Integrating the Management of Personal Data Protection and Open
            Science with Research Ethics

Lewis, David; Moorkens, Joss; Fatema, Kaniz

doi:10.18653/v1/w17-1607

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…While these laws do have gaps (Parks, 2017), they are largely robust and respected by technologists. More recent actions like the EU General Data Protection Regulation (GDPR) have also had meaningful impact on NLP research and data collection (Lewis et al, 2017). Legally, aggregating student data in order to develop and improve edtech provides a benefit to students and thus does not violate any law (Brinkman, 2013) -but scholars continue to ask ethical questions on how to account for student privacy and control (Morris and Stommel, 2018), and what data is being collected (Mieskes, 2017).…”

Section: Surveillance Capitalism In Edtechmentioning

confidence: 99%

Equity Beyond Bias in Language Technologies for Education

Mayfield¹,

Madaio²,

Prabhumoye³

et al. 2019

Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

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There is a long record of research on equity in schools. As machine learning researchers begin to study fairness and bias in earnest, language technologies in education have an unusually strong theoretical and applied foundation to build on. Here, we introduce concepts from culturally relevant pedagogy and other frameworks for teaching and learning, and identify future work on equity in NLP. We present case studies in a range of topics like intelligent tutoring systems, computer-assisted language learning, automated essay scoring, and sentiment analysis in classrooms, and provide an actionable agenda for research.

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Section: Surveillance Capitalism In Edtechmentioning

confidence: 99%

Equity Beyond Bias in Language Technologies for Education

Mayfield¹,

Madaio²,

Prabhumoye³

et al. 2019

Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

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“…They discussed the concepts of exclusion, overgeneralization, bias confirmation, topic under-and overexposure, and dual use from the perspective of NLP research. A lot of work followed this discussion and made contributions towards ethical frameworks and design practices (Leidner and Plachouras, 2017;Parra Escartín et al, 2017;Schnoebelen, 2017;Schmaltz, 2018), data handling practices (Lewis et al, 2017;Mieskes, 2017) and specific domains like education Loukina et al, 2019), healthcare (Šuster et al, 2017;Benton et al, 2017) and conversational agents (Cercas Curry and Rieser, 2018;. Our paper does not focus on a particular domain but calls for attention towards various NLP systems and what ethical issues may arise in them.…”

Section: Ethics In Nlpmentioning

confidence: 99%

Case Study: Deontological Ethics in NLP

Prabhumoye

Boldt²,

Salakhutdinov

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Recent work in natural language processing (NLP) has focused on ethical challenges such as understanding and mitigating bias in data and algorithms; identifying objectionable content like hate speech, stereotypes and offensive language; and building frameworks for better system design and data handling practices. However, there has been little discussion about the ethical foundations that underlie these efforts. In this work, we study one ethical theory, namely deontological ethics, from the perspective of NLP. In particular, we focus on the generalization principle and the respect for autonomy through informed consent. We provide four case studies to demonstrate how these principles can be used with NLP systems. We also recommend directions to avoid the ethical issues in these systems.

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“…While some translated content may be subject to confidentiality constraints, in general meta-data associated with such content, for example, the identity of translators, post-editors, and translation quality assessors, is not shared with appropriators. This is in part to avoid the need to address personal data regulations, which is also a concern with performance monitoring meta-data such as key-logging, which can act as a biometric to re-identify post-editors (Lewis et al, 2017). This meta-data information rule means, however, that appropriation is extremely limited in acknowledging the contributions of individuals.…”

Section: Rules-in-use: Post-editing Machine Translationmentioning

confidence: 99%

A Rights-Based Approach to Trustworthy AI in Social Media

Lewis

Moorkens

2020

Social Media + Society

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Social media platforms increasingly use powerful artificial intelligence (AI) that are fed by the vast flows of digital content that may be used to analyze user behavior, mental state, and physical context. New forms of AI-generated content and AI-driven virtual agents present new forms of risks in social media use, the harm of which will be difficult to predict. Delivering trustworthy social media will therefore be increasingly predicated on effectively governing the trustworthiness of its AI components. In this article, we examine different approaches to the governance AI and the Big Data processing that drives it being explored. We identify a potential over-reliance on individual rights at the expense of consideration of collective rights. In response, we propose a collective approach to AI data governance grounded in a legal proposal for universal, non-exclusive data ownership right. We use the Institutional Analysis and Development (IAD) framework to explore the relative costs and benefits on stakeholders in two use cases, one focused on digital content consumers the other focused on digital content knowledge workers. Following an analysis that looks at self-regulation and industry-state co-regulation, we propose governance through shared data ownership. In this way, future social media platforms may be able to maintain trust in their use of AI by committing to no datafication without representation.

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Integrating the Management of Personal Data Protection and Open Science with Research Ethics

Cited by 5 publications

References 6 publications

Equity Beyond Bias in Language Technologies for Education

Equity Beyond Bias in Language Technologies for Education

Case Study: Deontological Ethics in NLP

A Rights-Based Approach to Trustworthy AI in Social Media

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