Bibliographic record
Abstract
Proceedings of the 2015 Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence in Austin, USA, 2015. Communication involves transferring information from one agent to another. An intelligent agent, either human or machine, is often able to choose to hide information in order to protect their interests. The notion of information hiding is closely linked to secrecy and dishonesty, but it also plays an important role in domains such as software engineering. In this paper, we consider the ethics of information hiding, particularly with respect to intelligent agents. In other words, we are concerned with situations that involve a human and an intelligent agent with access to different information. Is the intelligent agent justified in preventing a human user from accessing the information that they possess? This is trivially true in the case where access control systems exist. However, we are concerned with the situation where an intelligent agent is able to using a reasoning system to decide not to share information with all humans. On the other hand, we are also concerned with situations where humans hide information from machines. Are we ever under a moral obligation to share information with a computional agent? We argue that questions of this form are increasingly important now, as people are increasingly willing to divulge private information to machines with a great capacity to reason with that information and share it with others.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".