Ambient Intelligence and Problems with Inferring Desires from Behaviour
Bibliographic record
Abstract
In this paper we will argue that many of the ethical problems raised by Ambient Intelligence stems from presupposing a behaviourist conception of the relation between human desires and behaviour. Insofar as Ambient Intelligence systems take overt, natural behaviour as input, they are likely to suffer from many of the same problems that have fuelled the widespread criticism of behaviourist explanations of human behav-iour. If these limitations of the technology are not sufficiently recognized, the technology is likely to be insuf-ficiently successful in supporting the needs and desires of human users. We will focus on four distinct chal-lenges that result from this behaviourist presupposition, all of which ought to be taken into consideration at the design stage: reciprocal adaptation, bias towards isolated use, culture-specific behaviour, and inability to manually configure the system. By considering these issues, our purpose is to raise awareness of the ethical problems that can arise because of intelligent user interfaces that rely on natural, overt behaviour.
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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 | 0.040 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".