4.6 Article

An extension of iStar for Machine Learning requirements by following the PRISE methodology

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Information Systems

A methodology to automatically translate user requirements into visualizations: Experimental validation

Ana Lavalle et al.

Summary: This paper aims to help non-expert users effectively express their analytical needs in data visualization, generate the most suitable visualizations, and evaluate the impact of the proposal through a case study.

INFORMATION AND SOFTWARE TECHNOLOGY (2021)

Article Computer Science, Artificial Intelligence

Legal requirements on explainability in machine learning

Adrien Bibal et al.

Summary: Deep learning and other black-box models are increasingly popular, but their lack of explainability may pose ethical and legal challenges. This paper discusses the growing legal requirements for interpretability in machine learning models in decision making contexts and calls for interdisciplinary research to enhance explainability.

ARTIFICIAL INTELLIGENCE AND LAW (2021)

Article Computer Science, Information Systems

Designing Business Analytics Solutions A Model-Driven Approach

Soroosh Nalchigar et al.

BUSINESS & INFORMATION SYSTEMS ENGINEERING (2020)

Article Computer Science, Software Engineering

PRISE: A process to support iStar extensions

Enyo Goncalves et al.

JOURNAL OF SYSTEMS AND SOFTWARE (2020)

Article Medical Informatics

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective

Julia Amann et al.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2020)

Review Multidisciplinary Sciences

Integrating Machine Learning with Human Knowledge

Changyu Deng et al.

ISCIENCE (2020)

Article Computer Science, Information Systems

Goal-oriented requirements engineering: an extended systematic mapping study

Jennifer Horkoff et al.

REQUIREMENTS ENGINEERING (2019)

Article Computer Science, Artificial Intelligence

Incorporating domain knowledge in machine learning for soccer outcome prediction

Daniel Berrar et al.

MACHINE LEARNING (2019)

Article Computer Science, Interdisciplinary Applications

Machine learning based concept drift detection for predictive maintenance

Jan Zenisek et al.

COMPUTERS & INDUSTRIAL ENGINEERING (2019)

Article Computer Science, Artificial Intelligence

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Cynthia Rudin

NATURE MACHINE INTELLIGENCE (2019)

Review Computer Science, Software Engineering

A Systematic Literature Review of iStar extensions

Enyo Goncalves et al.

JOURNAL OF SYSTEMS AND SOFTWARE (2018)

Article Computer Science, Information Systems

Goal-driven risk assessment in requirements engineering

Yudistira Asnar et al.

REQUIREMENTS ENGINEERING (2011)

Article Computer Science, Artificial Intelligence

VQSVM: A case study for incorporating prior domain knowledge into inductive machine learning

Ting Yu et al.

NEUROCOMPUTING (2010)

Article Automation & Control Systems

Tropos: An agent-oriented software development methodology

P Bresciani et al.

AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS (2004)