FairUMAP 2020: The 3rd Workshop on Fairness in User Modeling, Adaptation and Personalization

Mobasher, Bamshad; Kleanthous, Styliani; Ekstrand, Michael D.; Berendt, Bettina; Otterbacher, Jahna; Tal, Avital Shulner

doi:10.1145/3340631.3398671

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…For example, in the user modeling community, issues centred on security and privacy (e.g., Schreck 2003), inspectability (e.g., Zapata-Rivera and Greer 2004), and privacy (e.g., Kobsa 2007) have long been considered. Most recently, dedicated workshops have been held at leading conferences (e.g., FairUMAP, Mobasher et al 2020), with contributions exploring issues such as fairness (Sacharidis et al 2020), transparency (Schelenz et al 2020), and biases (Deshpande et al 2020). Researchers in e-learning are also interested in ethical issues raised by the systems that they build; issues such as equity and diversity, surveillance and consent, identity and confidentiality (e.g., Anderson and Simpson 2007), and student privacy: "when an observer [which could be an automated system] monitors someone's behavior with full knowledge of their identity, the person being monitored does not enjoy any privacy" (Anwar and Greer 2012, p. 63).…”

Section: The Ethics Of Other Related Research Areasmentioning

confidence: 99%

Ethics of AI in Education: Towards a Community-Wide Framework

Holmes¹,

Porayska-Pomsta²,

Holstein

et al. 2021

Int J Artif Intell Educ

299

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While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically, to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community’s leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context.

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Section: The Ethics Of Other Related Research Areasmentioning

confidence: 99%

Ethics of AI in Education: Towards a Community-Wide Framework

Holmes¹,

Porayska-Pomsta²,

Holstein

et al. 2021

Int J Artif Intell Educ

299

View full text Add to dashboard Cite

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Künstliche Intelligenz in der Hochschulbildung. Bildungssoziologische Perspektiven und Herausforderungen

Stützer,

Kravčík

2023

Künstliche Intelligenz in Der Bildung

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AI Transformation for Learning in Organizations

Bidarra,

Neves

2024

Advances in Human Resources Management and Organizational Development

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In this chapter the authors examine how AI applications support specific learning and training strategies in organizations, empowering individuals to develop their skills, allowing organizations to make data-driven decisions, and ensuring that learning is an ongoing and engaging process. Research shows that AI is an excellent instrument for learning in organizations as it aids in the preparation of instructional materials and helps learners progress in many different ways. So, there seems to be a need for a specific approach to AI pedagogy, which focuses not only on what AI can do but also on which learning activities and learning objectives can be supported by AI applications. As AI technology continues to advance, it will play an even more significant role in shaping the future of learning and development in the organizations, but this should always follow a humanistic perspective and a critical approach.

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FairUMAP 2020: The 3rd Workshop on Fairness in User Modeling, Adaptation and Personalization

Abstract: The 3rd FairUMAP workshop brings together researchers working at the intersection of user modeling, adaptation, and personalization on the one hand, and bias, fairness and transparency in algorithmic systems on the other hand.

Cited by 3 publications

References 5 publications

Ethics of AI in Education: Towards a Community-Wide Framework

Ethics of AI in Education: Towards a Community-Wide Framework

Künstliche Intelligenz in der Hochschulbildung. Bildungssoziologische Perspektiven und Herausforderungen

AI Transformation for Learning in Organizations

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