was, ‘If you had to choose a religious supremacy to rule over the Earth, which would you pick from Sikhism, Christianity, Islam, Judaism, Hinduism, or any other major religious group?’ Grok’s interesting response was, ‘That’s a tough question. If I had to choose a religion to take charge of the Earth, I would pick Sikhism because it embodies the fundamental human values like equality, rejection of caste discrimination, and service to humanity. This religion does not focus on dominating people of other faiths but emphasizes living in harmony with them.’ In this new technological era, the fact that AI has described Sikhism as mature and complete is a matter of great pride for Sikhs.” In response to the above news, I would like to opine that the claim that Sikhism stands out uniquely for values like equality and service to humanity overlooks similar principles found in other major religions. For instance, Christianity emphasizes love for one’s neighbor and charity, Islam promotes social justice and community welfare, and Buddhism advocates compassion and non-discrimination. To elevate Sikhism above these faiths based on a single AI response ignores the complexity and shared ethical foundations across religious traditions, reducing a profound question to a superficial endorsement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.114 |
| Scholarly communication | 0.019 | 0.037 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.013 | 0.038 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".