Applying AI in Practice: Key Challenges and Lessons Learned

Fischer, Lukas; Ehrlinger, Lisa; Geist, Verena; Ramler, Rudolf; Sobieczky, Florian; Zellinger, Werner; Moser, Bernhard

doi:10.1007/978-3-030-57321-8_25

Cited by 11 publications

(2 citation statements)

References 86 publications

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“…Conventional software development works in the exact opposite way compared to data-based/learning-based approaches [14]. Specifications are defining the required behavior of the system, i.e., the "rules".…”

Section: Challengesmentioning

confidence: 99%

Quality Assurance for AI-based Systems: Overview and Challenges

Felderer,

Ramler

2021

Preprint

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The number and importance of AI-based systems in all domains is growing. With the pervasive use and the dependence on AIbased systems, the quality of these systems becomes essential for their practical usage. However, quality assurance for AI-based systems is an emerging area that has not been well explored and requires collaboration between the SE and AI research communities. This paper discusses terminology and challenges on quality assurance for AI-based systems to set a baseline for that purpose. Therefore, we define basic concepts and characterize AI-based systems along the three dimensions of artifact type, process, and quality characteristics. Furthermore, we elaborate on the key challenges of (1) understandability and interpretability of AI models, (2) lack of specifications and defined requirements, (3) need for validation data and test input generation, (4) defining expected outcomes as test oracles, (5) accuracy and correctness measures, (6) non-functional properties of AI-based systems, (7) self-adaptive and self-learning characteristics, and (8) dynamic and frequently changing environments.

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Section: Challengesmentioning

confidence: 99%

Quality Assurance for AI-based Systems: Overview and Challenges

Felderer,

Ramler

2021

Preprint

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“…Therefore, explainability is an essential feature of ML models, as users or experts need to know and understand how the input data affects the results of model decision-making. [10,11].…”

Section: Introductionmentioning

confidence: 99%

Explanation of clustering result based on multi-objective optimization

Chen,

Zhong,

Zhang

2023

PLoS ONE

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Clustering is an unsupervised machine learning technique whose goal is to cluster unlabeled data. But traditional clustering methods only output a set of results and do not provide any explanations of the results. Although in the literature a number of methods based on decision tree have been proposed to explain the clustering results, most of them have some disadvantages, such as too many branches and too deep leaves, which lead to complex explanations and make it difficult for users to understand. In this paper, a hypercube overlay model based on multi-objective optimization is proposed to achieve succinct explanations of clustering results. The model designs two objective functions based on the number of hypercubes and the compactness of instances and then uses multi-objective optimization to find a set of nondominated solutions. Finally, an Utopia point is defined to determine the most suitable solution, in which each cluster can be covered by as few hypercubes as possible. Based on these hypercubes, an explanations of each cluster is provided. Upon verification on synthetic and real datasets respectively, it shows that the model can provide a concise and understandable explanations to users.

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Quality Assurance for AI-Based Systems: Overview and Challenges (Introduction to Interactive Session)

Felderer

Ramler

2021

Lecture Notes in Business Information Processing

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Applying AI in Practice: Key Challenges and Lessons Learned

Cited by 11 publications

References 86 publications

Quality Assurance for AI-based Systems: Overview and Challenges

Quality Assurance for AI-based Systems: Overview and Challenges

Explanation of clustering result based on multi-objective optimization

Quality Assurance for AI-Based Systems: Overview and Challenges (Introduction to Interactive Session)

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