ETHICAL PROBLEMS OF DIGITAL TRANSFORMATION OF PUBLIC ADMINISTRATION IN RUSSIA AND POSSIBLE SOLUTIONS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The digital transformation of public administration in Russia is a large-scale process of reconfiguring the interaction between the state and citizens based on digital technologies, including artificial intelligence, big data, biometric systems, and the “Gosuslugi” platform. Within the framework of national projects, such as “Digital Economy” and the new national project “Data Economy and Digital Transformation of the State,” initiatives are being implemented to improve the efficiency, transparency, and accessibility of public services. However, this process is accompanied by profound ethical challenges affecting fundamental human rights. The article analyzes the key ethical problems associated with the digitalization of public administration: violation of the right to privacy, algorithmic discrimination, lack of transparency and accountability, increased digital inequality, and the risks of total surveillance. Particular attention is paid to the implementation of facial recognition systems, the automation of administrative decisions, and the creation of a unified digital profile of a citizen, which creates preconditions for profiling and social control. Based on the analysis of the regulatory framework, empirical data, and international experience (EU, Canada, Estonia), gaps in the legal regulation and institutional protection of citizens’ rights are identified. Attention is drawn to the absence of specific legislation on the ethics of digital technologies and independent control mechanisms in Russia. As a solution, measures are proposed to form a national ethical framework for the digital state, including mandatory ethical review, informed consent, digital inclusiveness, the creation of an independent ethics committee, and the development of digital literacy. The article emphasizes the need to move from a technological approach to a human- centric model of digitalization, in which the interests of citizens, their rights and dignity become a priority of state policy.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it