The Landscape and Gaps in Open Source Fairness Toolkits

Lee, Michelle Seng Ah; Singh, Jat

doi:10.1145/3411764.3445261

Cited by 75 publications

(47 citation statements)

References 22 publications

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“…The documentation produced by these tools should form part of impact assessment and audit processes in order that all ethical aspects of a product can be captured (not just a focus on, e.g. metrics for fairness [102]). In Fig.…”

Section: Discussionmentioning

confidence: 99%

“…This study contributes to the discussion about ethical AI by clarifying the different themes emerging in this landscape. It also serves to illustrate how complex this landscape is, and as others have noted [7,19,98,102,104,105], this provides a barrier to those developing or purchasing AI systems as to which tool is appropriate for their purposes.…”

Section: Discussionmentioning

confidence: 99%

“…We also have not set out to provide an exhaustive review of the computational techniques in the AI/ML research to address ethical issues like fairness and explainability, for this, see for example [102,103]. The system is tested Deployment…”

Section: Document Analysismentioning

confidence: 99%

See 2 more Smart Citations

Putting AI ethics to work: are the tools fit for purpose?

Ayling

Chapman

2021

AI Ethics

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Bias, unfairness and lack of transparency and accountability in Artificial Intelligence (AI) systems, and the potential for the misuse of predictive models for decision-making have raised concerns about the ethical impact and unintended consequences of new technologies for society across every sector where data-driven innovation is taking place. This paper reviews the landscape of suggested ethical frameworks with a focus on those which go beyond high-level statements of principles and offer practical tools for application of these principles in the production and deployment of systems. This work provides an assessment of these practical frameworks with the lens of known best practices for impact assessment and audit of technology. We review other historical uses of risk assessments and audits and create a typology that allows us to compare current AI ethics tools to Best Practices found in previous methodologies from technology, environment, privacy, finance and engineering. We analyse current AI ethics tools and their support for diverse stakeholders and components of the AI development and deployment lifecycle as well as the types of tools used to facilitate use. From this, we identify gaps in current AI ethics tools in auditing and risk assessment that should be considered going forward.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Putting AI ethics to work: are the tools fit for purpose?

Ayling

Chapman

2021

AI Ethics

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“…Furthermore, an evaluation of the available AML tools and libraries with regards to capabilities and needs of industrial practitioners might ease their usage across application domains. In line with recent work on fairness [50] and ethics [21], we consider this crucial for designing tools, corporate guidelines and regulations.…”

Section: Future Workmentioning

confidence: 95%

Mental Models of Adversarial Machine Learning

Bieringer¹,

Grosse²,

Backes³

et al. 2021

Preprint

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Although machine learning (ML) is widely used in practice, little is known about practitioners' actual understanding of potential security challenges. In this work, we close this substantial gap in the literature and contribute a qualitative study focusing on developers' mental models of the ML pipeline and potentially vulnerable components. Studying mental models has helped in other security fields to discover root causes or improve risk communication. Our study reveals four characteristic ranges in mental models of industrial practitioners. The first range concerns the intertwined relationship of adversarial machine learning (AML) and classical security. The second range describes structural and functional components. The third range expresses individual variations of mental models, which are neither explained by the application nor by the educational background of the corresponding subjects. The fourth range corresponds to the varying levels of technical depth, which are however not determined by our subjects' level of knowledge. Our characteristic ranges have implications for the integration of AML into corporate workflows, security enhancing tools for practitioners, and creating appropriate regulatory frameworks for AML.

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“…One requirement is an organizational culture that fosters productive ethical deliberation (Buhmann and Fieseler, 2021). Deliberation is required when issues do not have obviously “right” answers or moral “owners,” as is often the case when implementing currently available Ethical AI tools (Lee and Singh, 2021). Ethical deliberation is most successful when it engages contributors with different backgrounds and organizational roles (Thompson, 2007).…”

Section: Investment Area 2: Committed Organizational Leadership and R...mentioning

confidence: 99%

Four investment areas for ethical AI: Transdisciplinary opportunities to close the publication-to-practice gap

Borg

2021

Big Data & Society

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Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs to be trained on Big Data to be accurate, and Big Data's value is largely realized through its use by Artificial Intelligence. As a result, Big Data and Artificial Intelligence practices are tightly intertwined in real life settings, as are their impacts on society. Unethical uses of Artificial Intelligence are therefore a Big Data problem, at least to some degree. Efforts to address this problem have been dominated by the documentation of Ethical Artificial Intelligence principles and the creation of technical tools that address specific aspects of those principles. However, there is mounting evidence that Ethical Artificial Intelligence principles and technical tools have little impact on the Artificial Intelligence that is created in practice, sometimes in very public ways. The goal of this commentary is to highlight four interconnected areas society can invest in to close this Ethical Artificial Intelligence publication-to-practice gap, maximizing the positive impact Artificial Intelligence and Big Data have on society. For Ethical Artificial Intelligence to become a reality, I argue that these areas need to be addressed holistically in a way that acknowledges their interdependencies. Progress will require iteration, compromise, and transdisciplinary collaboration, but the result of our investments will be the realization of Artificial Intelligence's and Big Data's tremendous potential for social good, in practice rather than in just our hopes and aspirations.

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The Landscape and Gaps in Open Source Fairness Toolkits

Cited by 75 publications

References 22 publications

Putting AI ethics to work: are the tools fit for purpose?

Putting AI ethics to work: are the tools fit for purpose?

Mental Models of Adversarial Machine Learning

Four investment areas for ethical AI: Transdisciplinary opportunities to close the publication-to-practice gap

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