Non-Functional Requirements Orienting the Development of Socially Responsible Software

Cysneiros, Luiz Marcio; Leite, Julio Cesar Sampaio do Prado

doi:10.1007/978-3-030-49418-6_23

Cited by 21 publications

(17 citation statements)

References 26 publications

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“…We agree with the idea of Socially Responsible Software (SRS) [9], which holds that the software development process needs to address Trust in software-embedded products. Within SRS, Trust in software, from the consumer viewpoint, was inspired by the metaphor of Corporate Social Responsibility (CSR) [9]. CSR promotes loyalty, repeat business, and purchase intention [10].…”

Section: Introductionmentioning

confidence: 71%

“…To address the challenge of encoding the social perspective knowledge of Trust, we relied on a social science elicitation method (Grounded Theory (GT)), and a requirements engineering modeling framework, the NFR framework [12]. Our work used a previous Trust NFR catalog [9] and created a translation protocol from the concepts elicited using GT to evolve the Trust catalog. An NFR catalogue is a collection of SIGs that showcase the relationships between multiple softgoals and their operationalization to satisfice one particular NFR.…”

Section: Research Approachmentioning

confidence: 99%

“…CSR also promotes intrinsic Trust and positively impacts market value and companies' profits perceived to be committed to CSR [11]. Cysneiros and Leite developed an initial Softgoal Interdependency Graph (SIG) [12] illustrating the key non-functional requirements (NFRs) identified for creating SRS [9]. Their SIG introduces how Ethics and Transparency are two NFRs vital to the fostering of Trust.…”

Section: Introductionmentioning

confidence: 99%

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Artifacts for Towards Achieving Trust Through Transparency and Ethics [RE 21]

Kwan,

Cysneiros,

Leite

2021

Self Cite

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The ubiquitous presence of software in the products we use, together with Artificial Intelligence in these products, has led to an increasing need for consumer trust. Consumers often lose faith in products, and the lack of Trust propagates to the companies behind them. This is even more so in mission-critical systems such as autonomous vehicles and clinical support systems. This paper follows grounded theory principles to elicit knowledge related to Trust, Ethics, and Transparency. We approach these qualities as Non-Functional Requirements (NFRs), aiming to build catalogs to subsidize the construction of Socially Responsible Software. The corpus we have used was built on a selected collection of literature on Corporate Social Responsibility, with an emphasis on Business Ethics. Our challenge is how to encode the social perspective knowledge, mainly through the view of Corporate Social Responsibility, on how organizations or institutions achieve trustworthiness. Since our ground perspective is that of NFRs, results are presented by a catalogue of Trust as a Non-Functional Requirement, represented as a Softgoal Interdependency Graph (SIG). The SIG language helps software engineers in understanding alternatives they have to improve Trust in software products.

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Section: Introductionmentioning

confidence: 71%

Section: Research Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artifacts for Towards Achieving Trust Through Transparency and Ethics [RE 21]

Kwan,

Cysneiros,

Leite

2021

Self Cite

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“…The companies also highlighted prioritizing transparency when developing AI systems to gain users' trust. Cysneiros et al [11,12] have also proposed transparency as a key quality requirement of AI systems, and Horkoff's [22] study on the quality requirements of machine learning revealed transparency as a key quality.…”

Section: Ethical Guidelines Of Ai For the Development Of Ai Systemsmentioning

confidence: 99%

“…All the interviewees especially pointed out the importance of ensuring the privacy of personal data because of the GDPR. Cysneiros et al [11,12] have also highlighted privacy as a potential quality requirement of AI systems.…”

Section: Ethical Guidelines Of Ai For the Development Of Ai Systemsmentioning

confidence: 99%

Ethical Guidelines for Solving Ethical Issues and Developing AI Systems

Balasubramaniam

Kauppinen

Kujala

et al. 2020

Lecture Notes in Computer Science

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Artificial intelligence (AI) has become a fast-growing trend. Increasingly, organizations are interested in developing AI systems, but many of them have realized that the use of AI technologies can raise ethical questions. The goal of this study was to analyze what kind of ethical guidelines companies have for solving potential ethical issues of AI and developing AI systems. This paper presents the results of the case study conducted in three companies. The ethical guidelines defined by the case companies focused on solving potential ethical issues, such as accountability, explainability, fairness, privacy, and transparency. To analyze different viewpoints on critical ethical issues, two of the companies recommended using multi-disciplinary development teams. The companies also considered defining the purposes of their AI systems and analyzing their impacts to be important practices. Based on the results of the study, we suggest that organizations develop and use ethical guidelines to prioritize critical quality requirements of AI. The results also indicate that transparency, explainability, fairness, and privacy can be critical quality requirements of AI systems.

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Transparency and Explainability of AI Systems: Ethical Guidelines in Practice

Balasubramaniam

Kauppinen

Hiekkanen

et al. 2022

Lecture Notes in Computer Science

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Context and Motivation]Recent studies have highlighted transparency and explainability as important quality requirements of AI systems. However, there are still relatively few case studies that describe the current state of defining these quality requirements in practice.[Question] The goal of our study was to explore what ethical guidelines organizations have defined for the development of transparent and explainable AI systems. We analyzed the ethical guidelines in 16 organizations representing different industries and public sector.[Results] In the ethical guidelines, the importance of transparency was highlighted by almost all of the organizations, and explainability was considered as an integral part of transparency. Building trust in AI systems was one of the key reasons for developing transparency and explainability, and customers and users were raised as the main target groups of the explanations. The organizations also mentioned developers, partners, and stakeholders as important groups needing explanations. The ethical guidelines contained the following aspects of the AI system that should be explained: the purpose, role of AI, inputs, behavior, data utilized, outputs, and limitations. The guidelines also pointed out that transparency and explainability relate to several other quality requirements, such as trustworthiness, understandability, traceability, privacy, auditability, and fairness. [Contribution] For researchers, this paper provides insights into what organizations consider important in the transparency and, in particular, explainability of AI systems. For practitioners, this study suggests a structured way to define explainability requirements of AI systems.

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Non-Functional Requirements Orienting the Development of Socially Responsible Software

Cited by 21 publications

References 26 publications

Artifacts for Towards Achieving Trust Through Transparency and Ethics [RE 21]

Artifacts for Towards Achieving Trust Through Transparency and Ethics [RE 21]

Ethical Guidelines for Solving Ethical Issues and Developing AI Systems

Transparency and Explainability of AI Systems: Ethical Guidelines in Practice

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