Requirements Engineering for Artificial Intelligence: What Is a Requirements Specification for an Artificial Intelligence?

Berry, Daniel M.

doi:10.1007/978-3-030-98464-9_2

Cited by 18 publications

(7 citation statements)

References 14 publications

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“…For instance, performance metrics, such as precision and recall, F-measure, ROC curve, are critical acceptance criteria for the viability of specific machine learning algorithms in specific contexts, which also direct the continuous optimization of ML models. In addition, Berry discussed requirements specifications for AI applications in terms of performance measures acceptable in a given context, as a value or criteria [3]. Other well-discussed topics include the Financial Support from National key Research and Development Program Project 2021YFB1715200, and NSFC Innovation Group Project 62021002 is gratefully acknowledged.…”

Section: Introductionmentioning

confidence: 99%

Requirements Engineering for Machine Learning: A Review and Reflection

Pei

Liu

Wang

et al. 2022

2022 IEEE 30th International Requirements Engineering Conference Workshops (REW)

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Today, many industrial processes are undergoing digital transformation, which often requires the integration of well-understood domain models and state-of-the-art machine learning technology in business processes. However, requirements elicitation and design decision making about when, where and how to embed various domain models and end-to-end machine learning techniques properly into a given business workflow requires further exploration. This paper aims to provide an overview of the requirements engineering process for machine learning applications in terms of cross domain collaborations. We first review the literature on requirements engineering for machine learning, and then go through the collaborative requirements analysis process step-by-step. An example case of industrial data-driven intelligence applications is also discussed in relation to the aforementioned steps.

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Section: Introductionmentioning

confidence: 99%

Requirements Engineering for Machine Learning: A Review and Reflection

Pei

Liu

Wang

et al. 2022

2022 IEEE 30th International Requirements Engineering Conference Workshops (REW)

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show abstract

“…The choice of algorithm will also depend on the user's needs for the system. Thus as explained by Berry [44], as part of the requirements specification for AI, one needs to clearly specify the possible settings of each variable in a given AI algorithm, applied to a particular task in a given context.…”

Section: Area#2 Requirements For Model Needsmentioning

confidence: 99%

“…Therefore, we need to consider other possible requirements that might need to be elicited and specified in such situations, in our framework. This relates to the Berry's finding [44] that different AI algorithms and their individual settings in different application contexts would require different requirements specifications. However, had the system be designed using a classification algorithm and had to choose between precision or recall, one would need to calculate the trade-offs.…”

Section: Rq1 How Does Our Re4hcai Framework Fare For Eliciting and Mo...mentioning

confidence: 99%

“…In a search system with high recall, all the results relevant to the search query are retrieved, potentially also including results that might not be relevant. Berry [44] explains that when evaluating the reward function, we need to compare the effects of either using precision or recall to how a human would perform the task. The decision to go for either would be based on the trade-off from selecting precision vs recall.…”

Section: Introductionmentioning

confidence: 99%

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Requirements Framework for Engineering Human-centered Artificial Intelligence-Based Software Systems

Ahmad¹,

Abdelrazek²,

Arora³

et al. 2023

Preprint

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Artificial intelligence (AI) components used in building software solutions have substantially increased in recent years. However, many of these solutions end up focusing on technical aspects and ignore critical human-centered aspects. [Objective] Including humancentered aspects during requirements engineering (RE) when building AI-based software can help achieve more responsible, unbiased, and inclusive AI-based software solutions. [Method] In this paper, we present a new framework developed based on human-centered AI guidelines and a user survey to aid in collecting requirements for human-centered AI-based software. We provide a catalog to elicit these requirements and a conceptual model to present them visually.[Results] The framework is applied to a case study to elicit and model requirements for enhancing the quality of 360°videos intended for virtual reality (VR) users. [Conclusion] We found that our proposed approach helped the project team fully understand the needs of the project to deliver. Furthermore, the framework helped to understand what requirements need to be captured at the initial stages against later stages in the engineering process of AI-based software.

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“…More recently, Berry [5] discussed some RE related ideas on how to use measures to evaluate AI related solutions from the point of view of recall and precision. Hence, he addresses what we identify as the model performance concern.…”

Section: Related Workmentioning

confidence: 99%

A Catalogue of Concerns for Specifying Machine Learning-Enabled Systems

Villamizar¹,

Kalinowski²,

lopes³

2022

Preprint

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Requirements engineering (RE) activities for Machine Learning (ML) are not well-established and researched in the literature. Many issues and challenges exist when specifying, designing, and developing ML-enabled systems. Adding more focus on RE for ML can help to develop more reliable ML-enabled systems. Based on insights collected from previous work and industrial experiences, we propose a catalogue of 45 concerns to be considered when specifying ML-enabled systems, covering five different perspectives we identified as relevant for such systems: objectives, user experience, infrastructure, model, and data. Examples of such concerns include the execution engine and telemetry for the infrastructure perspective, and explainability and reproducibility for the model perspective. We conducted a focus group session with eight software professionals with experience developing ML-enabled systems to validate the importance, quality and feasibility of using our catalogue. The feedback allowed us to improve the catalogue and confirmed its practical relevance. The main research contribution of this work consists in providing a validated set of concerns grouped into perspectives that can be used by requirements engineers to support the specification of ML-enabled systems.

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Requirements Engineering for Artificial Intelligence: What Is a Requirements Specification for an Artificial Intelligence?

Cited by 18 publications

References 14 publications

Requirements Engineering for Machine Learning: A Review and Reflection

Requirements Engineering for Machine Learning: A Review and Reflection

Requirements Framework for Engineering Human-centered Artificial Intelligence-Based Software Systems

A Catalogue of Concerns for Specifying Machine Learning-Enabled Systems

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