IEEE P7003™ standard for algorithmic bias considerations

Koene, Ansgar; Dowthwaite, Liz; Seth, Suchana

doi:10.1145/3194770.3194773

Cited by 16 publications

(9 citation statements)

References 3 publications

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“…Surprisingly, we found nine papers that expanded on the legal and ethical implication of gender biases in automated systems (Katell et al, 2020 ; Fleisher, 2021 ). This includes a paper by Koene et al, which is a work-in-progress paper regarding an IEEE industry standard to prevent algorithmic biases (Koene et al, 2018 ). Similarly, a paper by Karimi et al discusses the presence of gender bias in criminal recidivism and highlights how a biased system affect female prisoners (Karimi-Haghighi and Castillo, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

Exploring gender biases in ML and AI academic research through systematic literature review

Shrestha

Das

2022

Front. Artif. Intell.

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Automated systems that implement Machine learning (ML) and Artificial Intelligence (AI) algorithms present promising solutions to a variety of technological and non-technological issues. Although, industry leaders are rapidly adopting these systems for anything from marketing to national defense operations, these systems are not without flaws. Recently, many of these systems are found to inherit and propagate gender and racial biases that disadvantages the minority population. In this paper, we analyze academic publications in the area of gender biases in ML and AI algorithms thus outlining different themes, mitigation and detection methods explored through research in this topic. Through a detailed analysis of N = 120 papers, we map the current research landscape on gender specific biases present in ML and AI assisted automated systems. We further point out the aspects of ML/AI gender biases research that are less explored and require more attention. Mainly we focus on the lack of user studies and inclusivity in this field of study. We also shed some light into the gender bias issue as experienced by the algorithm designers. In conclusion, in this paper we provide a holistic view of the breadth of studies conducted in the field of exploring, detecting and mitigating gender biases in ML and AI systems and, a future direction for the studies to take in order to provide a fair and accessible ML and AI systems to all users.

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Section: Resultsmentioning

confidence: 99%

Exploring gender biases in ML and AI academic research through systematic literature review

Shrestha

Das

2022

Front. Artif. Intell.

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“…However, the technical solution of ensuring that the number of females selected for interviews matches the distribution of females in the training data is a statistical principle and not an ethical principle. To illustrate how an ethical approach to bias is different from a technical approach, take, for example, the IEEE P7003 Standard for Algorithmic Bias Considerations [22], which states that "Unjustified bias refers to differential treatment of individuals based on criteria for which no operational justification is given." Whether bias is justified or not -which is another way of saying whether it is right or wrong -is a question of values that cannot be answered by appealing to the scientific method.…”

Section: A Ethics In Contextmentioning

confidence: 99%

From Ethical Artificial Intelligence Principles to Practice: A Case Study of University-Industry Collaboration

Smiee

Brophy

Attwood

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

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The ethical artificial intelligence principal to practice gap is a significant challenge for micro and small medium businesses (SME). The policy and legal landscape is very dynamic and whilst there are limited toolkits, designed to help such businesses to embed the ethical design of responsible technology, there is generally a lack of skills, knowledge and resources on how to apply them within individual businesses. In this paper we present a small case study of practical examples that has led to the introduction of ethical AI practices into SMEs. Through a European funded university-industry collaboration, to date 102 SMEs within Greater Manchester, UK have been exposed to data and ethical AI workshops, with a subset of these choosing to deeper dive and apply tools such as consequence scanning and harms modelling within their business with support from university technical analysts and academics. The case study presents initial evaluations on embedding ethical principles, challenges faced by the SMEs and the reflections of technical analysts.

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“…Roadmaps to develop ethical software are proposed [13,28], where the needs for methods to build ethical software, to evaluate the compatibility of the software with human values, and to help stakeholders formulate their values are highlighted. In this direction, Hussain et al [78] and the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems [97], respectively, argue for a collaborative framework to create software design patterns including social values (such values would be unwanted biases and different types of unfairness in our case) and for standards on algorithmic biases in order to provide a development framework that could support the creation of value-aligned algorithmic software. We believe this is also highly relevant for the data management community as, for instance, the data schemas developed in discussion with stakeholders need to be aligned with the values to integrate into the decision-support systems.…”

Section: Guidance In Software Engineeringmentioning

confidence: 99%

Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems

2021

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The increasing use of data-driven decision support systems in industry and governments is accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of these systems. Multiple computer science communities, and especially machine learning, have started to tackle this problem, often developing algorithmic solutions to mitigate biases to obtain fairer outputs. However, one of the core underlying causes for unfairness is bias in training data which is not fully covered by such approaches. Especially, bias in data is not yet a central topic in data engineering and management research. We survey research on bias and unfairness in several computer science domains, distinguishing between data management publications and other domains. This covers the creation of fairness metrics, fairness identification, and mitigation methods, software engineering approaches and biases in crowdsourcing activities. We identify relevant research gaps and show which data management activities could be repurposed to handle biases and which ones might reinforce such biases. In the second part, we argue for a novel data-centered approach overcoming the limitations of current algorithmic-centered methods. This approach focuses on eliciting and enforcing fairness requirements and constraints on data that systems are trained, validated, and used on. We argue for the need to extend database management systems to handle such constraints and mitigation methods. We discuss the associated future research directions regarding algorithms, formalization, modelling, users, and systems.

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IEEE P7003™ standard for algorithmic bias considerations

Cited by 16 publications

References 3 publications

Exploring gender biases in ML and AI academic research through systematic literature review

Exploring gender biases in ML and AI academic research through systematic literature review

From Ethical Artificial Intelligence Principles to Practice: A Case Study of University-Industry Collaboration

Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems

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