Data-gathering, governance, and algorithms : how accountable and transparent practices can mitigate algorithmic threats
Bibliographic record
Abstract
Corporate use of algorithms for marketing purposes often entails that user data is collected and processed by corporations to influence consumers online. Despite the technological efficiencies that many algorithms provide, algorithms often pose threats to human autonomy and privacy in a consumer context. While algorithms have the capacity to influence individuals and shape their behaviour, human inputs and regulations shape their functions and mandates. Regulatory measures and government legislation are also capable of shaping algorithmic functions, sometimes in ways that mitigate threats to user autonomy and privacy. Many scholars suggest that implementing practices of accountability and transparency into algorithmic regulation can mitigate the threats algorithms pose to society. This Major Research Paper will conceptualize algorithmic threats to user privacy and autonomy, as well as practices of accountability and transparency. A critical analysis of the European Union’s General Data Protection Regulation will assist in recognizing specific practices that are capable of mitigating algorithmic threats to user privacy and autonomy. The analysis and discussion of the GDPR’s potential efficacy will use mutual shaping theory to explore the role legislation plays in the co-evolution of algorithmic technology and society. Key Words: Algorithms, Data-Gathering, Privacy, Autonomy, Accountability, Transparency, General Data Protection Regulation, GDPR, European Union, Mutual Shaping Theory
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 | 0.049 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| 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".