LEGOS-SLEEC: Tool for Formalizing and Analyzing Normative Requirements

TL;DR

This work addresses the challenge of eliciting and validating normative, time-sensitive requirements (SLEEC rules) from interdisciplinary stakeholders. It introduces LEGOS-SLEEC, an IDE that combines rule elicitation with automated well-formedness analysis via translation to formal logic and satisfiability checking, augmented by improved diagnostics. The main contributions include a preliminary study, the LEGOS-SLEEC IDE with enhanced diagnostic support, an evaluation across four real-world case studies, and a demonstration video. The findings show that the enhanced diagnostics help stakeholders understand and resolve well-formedness issues more efficiently, particularly for situational conflicts, with ongoing work to further improve insufficiency guidance and provide resolution patches for detected issues.

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

Figures (3)

• Figure 1: Architecture of LEGOS-SLEEC ($\bigstar$ - new components).
• Figure 2: Comparison of previous and new LEGOS-SLEEC diagnostics for situational conflicting SLEEC rules.
• Figure 3: Comparison of previous and new LEGOS-SLEEC diagnostics for insufficient SLEEC rules.