Why ethical audit matters in artificial intelligence?

Rai, Nitesh

doi:10.1007/s43681-021-00100-0

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…One example of this is the "Censius Platform" or the "Tag Tool & Deep Learning Sandbox". The first contains 23 tools for MLOps 8 , as well as AI observability, monitoring and explainability tools and contains an enterprise AI audit checklist [66]. The latter provides a web interface for testing purposes, mainly focusing on capabilities and performance metrics [10, p. 97].…”

Section: Tools and Methodsmentioning

confidence: 99%

Designing an ML Auditing Criteria Catalog as Starting Point for the Development of a Framework

Schwarz,

Hinske,

Mansmann

et al. 2024

IEEE Access

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Background: Although AI algorithms and applications become more and popular in the healthcare sector, only few institutions have an operational AI strategy. Identifying the best suited processes for ML algorithm implementation and adoption is a big challenge. Also, raising human confidence in AI systems is elementary to building trustworthy, socially beneficial and responsible AI. A commonly agreed AI auditing framework that provides best practices and tools could help speeding up the adoption process. In this paper, we first highlight important concepts in the field of AI auditing and then restructure and subsume them into an ML auditing core criteria catalog. Methods: We conducted a scoping study where we analyzed sources being associated with the term "Auditable AI" in a qualitative way. We utilized best practices from Mayring [1], Miles and Huberman [2] as well as Bortz and Döring [3]. Based on referrals, additional relevant white papers and sources in the field of This article has been accepted for publication in IEEE Access.

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Section: Tools and Methodsmentioning

confidence: 99%

Designing an ML Auditing Criteria Catalog as Starting Point for the Development of a Framework

Schwarz,

Hinske,

Mansmann

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…In a complex system, there are many factors affecting it, such as structure, function, materials and technology. The most important is the part itself [13][14]. Therefore, the entire system should be fully analyzed before testing.…”

Section: Factors Affecting the Perfection Test Of Product Partsmentioning

confidence: 99%

A New Method for Testing the Perfection of Construction Machinery Parts Based on Artificial Intelligence

2023

KME

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With the continuous development of artificial intelligence, more and more researchers begin to attach importance to the realization of perfection testing in products, especially the intelligent and simulation technology. In this paper, based on the combination of computer and robot, the simulation experiment on mechanical structure is carried out. This paper first introduces the important role of machine vision system in the process of defect detection of automobile parts, then expounds the method of feature recognition of automobile parts, related principles and key technical basic knowledge points, etc. Finally, a three-dimensional entity model simulating real products is constructed using MATLAB software, and its results are calculated and analyzed. The concepts of intelligence and simulation are described in detail. The test results show that, the accuracy of the model in detecting the degree of perfection of product parts is more than 90%, and the error rate is low. This shows that the testing performance of the model is excellent.

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They shall be fair, transparent, and robust: auditing learning analytics systems

Simbeck

2023

AI Ethics

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In the near future, systems, that use Artificial Intelligence (AI) methods, such as machine learning, are required to be certified or audited for fairness if used in ethically sensitive fields such as education. One example of those upcoming regulatory initiatives is the European Artificial Intelligence Act [AI Ethics 3:267-279, 2022]. Interconnected with fairness are the notions of system transparency (i.e. how understandable is the system) and system robustness (i.e. will similar inputs lead to similar results). Ensuring fairness, transparency, and robustness requires looking at data, models, system processes, and the use of systems as the ethical implications arise at the intersection between those. The potential societal consequences are domain specific, it is, therefore, necessary to discuss specifically for Learning Analytics (LA) what fairness, transparency, and robustness mean and how they can be certified. Approaches to certifying and auditing fairness in LA include assessing datasets, machine learning models, and the end-to-end LA process for fairness, transparency, and robustness. Based on Slade and Prinsloo’s six principals for ethical LA [(Artificial Intelligence Act) And Amending Certain Union Legislative Acts. AIA, 2021], relevant audit approaches will be deduced. Auditing AI applications in LA is a complex process that requires technical capabilities and needs to consider the perspectives of all stakeholders. This paper proposes a comprehensive framework for auditing AI applications in LA systems from the perspective of learners' autonomy, provides insights into different auditing methodologies, and emphasizes the importance of reflection and dialogue among providers, buyers, and users of these systems to ensure their ethical and responsible use.

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Why ethical audit matters in artificial intelligence?

Cited by 5 publications

References 20 publications

Designing an ML Auditing Criteria Catalog as Starting Point for the Development of a Framework

Designing an ML Auditing Criteria Catalog as Starting Point for the Development of a Framework

A New Method for Testing the Perfection of Construction Machinery Parts Based on Artificial Intelligence

They shall be fair, transparent, and robust: auditing learning analytics systems

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