Reflections on Artificial Intelligence Alignment with Human Values: a phenomenological Perspective.
Bibliographic record
Abstract
The need for a systematic approach to work with artificial intelligence (AI) is current and rapidly growing. It is important that Information Systems researchers get ahead of public sentiment and be able to provide proactive commentary about the current state-of-the-art, as well as solutions for future systems. One critical question is how can we ensure value alignment between AI and human values through AI operations from design to use? For the purposes of this discussion, we adopt the phenomenological theories of material values and technological mediation to be that beginning step. In this paper, we firstly analyze the AI phenomenon from selected resources from the top IS research outlets (basket of 8 journals and 5 AI journals in IS). Secondly, we briefly present what are material values and technological mediation and reflect on the AI value alignment principle through the lenses of these theories. Supported by these new understandings and reflections, we propose to build a common principle of human values to understand the AI value alignment principle through phenomenological theories. The paper contributes the unique aspect of material values to the discourse within current AI research.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.064 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".