Exploring data ageism: What good data can(’t) tell us about the digital practices of older people?

Fernández-Ardèvol, Mireia; Grenier, Line

doi:10.1177/14614448221127261

Cited by 11 publications

(9 citation statements)

References 60 publications

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“… Chu et al (2022) note that there is hardly enough data about older adults available to train AI models toward the needs of this population. Available data infrastructures often show explicit or implicit age-related bias ( Fernández-Ardèvol & Grenier, 2022 ). This points to a major structural problem for the creation of inclusive and fair AI systems ( Stypinska, 2022 ) but also enables AI companies to collect “unique aged-data” to create new market segments.…”

Section: Black-boxing Ai In Gerontologymentioning

confidence: 99%

Addressing the Black Box of AI—A Model and Research Agenda on the Co-constitution of Aging and Artificial Intelligence

Gallistl,

Banday,

Berridge

et al. 2024

The Gerontologist

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Algorithmic technologies and (large) data infrastructures, often referred to as Artificial Intelligence (AI), have received increasing attention from gerontological research in the last decade. While there is much literature that dissects and explores the development, application, and evaluation of AI relevant for gerontology, this article makes a novel contribution by critically engaging with the theorizing in this growing field of research. We observe that gerontology’s engagement with AI is shaped by an interventionist logic that situates AI as a black box for gerontological research. We demonstrate how this black box logic has neglected many aspects of AI as a research topic for gerontology and discuss three classical concepts in gerontology to show how they can be used to open various black boxes of aging and AI in the areas: a) the datafication of aging, b) the political economy of AI and aging, and c) everyday engagements and embodiments of AI in later life. In the final chapter, we propose a model of the co-constitution of aging and AI that makes theoretical propositions to study the relational terrain between aging and AI and hence aims to open the black box of AI in gerontology beyond an interventionist logic.

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Section: Black-boxing Ai In Gerontologymentioning

confidence: 99%

Addressing the Black Box of AI—A Model and Research Agenda on the Co-constitution of Aging and Artificial Intelligence

Gallistl,

Banday,

Berridge

et al. 2024

The Gerontologist

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show abstract

“…Perhaps it has not been taken into account that older adults learn especially by trial and error, while younger adults learn more by anticipation. This is related to various cognitive deficits (Cerella, Poon, & Williams, 1980), which make it imperative to insist on the link with "advanced" technologies, in the sense that it does not become persecutory, but comforting (Berridge & Grigorovich, 2022;Fernández-Ardèvol & Grenier, 2022)…”

Section: Factors Predicting Technology Usementioning

confidence: 99%

Ageing, Empowerment and Technology New Opportunities for Cultural Traditions?

Klein

2024

IJSW

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As computers become an increasingly integral part of the lives of older adults, the study of computer used by older adults is becoming an increasingly relevant field of study. The aim of the study is a bibliographic review to better understand the relationship of new technologies with the population of older adults. One of the consequences that appears is that to encourage and facilitate the use of new technologies by this important age group, for both personal and professional benefit, it is necessary to understand its implication in the term of cultural traditions. It should be noted that all the researches consulted insist on the increasingly important role of the new technologis in the life of old people, and far from assuming that ageing people would have a rejection to the same, is possible to perceptive an operative and surprising use of the new technologies

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“…In turn, results and predictions can also be biased. The lack of representation of given social groups might be either caused or a consequence of existing strands of discrimination even in data deemed to be of very high quality (e.g., Fernández-Ardèvol & Grenier, 2022). Therefore, technical and social biases might reinforce each other.…”

Section: Frameworkmentioning

confidence: 99%

Quality Assessment and Biases in Reused Data

Fernández-Ardèvol

Rosales

2022

American Behavioral Scientist

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This article investigates digital and non-digital traces reused beyond the context of creation. A central idea of this article is that no (reused) dataset is perfect. Therefore, data quality assessment becomes essential to determine if a given dataset is “good enough” to be used to fulfill the users’ goals. Biases, a possible source of discrimination, have become a relevant data challenge. Consequently, it is appropriate to analyze whether quality assessment indicators provide information on potential biases in the dataset. We use examples representing two opposing sides regarding data access to reflect on the relationship between quality and bias. First, the European Union open data portal fosters the democratization of data and expects users to manipulate the databases directly to perform their analyses. Second, online behavioral advertising systems offer individualized promotional services but do not share the datasets supporting their design. Quality assessment is socially constructed, as there is not a universal definition but a set of quality dimensions, which might change for each professional context. From the users’ perspective, trust/credibility stands out as a relevant quality dimension in the two analyzed cases. Results show that quality indicators (whatever they are) provide limited information on potential biases. We suggest that data literacy is most needed among both open data users and clients of behavioral advertising systems. Notably, users must (be able to) understand the limitations of datasets for an optimal and bias-free interpretation of results and decision-making.

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Exploring data ageism: What good data can(’t) tell us about the digital practices of older people?

Cited by 11 publications

References 60 publications

Addressing the Black Box of AI—A Model and Research Agenda on the Co-constitution of Aging and Artificial Intelligence

Addressing the Black Box of AI—A Model and Research Agenda on the Co-constitution of Aging and Artificial Intelligence

Ageing, Empowerment and Technology New Opportunities for Cultural Traditions?

Quality Assessment and Biases in Reused Data

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