Eavesmining: A Critical Audit of the Amazon Echo and Alexa Conditions of Use
Bibliographic record
Abstract
The emergence of smart speakers and voice-activated personal assistants (VAPAs) calls for updated scrutiny and theorization of auditory surveillance. This paper introduces the neologism and concept of “eavesmining” (eavesdropping + data mining) to characterize a mode of surveillance that operates on the edge of acoustic space and digital infrastructure. In contributing to a sonic epistemology of surveillance, I explain how eavesmining platforms and processes burrow the voice as a medium between sound and data and articulate the acoustic excavation of smart environments. The paper discusses eavesmining in relation to theories of dataveillance, the sensor society, and surveillance capitalism before outlining the potential contributions offered by a theoretical alignment with sound studies literature. The paper centers on an empirical case study of the Amazon Echo and Alexa conditions of use. By conducting a discourse analysis of Amazon’s End User Agreements (EUAs), I provide evidence in support of growing privacy and surveillance concerns produced by Amazon’s eavesmining platform that are obfuscated by the illegibility of the documents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".