LEGOS-SLEEC: Tool for Formalizing and Analyzing Normative Requirements
Bibliographic record
Abstract
Systems interacting with humans, such as assistive robots or chatbots, are increasingly integrated into our society. To prevent these systems from causing social, legal, ethical, empathetic, or cultural (SLEEC) harms, normative requirements specify the permissible range of their behaviors. These requirements encompass both functional and non-functional aspects and are defined with respect to time. Typically, these requirements are specified by stakeholders from a broad range of fields, such as lawyers, ethicists, or philosophers, who may lack technical expertise. Because such stakeholders often have different goals, responsibilities, and objectives, ensuring that these requirements are well-formed is crucial. SLEEC DSL, a domain-specific language resembling natural language, has been developed to formalize these requirements as SLEEC rules. In this paper, we present LEGOS-SLEEC, a tool designed to support interdisciplinary stakeholders in specifying normative requirements as SLEEC rules, and in analyzing and debugging their well-formedness. LEGOS-SLEEC is built using four previously published components, which have been shown to be effective and usable across nine case studies. Reflecting on this experience, we have significantly improved the user interface of LEGOS-SLEEC and its diagnostic support, and demonstrate the effectiveness of these improvements using four interdisciplinary stakeholders. Showcase video URL is https://youtu.be/LLaBLGxSi8A
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".