AI Ethics Principles in Practice: Perspectives of Designers and Developers

Sanderson, Conrad; Douglas, David C.; Lu, Qinghua; Schleiger, Emma; Whittle, Jon; Lacey, Justine; Newnham, Glenn; Hajkowicz, Stefan; Robinson, Catherine J.; Hansen, David

doi:10.48550/arxiv.2112.07467

Cited by 4 publications

(4 citation statements)

References 33 publications

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“…It prescribes how the framework may be used most productively. Sanderson et al (2021) stress the importance of "diverse perspectives and collaborative teams in the design, development and use of AI technology" to promote the beneficent use of AI. To ensure comprehensive involvement, management, data managers and relevant business units (including those some steps removed from the area directly impacted by the AI system) should be purposely engaged (Krzywdzinski et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Preparing to work with artificial intelligence: assessing WHS when using AI in the workplace

Cebulla¹,

Szpak²,

Knight³

2023

IJWHM

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PurposeArtificial Intelligence (AI) systems play an increasing role in organisation management, process and product development. This study identifies risks and hazards that AI systems may pose to the work health and safety (WHS) of those engaging with or exposed to them. A conceptual framework of organisational measures for minimising those risks is proposed.Design/methodology/approachAdopting an exploratory, inductive qualitative approach, the researchers interviewed 30 experts in data science, technology and WHS; 12 representatives of nine organisations using or preparing to use AI; and ran online workshops, including with 12 WHS inspectors. The research mapped AI ethics principles endorsed by the Australian government onto the AI Canvas, a tool for tracking AI implementation from ideation via development to operation. Fieldwork and analysis developed a matrix of WHS and organisational–managerial risks and risk minimisation strategies relating to AI use at each implementation stage.FindingsThe study identified psychosocial, work stress and workplace relational risks that organisations and employees face during AI implementation in a workplace. Privacy, business continuity and gaming risks were also noted. All may persist and reoccur during the lifetime of an AI system. Alertness to such risks may be enhanced by adopting a systematic risk assessment approach.Originality/valueA collaborative project involving sociologists, economists and computer scientists, the study relates abstract AI ethics principles to concrete WHS risks and hazards. The study translates principles typically applied at the societal level to workplaces and proposes a process for assessing AI system risks.

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Section: Discussionmentioning

confidence: 99%

Preparing to work with artificial intelligence: assessing WHS when using AI in the workplace

Cebulla¹,

Szpak²,

Knight³

2023

IJWHM

View full text Add to dashboard Cite

show abstract

“…We note that the suggestions presented in this work reflect the content of the interviews and the surrounding discussions, and are hence not exhaustive. As such, including suggestions from relevant literature [15], [21], [22], [25], [26] may yield a more complete picture.…”

Section: Discussionmentioning

confidence: 99%

“…An important limitation of such AI ethics principles is the lack of empirically proven methods to robustly translate the principles into practice [17], [22], [27]. This includes the lack of methods that support the proactive design of transparent, explainable and accountable systems, in contrast to the less useful post-hoc explanations of system outputs.…”

Section: Introductionmentioning

confidence: 99%

Towards Implementing Responsible AI

Sanderson

Douglas

et al. 2022

2022 IEEE International Conference on Big Data (Big Data)

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As the deployment of artificial intelligence (AI) is changing many fields and industries, there are concerns about AI systems making decisions and recommendations without adequately considering various ethical aspects, such as accountability, reliability, transparency, explainability, contestability, privacy, and fairness. While many sets of AI ethics principles have been recently proposed that acknowledge these concerns, such principles are high-level and do not provide tangible advice on how to develop ethical and responsible AI systems. To gain insight on the possible implementation of the principles, we conducted an empirical investigation involving semi-structured interviews with a cohort of AI practitioners. The salient findings cover four aspects of AI system design and development, adapting processes used in software engineering: (i) high-level view, (ii) requirements engineering, (iii) design and implementation, (iv) deployment and operation.

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“…With a view towards addressing the gap between highlevel AI ethics principles and tangible implementations of the principles, we seek to identify the current state and potential challenges that developers face in dealing with responsible AI issues during the development of AI systems. To that end, we have conducted an empirical study involving semistructured interviews with 21 scientists and engineers from various backgrounds, all working on AI/ML related projects at CSIRO (Australia's national science agency) [18]. We asked the interviewees what ethical issues they have considered in their AI/ML work, and how they addressed or envisioned to address these issues.…”

Section: Introductionmentioning

confidence: 99%

Towards Implementing Responsible AI

Sanderson¹,

Lu²,

Douglas³

et al. 2022

Preprint

Self Cite

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While artificial intelligence (AI) has great potential to transform many industries, there are concerns about its ability to make decisions in a responsible way. Many AI ethics guidelines and principles have been recently proposed by governments and various organisations, covering areas such as privacy, accountability, safety, reliability, transparency, explainability, contestability, and fairness. However, such principles are typically high-level and do not provide tangible guidance on how to design and develop responsible AI systems. To address this shortcoming, we present an empirical study involving interviews with 21 scientists and engineers, designed to gain insight into practitioners' perceptions of AI ethics principles, their possible implementation, and the trade-offs between the principles. The salient findings cover four aspects of AI system development: (i) overall development process, (ii) requirements engineering, (iii) design and implementation, (iv) deployment and operation.

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AI Ethics Principles in Practice: Perspectives of Designers and Developers

Cited by 4 publications

References 33 publications

Preparing to work with artificial intelligence: assessing WHS when using AI in the workplace

Preparing to work with artificial intelligence: assessing WHS when using AI in the workplace

Towards Implementing Responsible AI

Towards Implementing Responsible AI

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