Young People's Policy Recommendations on Algorithm Fairness

Vallejos, Elvira Pérez; Koene, Ansgar; Portillo, Virginia; Dowthwaite, Liz; Cano, Monica

doi:10.1145/3091478.3091512

Cited by 12 publications

(10 citation statements)

References 5 publications

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“…Models can be biased based on a lack of representations in the training data or how the model makes decisions, e.g., the selected input variables. The model outcomes should be traceable back to input characteristics [2,23,[50][51][52]. Model values or choices become obsolete.…”

Section: Training Data Qualitiesmentioning

confidence: 99%

Artificial Intelligence Project Success Factors: Moral Decision-Making with Algorithms

Miller¹

2021

Annals of Computer Science and Information Systems

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The algorithms implemented through artificial intelligence (AI) and big data projects are used in life-anddeath situations. While research exists to address varying aspects of moral decision-making with algorithms, the definition of project success is not readily available. Nevertheless, researchers place the burden of responsibility for ethical decisions from AI systems on the system developers. Using a systematic literature review, this research identified 71 AI project success factors in 14 groups related to moral decision-making with algorithms. It contributes to project management literature, specifically for AI projects. Project managers and sponsors can use the results during project planning and execution.

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Section: Training Data Qualitiesmentioning

confidence: 99%

Artificial Intelligence Project Success Factors: Moral Decision-Making with Algorithms

Miller¹

2021

Annals of Computer Science and Information Systems

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“…The literature suggests that human-related factors influence the perception of AI systems. For example, empirical studies have found that age (Grgić-Hlača et al, 2020;Helberger et al, 2020;Vallejos et al, 2017), educational level (Helberger et al, 2020), self-interest (Grgić-Hlača et al, 2020;Wang et al, 2020), familiarity with algorithms (Saha et al, 2020), and concerns about data collection (Araujo et al, 2020) have effects on the perception of algorithmic fairness. Hancock et al (2011) performed a meta-analysis of factors influencing trust in human-robot interaction and identified, among others, demographics and attitudes toward robots as possible predictors.…”

Section: Public Preferences For Ai Ethics Guidelinesmentioning

confidence: 99%

“…For example, the Fairness Concerned group should be addressed in different ways than the Safety Concerned or the Human in the Loop groups. Several studies have already been conducted on the inclusion of stakeholders in the design process, especially for fairness (Vallejos et al, 2017;Webb et al, 2018).…”

Section: Ethical Design and Demands Of Potential Stakeholder Groupsmentioning

confidence: 99%

AI-Ethics by Design. Evaluating Public Perception on the Importance of Ethical Design Principles of AI

Kieslich,

Keller,

Starke

2021

Preprint

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Despite the immense societal importance of ethically designing artificial intelligence (AI), little research on the public perceptions of ethical AI principles exists. This becomes even more striking when considering that ethical AI development has the aim to be human-centric and of benefit for the whole society. In this study, we investigate how ethical principles (explainability, fairness, security, accountability, accuracy, privacy, machine autonomy) are weighted in comparison to each other. This is especially important, since simultaneously considering ethical principles is not only costly, but sometimes even impossible, as developers must make specific trade-off decisions. In this paper, we give first answers on the relative importance of ethical principles given a specific use case -the use of AI in tax fraud detection. The results of a large conjoint survey (n=1099) suggest that, by and large, German respondents found the ethical principles equally important. However, subsequent cluster analysis shows that different preference models for ethically designed systems exist among the German population. These clusters substantially differ not only in the preferred attributes, but also in the importance level of the attributes themselves. We further describe how these groups are constituted in terms of sociodemographics as well as opinions on AI. Societal implications as well as design challenges are discussed. K eywords ethical principles • artificial intelligence • public perception • design preferences • trade-offs

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“…They are used here to explore the impact of algorithmic biases on young people and generate their recommendation for a fairer online world that is best aligned to young people's expectations and concerns. The first series of juries produced a rich dataset that is continuing to showcase concerns and provide recommendations [29,31,51,52].…”

Section: Engaging Young People In Researchmentioning

confidence: 99%

"It's your private information. it's your life."

Dowthwaite

Creswick

Portillo

et al. 2020

Proceedings of the Interaction Design and Children Conference

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Children and young people make extensive and varied use of digital and online technologies, yet issues about how their personal data may be collected and used by online platforms are rarely discussed. Additionally, despite calls to increase awareness, schools often do not cover these topics, instead focusing on online safety issues, such as being approached by strangers, cyberbullying or access to inappropriate content. This paper presents the results of one of the activities run as part of eleven workshops with 13-18 year olds, using co-designed activities to encourage critical thinking. Sets of 'data cards' were used to stimulate discussion about sharing and selling of personal data by online technology companies. Results highlight the desire and need for increased awareness about the potential uses of personal data amongst this age group, and the paper makes recommendations for embedding this into school curriculums as well as incorporating it into interaction design, to allow young people to make informed decisions about their online lives.

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Young People's Policy Recommendations on Algorithm Fairness

Cited by 12 publications

References 5 publications

Artificial Intelligence Project Success Factors: Moral Decision-Making with Algorithms

Artificial Intelligence Project Success Factors: Moral Decision-Making with Algorithms

AI-Ethics by Design. Evaluating Public Perception on the Importance of Ethical Design Principles of AI

"It's your private information. it's your life."

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