LEGAL AND ETHICAL PERSPECTIVES ON (BIG) DATA, PLATFORMS, AI AND ALGORITHMS
Bibliographic record
Abstract
AoIR and the Journal of Information, Communication and Ethics in Society (JICES) share common interests in critical reflection on the ethical and social dimensions of the internet and internet-facilitated communication, and have begun a collaboration aimed at collecting ethically-focused AoIR conference submissions for presentation and critique at AoIR, with a view towards subsequent publication in a special issue of JICES. This panel collects four papers exploring especially the legal and ethical dimensions of new technologies, including data collection and storage as public goods vis-à-vis central questions of justice (Paper 1, Towards a Political Theory of Data Justice: A Public Good Perspective); critiques from Western and non-Western positions of the utilitarianism otherwise driving the platforms’ business models and rationales (Paper 2, Google and Facebook VS Rawls and Lao-Tsu: How Silicon Valley’s utilitarianism and Confucianism are bad for Internet ethics); basic tensions between the rule of law vis-à-vis algorithmic “decision-making” processes in jurisprudence and “surveillance capitalism” (Paper 3, The Jurisprudence of Datafied Law); and a taxonomy of the ethics of AI, algorithms and big data based on an analysis of 90 guidelines from 2017-2020 (Paper 4, A systematic literature Review of ethical Code of Conducts in the field of Internet Research). These papers directly take up the central interests shared between AoIR and JICES in the ethical and social dimensions of the internet and internet-facilitated communication. They offer new insight on legal and ethical aspects of contemporary technologies, some of which will have specific implications for internet research ethics.
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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 | 0.148 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.014 | 0.143 |
| Scholarly communication | 0.040 | 0.029 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.024 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 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".