LEGOS-SLEEC: Tool for Formalizing and Analyzing Normative Requirements
- URL: http://arxiv.org/abs/2501.12544v1
- Date: Tue, 21 Jan 2025 23:31:04 GMT
- Title: LEGOS-SLEEC: Tool for Formalizing and Analyzing Normative Requirements
- Authors: Kevin Kolyakov, Lina Marsso, Nick Feng, Junwei Quan, Marsha Chechik,
- Abstract summary: We present LEGOS-SLEEC, a tool designed to support interdisciplinary stakeholders in specifying normative requirements.
SLEEC DSL has been developed to formalize these requirements as SLEEC rules.
We have significantly improved the user interface of LEGOS-SLEEC and its diagnostic support.
- Score: 1.4272256806865102
- License:
- 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
Related papers
- The Impossibility of Fair LLMs [59.424918263776284]
The need for fair AI is increasingly clear in the era of large language models (LLMs)
We review the technical frameworks that machine learning researchers have used to evaluate fairness.
We develop guidelines for the more realistic goal of achieving fairness in particular use cases.
arXiv Detail & Related papers (2024-05-28T04:36:15Z) - A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law.
We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies.
We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
arXiv Detail & Related papers (2024-05-02T22:43:02Z) - Normative Requirements Operationalization with Large Language Models [3.456725053685842]
Normative non-functional requirements specify constraints that a system must observe in order to avoid violations of social, legal, ethical, empathetic, and cultural norms.
Recent research has tackled this challenge using a domain-specific language to specify normative requirements.
We propose a complementary approach that uses Large Language Models to extract semantic relationships between abstract representations of system capabilities.
arXiv Detail & Related papers (2024-04-18T17:01:34Z) - Towards Responsible AI in Banking: Addressing Bias for Fair
Decision-Making [69.44075077934914]
"Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture.
This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias.
In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
arXiv Detail & Related papers (2024-01-13T14:07:09Z) - Social, Legal, Ethical, Empathetic, and Cultural Rules: Compilation and Reasoning (Extended Version) [8.425874385897831]
SLEEC (social, legal, ethical, empathetic, or cultural) rules aim to facilitate the formulation, verification, and enforcement of rules AI-based and autonomous systems should obey.
To enable their effective use in AI systems, it is necessary to translate these rules systematically into a formal language that supports automated reasoning.
In this study, we first conduct a linguistic analysis of the SLEEC rules pattern, which justifies the translation of SLEEC rules into classical logic.
arXiv Detail & Related papers (2023-12-15T11:23:49Z) - Specification, Validation and Verification of Social, Legal, Ethical,
Empathetic and Cultural Requirements for Autonomous Agents [4.673587416936401]
We introduce a framework for formal specification, validation and verification of social, legal, ethical, empathetic and cultural (SLEEC) rules for autonomous agents.
We show the applicability of our framework for two autonomous agents from different domains: a firefighter UAV, and an assistive-dressing robot.
arXiv Detail & Related papers (2023-07-07T16:13:43Z) - Stronger Together: on the Articulation of Ethical Charters, Legal Tools,
and Technical Documentation in ML [5.433040083728602]
The need for accountability of the people behind AI systems can be addressed by leveraging processes in three fields of study: ethics, law, and computer science.
We first contrast notions of compliance in the ethical, legal, and technical fields.
We then focus on the role of values in articulating the synergies between the fields.
arXiv Detail & Related papers (2023-05-09T15:35:31Z) - Ethical-Advice Taker: Do Language Models Understand Natural Language
Interventions? [62.74872383104381]
We investigate the effectiveness of natural language interventions for reading-comprehension systems.
We propose a new language understanding task, Linguistic Ethical Interventions (LEI), where the goal is to amend a question-answering (QA) model's unethical behavior.
arXiv Detail & Related papers (2021-06-02T20:57:58Z) - Hacia los Comit\'es de \'Etica en Inteligencia Artificial [68.8204255655161]
It is priority to create the rules and specialized organizations that can oversight the following of such rules.
This work proposes the creation, at the universities, of Ethical Committees or Commissions specialized on Artificial Intelligence.
arXiv Detail & Related papers (2020-02-11T23:48:31Z) - On the Morality of Artificial Intelligence [154.69452301122175]
We propose conceptual and practical principles and guidelines for Machine Learning research and deployment.
We insist on concrete actions that can be taken by practitioners to pursue a more ethical and moral practice of ML aimed at using AI for social good.
arXiv Detail & Related papers (2019-12-26T23:06:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.