Based on Past Experience: Highlighting Potential Human Value Issues in Domain Modelling
Bibliographic record
Abstract
In this technologically evolving era, important human values such as freedom and social responsibility are frequently overlooked in software systems, which can have significant negative social consequences as can be seen by recent examples involving Facebook or Delta Airlines. Therefore, it is important to help software developers incorporate human values considerations throughout the software development process. In this paper, we focus on domain modelling with class diagrams, an important technique for requirements engineering and early design activities. We propose a domain-specific language called HVT (Human Value Trigger) that enables the collection of human value issues including how to mitigate them. Practitioners may utilize this language to contribute more such examples to grow a catalogue of these past experiences over time. As a motivating example, we analyze the domain model of WhatsApp through the lens of Schwartz's taxonomy of human values to compile a list of issues concerning human values (i.e., model elements that may affect various human values). Furthermore as proof-of-concept, a prototype implementation addresses the need for human values to be integrated in domain models with the help of these collected past experiences by providing suggestions based on the model element type, name, and semantics based on synonyms. An analysis of eight synonym services is performed to find the optimal synonym service or combination of synonym services to use with the implementation.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".