Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications

Eder, Sarah; Weissenböck, Johannes; Schwald, Christoph; Doms, Thomas; Vogt, Thomas; Hochreiter, Sepp; Nessler, Bernhard

doi:10.48550/arxiv.2103.16910

Cited by 10 publications

(9 citation statements)

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“…This is especially true in what are known as deep neural networks (DNNs), which stack up multiples layers of neurons between the inputs and outputs of the NN. Again, certification of AI systems can help with explainability, not in terms of requiring exact reproducibility under ideally defined conditions, but in ensuring that the implementation of the ML models meet some minimum performance requirements in a statistically significant fashion [9].…”

Section: The Pillars Of Trustworthy Aimentioning

confidence: 99%

Certification of machine learning applications in the context of trustworthy AI with reference to the standardisation of AI systems

Levene¹,

Wooldridge²

2023

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Artificial intelligence (AI) and its subset machine learning (ML), which focuses on learning tasks from data, are some of the most disruptive emergent technologies. AI standards inform organisations how to develop and manage their AI systems and are emerging to satisfy the increasing demand from industry and society for the safe adoption of AI and ML technologies. However, AI systems must be trustworthy in the sense that they can be relied upon to make responsible decisions. Consequently, trustworthy AI is a collection of principles that encourages responsible development, use and deployment of AI systems, and can be viewed as a framework for managing risk in AI systems.

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Section: The Pillars Of Trustworthy Aimentioning

confidence: 99%

Certification of machine learning applications in the context of trustworthy AI with reference to the standardisation of AI systems

Levene¹,

Wooldridge²

2023

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“…Alternatively, some propose independent bodies as an all-encompassing term, including NGOs and governmental agencies such as the FDA. Much like Raji et al (2022), they argue that an independent body ensures the quality of the certification (Sharkov et al, 2021) and the competence of the auditors (Winter et al, 2021).…”

Section: Potential Certifying Entities (Academic)mentioning

confidence: 99%

A consumer perspective of AI certification – the current certification landscape, consumer approval and directions for future research

Blösser,

Weihrauch

2023

EJM

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Purpose In spite of the merits of artificial intelligence (AI) in marketing and social media, harm to consumers has prompted calls for AI auditing/certification. Understanding consumers’ approval of AI certification entities is vital for its effectiveness and companies’ choice of certification. This study aims to generate important insights into the consumer perspective of AI certifications and stimulate future research. Design/methodology/approach A literature and status-quo-driven search of the AI certification landscape identifies entities and related concepts. This study empirically explores consumer approval of the most discussed entities in four AI decision domains using an online experiment and outline a research agenda for AI certification in marketing/social media. Findings Trust in AI certification is complex. The empirical findings show that consumers seem to approve more of non-profit entities than for-profit entities, with the government approving the most. Research limitations/implications The introduction of AI certification to marketing/social media contributes to work on consumer trust and AI acceptance and structures AI certification research from outside marketing to facilitate future research on AI certification for marketing/social media scholars. Practical implications For businesses, the authors provide a first insight into consumer preferences for AI-certifying entities, guiding the choice of which entity to use. For policymakers, this work guides their ongoing discussion on “who should certify AI” from a consumer perspective. Originality/value To the best of the authors’ knowledge, this work is the first to introduce the topic of AI certification to the marketing/social media literature, provide a novel guideline to scholars and offer the first set of empirical studies examining consumer approval of AI certifications.

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“…ML pipelines are orchestrations of components, each one implementing a specific aspect of ML, from data ingestion to visualization and reporting. New certification schemes should go beyond the assessment of standalone ML models, and support the assessment of ML pipelines [12]. C5: Multi-layer ML-based systems.…”

Section: Challenges In ML Certificationmentioning

confidence: 99%

Multi-Dimensional Certification of Modern Distributed Systems

Anisetti

Ardagna

Bena

2022

IEEE Trans. Serv. Comput.

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Machine Learning (ML) is increasingly used to drive the operation of complex distributed systems deployed on the cloud-edge continuum enabled by 5G. Correspondingly, distributed systems' behavior is becoming more non-deterministic in nature. This evolution of distributed systems requires the definition of new assurance approaches for the verification of non-functional properties. Certification, the most popular assurance technique for system and software verification, is not immediately applicable to systems whose behavior is determined by Machine Learning-based inference. However, there is an increasing push from policy makers, regulators, and industrial stakeholders towards the definition of techniques for the certification of non-functional properties (e.g., fairness, robustness, privacy) of ML. This article analyzes the challenges and deficiencies of current certification schemes, discusses open research issues and proposes a first certification scheme for ML-based distributed systems.

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Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications

Cited by 10 publications

References 0 publications

Certification of machine learning applications in the context of trustworthy AI with reference to the standardisation of AI systems

Certification of machine learning applications in the context of trustworthy AI with reference to the standardisation of AI systems

A consumer perspective of AI certification – the current certification landscape, consumer approval and directions for future research

Multi-Dimensional Certification of Modern Distributed Systems

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