Educating Software and AI Stakeholders About Algorithmic Fairness, Accountability, Transparency and Ethics

Bogina, Veronika; Hartman, Alan; Kuflik, Tsvi; Tal, Avital Shulner

doi:10.1007/s40593-021-00248-0

Cited by 49 publications

(23 citation statements)

References 48 publications

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“…Some of the records that did present interventions for teachers ( n = 4) were excluded because they did not evaluate (in its broadest definition) its effect on bias/equitable teaching. These either focused on the evaluation of algorithmic bias (Whalen and Glinksi, 1976 ; Bogina et al, 2021 ), targeted the bias of the learners, rather than the teachers (Dinnar et al, 2021 ), or targeted teachers' biases toward particular educational techniques, such as written homework and clicker polls, rather than bias against learners (Duzhin and Gustafsson, 2018 ).…”

Section: Resultsmentioning

confidence: 99%

Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biases

Gauthier

Rizvi

Cukurova

et al. 2022

Front. Artif. Intell.

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Data-driven technologies for education, such as artificial intelligence in education (AIEd) systems, learning analytics dashboards, open learner models, and other applications, are often created with an aspiration to help teachers make better, evidence-informed decisions in the classroom. Addressing gender, racial, and other biases inherent to data and algorithms in such applications is seen as a way to increase the responsibility of these systems and has been the focus of much of the research in the field, including systematic reviews. However, implicit biases can also be held by teachers. To the best of our knowledge, this systematic literature review is the first of its kind to investigate what kinds of teacher biases have been impacted by data-driven technologies, how or if these technologies were designed to challenge these biases, and which strategies were most effective at promoting equitable teaching behaviors and decision making. Following PRISMA guidelines, a search of five databases returned n = 359 records of which only n = 2 studies by a single research team were identified as relevant. The findings show that there is minimal evidence that data-driven technologies have been evaluated in their capacity for supporting teachers to make less biased decisions or promote equitable teaching behaviors, even though this capacity is often used as one of the core arguments for the use of data-driven technologies in education. By examining these two studies in conjunction with related studies that did not meet the eligibility criteria during the full-text review, we reveal the approaches that could play an effective role in mitigating teachers' biases, as well as ones that may perpetuate biases. We conclude by summarizing directions for future research that should seek to directly confront teachers' biases through explicit design strategies within teacher tools, to ensure that the impact of biases of both technology (including data, algorithms, models etc.) and teachers are minimized. We propose an extended framework to support future research and design in this area, through motivational, cognitive, and technological debiasing strategies.

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Section: Resultsmentioning

confidence: 99%

Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biases

Gauthier

Rizvi

Cukurova

et al. 2022

Front. Artif. Intell.

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“…To fill the disconnect between the disability and AI community, the process of stewarding accessibility datasets requires greater transparency of data use as well as awareness [17], especially to challenge inclusivity issues that are pressing for marginalized communities [30,76].…”

Section: Bringing Trustworthiness In Datamentioning

confidence: 99%

Contributing to Accessibility Datasets: Reflections on Sharing Study Data by Blind People

Kamikubo

Lee

Kacorri

2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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To ensure that AI-infused systems work for disabled people, we need to bring accessibility datasets sourced from this community in the development lifecycle. However, there are many ethical and privacy concerns limiting greater data inclusion, making such datasets not readily available. We present a pair of studies where 13 blind participants engage in data capturing activities and reflect with and without probing on various factors that influence their decision to share their data via an AI dataset. We see how different factors influence blind participants' willingness to share study data as they assess risk-benefit tradeoffs. The majority support sharing of their data to improve technology but also express concerns over commercial use, associated metadata, and the lack of transparency about the impact of their data. These insights have implications for the development of responsible practices for stewarding accessibility datasets, and can contribute to broader discussions in this area. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Accessibility; • Social and professional topics → People with disabilities; • Security and privacy → Human and societal aspects of security and privacy.

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“…There is the danger that technological engineers, software developers and businesspeople do not necessarily have the same goals and incentives as individual consumers, ordinary citizens, policy makers, or societies at large. As such, given the rapid advancements, there may be the risk that the needs of some stakeholders would be overheard [13,15,27,31,71,95]. This is where digital humanists can assume a crucial role in these dynamics: with a focus not primarily on profit or technological concerns, they should research and discuss how such changes impact humans and societies, preserving a critical stance towards these developments and at the same time making constructive suggestions to help engineers who often have less time to dwell on ethical concerns, or have to meet deadlines and revenue goals.…”

Section: Implications For the (Digital) Humanitiesmentioning

confidence: 99%

The rapid competitive economy of machine learning development: a discussion on the social risks and benefits

Walter

2023

AI Ethics

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Research in artificial intelligence (AI) has started in the twentieth century but it was not until 2012 that modern models of artificial neural networks aided the machine learning process considerably so that in the past ten years, both computer vision as well as natural language processing have become increasingly better. AI developments have accelerated rapidly, leaving open questions about the potential benefits and risks of these dynamics and how the latter might be managed. This paper discusses three major risks, all lying in the domain of AI safety engineering: the problem of AI alignment, the problem of AI abuse, and the problem of information control. The discussion goes through a short history of AI development, briefly touching on the benefits and risks, and eventually making the case that the risks might potentially be mitigated through strong collaborations and awareness concerning trustworthy AI. Implications for the (digital) humanities are discussed.

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Educating Software and AI Stakeholders About Algorithmic Fairness, Accountability, Transparency and Ethics

Cited by 49 publications

References 48 publications

Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biases

Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biases

Contributing to Accessibility Datasets: Reflections on Sharing Study Data by Blind People

The rapid competitive economy of machine learning development: a discussion on the social risks and benefits

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