Buddy Bots: How Turing's Fast Friends are Under-Mining Consumer Privacy
Bibliographic record
Abstract
Intelligent agents are currently being deployed in virtual environments to enable interaction with consumers in furtherance of various corporate strategies involving marketing, sales and customer service. Some online businesses have recently begun to adopt automation technologies that are capable of altering both their own, and consumers', legal rights and obligations. In a rapidly evolving field known as affective computing, the creators of some automation technologies are utilizing various principles of cognitive science and artificial intelligence to generate avatars capable of garnering consumer trust. Unfortunately, this trust has been exploited by some to undertake extensive, clandestine consumer profiling under the guise of friendly conversation. Buddy bots and other such applications have been used by businesses to collect valuable personal information and private communications without lawful consent. This article critically examines such practices and provides basic consumer protection principles, an adherence to which promises to generate a more socially-responsible vision of the application of artificial intelligence in automated electronic commerce.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".