AI Certification: Advancing Ethical Practice by Reducing Information Asymmetries

Cihon, Peter; Kleinaltenkamp, Moritz Jan; Schuett, Jonas; Baum, Seth D.

doi:10.1109/tts.2021.3077595

Cited by 39 publications

(19 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A primary role of certification in the context of AI is to reduce information asymmetries between the suppliers and users of AI systems by accurately characterising the object of certification, justifying the requirement for an independent third party to carry out the conformity assessment [21]. It is important that certification is adopted substantively and not just symbolically, which can be achieved by offering the right incentives for conformity.…”

Section: Figure 2 Components Within the Uk National Quality Infrastru...mentioning

confidence: 99%

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

Levene¹,

Wooldridge²

2023

View full text Add to dashboard Cite

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.

show abstract

Section: Figure 2 Components Within the Uk National Quality Infrastru...mentioning

confidence: 99%

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

Levene¹,

Wooldridge²

2023

View full text Add to dashboard Cite

show abstract

“…Our observations around the use of "AI" as a marketing "buzz word, " reflect recent concerns around "AI as snake oil" [27], and the exploitation of AI's vague definition as a loose umbrella term. Thus, strategies that ensure that AI ethics constitute more than an ethical Potemkin facade are needed [9].…”

Section: Financial Pressuresmentioning

confidence: 99%

Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups

Winecoff,

Watkins

2022

Preprint

View full text Add to dashboard Cite

Scholars and industry practitioners have debated how to best develop interventions for ethical artificial intelligence (AI). Such interventions recommend that companies building and using AI tools change their technical practices, but fail to wrangle with critical questions about the organizational and institutional context in which AI is developed. In this paper, we contribute descriptive research around the life of "AI" as a discursive concept and organizational practice in an understudied sphere-emerging AI startups-and with a focus on extra-organizational pressures faced by entrepreneurs. Leveraging a theoretical lens for how organizations change, we conducted semi-structured interviews with 23 entrepreneurs working at early-stage AI startups. We find that actors within startups both conform to and resist institutional pressures. Our analysis identifies a central tension for AI entrepreneurs: they often valued scientific integrity and methodological rigor; however, influential external stakeholders either lacked the technical knowledge to appreciate entrepreneurs' emphasis on rigor or were more focused on business priorities. As a result, entrepreneurs adopted hyped marketing messages about AI that diverged from their scientific values, but attempted to preserve their legitimacy internally. Institutional pressures and organizational constraints also influenced entrepreneurs' modeling practices and their response to actual or impending regulation. We conclude with a discussion for how such pressures could be used as leverage for effective interventions towards building ethical AI.CCS Concepts: • Social and professional topics → Socio-technical systems; • Applied computing → Sociology.

show abstract

“…Conversely, the lack of either increases the risk of an ML system failing post-deployment or having some unforeseen effects on the affected community. There have been calls for mandatory certification of engineers to ensure a minimum level of competency and ethical training, though they are largely voluntary [38]. Additionally, the diversity of a team (in terms of demographics) will affect its ability to identify design decisions that may disproportionately impact different demographics [142], such as using proxy attributes in modeling or training an international chatbot only on White American English.…”

Section: Design Riskmentioning

confidence: 99%

The Risks of Machine Learning Systems

Tan¹,

Taeihagh²,

Baxter³

2022

Preprint

View full text Add to dashboard Cite

The speed and scale at which machine learning (ML) systems are deployed are accelerating even as an increasing number of studies highlight their potential for negative impact. There is a clear need for companies and regulators to manage the risk from proposed ML systems before they harm people. To achieve this, private and public sector actors first need to identify the risks posed by a proposed ML system. A system's overall risk is influenced by its direct and indirect effects. However, existing frameworks for ML risk/impact assessment often address an abstract notion of risk or do not concretize this dependence. Keeping discussions of risk at an abstract level puts the onus of defining them on the assessor, which may result in incomplete and inconsistent assessments.We propose to address this gap with a context-sensitive framework for identifying ML system risks comprising two components: a taxonomy of the first-and second-order risks posed by ML systems, and their contributing factors. First-order risks stem from aspects of the ML system, while second-order risks stem from the consequences of first-order risks. These consequences are system failures that result from design and development choices. We explore how different risks may manifest in various types of ML systems, the factors that affect each risk, and how first-order risks may lead to second-order effects when the system interacts with the real world.Throughout the paper, we show how real events and prior research fit into our Machine Learning System Risk framework (MLSR).MLSR operates on ML systems rather than technologies or domains, recognizing that a system's design, implementation, and use case all contribute to its risk. In doing so, it unifies the risks that are commonly discussed in the ethical AI community (e.g., ethical/human rights risks) with system-level risks (e.g., application, design, control risks), paving the way for holistic risk assessments of ML systems.

show abstract

AI Certification: Advancing Ethical Practice by Reducing Information Asymmetries

Cited by 39 publications

References 55 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

Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups

The Risks of Machine Learning Systems

Contact Info

Product

Resources

About