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Ethical Requirements in the Age of Artificial Intelligence - Supplementary material

<p><strong>Context</strong>: Software developers and users are growing concerned about the ethical use of software, especially with Artificial Intelligence (AI). In this context, we investigated how ethical requirements can be elicited and incorporated into software development. <strong>Problem</strong>: The challenge is identifying and defining effective methods for eliciting and managing ethical requirements in software development.&nbsp;<strong>Solution</strong>: We conducted a Systematic Literature Review (SLR) to identify techniques, methods, processes, frameworks, and tools for eliciting, analyzing, and specifying ethical requirements. <strong>IS Theory</strong>: We explore the application of theories related to requirements engineering, ethics in technology, and data governance. It focuses, in particular, on ensuring that information systems comply with ethical and legal principles from the beginning of the development cycle. <strong>Method</strong>: Following the Kitchenham and Charters protocol, we conducted an SLR with stages of planning, conducting, and reporting the results.&nbsp;<strong>Summarization of Results</strong>: We have identified 46 primary studies. These studies address different approaches to eliciting ethical requirements, including techniques based on user stories, analysis of ethical guidelines, specific frameworks such as ECCOLA, and methods such as interviews and modeling.&nbsp;<strong>Contributions and Impact on the IS area</strong>: The report contributes to the field by consolidating existing practices in the literature regarding ethical requirements. It provides a comprehensive overview of the techniques and tools available for integrating ethical considerations into software systems and identifies gaps and opportunities for future research. The study significantly impacts the IS field by providing practical and theoretical guidelines for eliciting ethical requirements in information systems.</p>

ShareScore

32/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
8
Engagement
0

Dataset details

Repository
zenodo
Published
November 18, 2024
License
cc-by-4.0
DOI
10.5281/zenodo.14183398

Scored with release 0.9.0

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