A Whole New Meaning to Having Our Head in the Clouds: Voice Recognition Technology, the Transmission of our Oral Communications to the Cloud and the Ability of Canadian Law to Protect Us from the Dangers it Presents
Bibliographic record
Abstract
Voice recognition technology is now included in modern devices as a matter of course, being used in anything from our cellular telephones, to our televisions, and even the toys of our children. While we may voluntarily interact with some of our devices using this technology, such as conversing with Siri on our iPhones, many of us remain unaware as to the dangerous implications of using voice recognition technology.\nIts ability to record some of our most personal conversations allows private companies to eavesdrop on us in an unprecedented manner and amass highly sensitive information about our lives that would have previously been impossible. What is further pressing about this situation is that all of these recordings of our oral communications are stored in the cloud by these entities for future use and consultation, and are sometimes even transmitted to third parties. This risks exposing what may be some of our most intimate moments. Imagine if a commercial were targeted to a person’s television based on a sensitive conversation they had in the privacy of their own home. Or, even more frightening, consider if a child predator were to communicate with a child through their Barbie doll and use this connection to discover their whereabouts.\nThe levels of security and privacy available through this use of voice recognition technology are therefore questionable, and the ability of Canadian law to adequately protect us in both these arenas is even more so. I seek to examine the inherent dangers that voice recognition technology presents to its users and whether the law properly addresses each of these risks. I will begin my analysis by exploring the security and privacy infrastructures employed by some of the foremost companies offering this technology, in an effort to determine if they are sufficiently robust to protect our private information. I will then turn my analysis to an in-depth examination of Canadian privacy laws so as to ascertain whether or not they are extensive enough to safeguard us from the numerous threats posed by this technology, to both our citizens in general and our children in particular.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.061 |
| Scholarly communication | 0.026 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".