Alexa, Where Is My Private Data?: Unanswered Legal and Ethical Questions Regarding Protection and Sharing of Private Data Collected and Stored by Virtual Private Assistants
Bibliographic record
Abstract
Virtual assistants are a constant presence in our day to day life. Fast technological advancements have increased their usage and capabilities. Depending on the provider, these assistants now know our daily schedule, plan our doctor appointments, do our shopping, play our music lists, control our smart devices/houses, make phone-calls and record our conversations. However, the entire amount of private data is collected and stored on cloud, on private companies' servers, over which users of virtual assistants do not have adequate control. At the same time, the question of ownership over collected private data is still debated upon, while security of private data is entirely handled by the companies, thus putting users in considerable risk. The paper's starts from issues related to the collection and storage of personal communications through virtual assistants, as emphasized by the recent Bates case in the US, where the police sought to seize information captured by an Amazon device in connection to an alleged crime. Left without a decision due to the criminal investigation being dropped, the case unveils significant legal and ethical questions that are relevant also for the EU, regarding ownership of collected data, third party access to it (with or without a court order) and the usage of such data in relation to protection of public interests (such as prevention or solving of a crime). The paper further assesses the compatibility of virtual assistants cloud services – such as Alexa (Amazon), Siri (Apple), Cortana (Microsoft) or Home (Google) – with personal data protection requirements imposed in the European Union (EU). Based on the terms and conditions applicable to data collected and stored on cloud by virtual assistants coupled with a discussion of the problems raised by the US Bates' Echo case, the paper argues that although in appearance such services protect private data, there are still unanswered concerns regarding non-customer third parties.
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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 | 0.005 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".