Studying Up Machine Learning Data

2022 ◽  
Vol 6 (GROUP) ◽  
pp. 1-14
Author(s):  
Milagros Miceli ◽  
Julian Posada ◽  
Tianling Yang
Keyword(s):  
Machine Learning ◽  
Social Contexts ◽  
Quality Data ◽  
Research Focus ◽  
Labor Conditions ◽  
The Social ◽  
Data Documentation ◽  
Societal Problems ◽  
Data Design ◽  
Learning Data

Research in machine learning (ML) has argued that models trained on incomplete or biased datasets can lead to discriminatory outputs. In this commentary, we propose moving the research focus beyond bias-oriented framings by adopting a power-aware perspective to "study up" ML datasets. This means accounting for historical inequities, labor conditions, and epistemological standpoints inscribed in data. We draw on HCI and CSCW work to support our argument, critically analyze previous research, and point at two co-existing lines of work within our research community \,---\,one bias-centered, the other power-aware. We highlight the need for dialogue and cooperation in three areas: data quality, data work, and data documentation. In the first area, we argue that reducing societal problems to "bias" misses the context-based nature of data. In the second one, we highlight the corporate forces and market imperatives involved in the labor of data workers that subsequently shape ML datasets. Finally, we propose expanding current transparency-oriented efforts in dataset documentation to reflect the social contexts of data design and production.

scholarly journals Estimating Degradation of Machine Learning Data Assets

2022 ◽  
Vol 14 (2) ◽  
pp. 1-15
Author(s):  
Lara Mauri ◽  
Ernesto Damiani
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Quantitative Estimate ◽  
Large Scale ◽  
Quality Measures ◽  
Data Sources ◽  
Quality Data ◽  
Convex Hulls ◽  
Training Set ◽  
Learning Data

Large-scale adoption of Artificial Intelligence and Machine Learning (AI-ML) models fed by heterogeneous, possibly untrustworthy data sources has spurred interest in estimating degradation of such models due to spurious, adversarial, or low-quality data assets. We propose a quantitative estimate of the severity of classifiers’ training set degradation: an index expressing the deformation of the convex hulls of the classes computed on a held-out dataset generated via an unsupervised technique. We show that our index is computationally light, can be calculated incrementally and complements well existing ML data assets’ quality measures. As an experimentation, we present the computation of our index on a benchmark convolutional image classifier.

scholarly journals The Nooscope manifested: AI as instrument of knowledge extractivism

AI & Society ◽  
2020 ◽  
Author(s):  
Matteo Pasquinelli ◽  
Vladan Joler
Keyword(s):  
Machine Learning ◽  
Statistical Model ◽  
Assembly Line ◽  
Learning Algorithm ◽  
Machine Intelligence ◽  
Training Dataset ◽  
The Social ◽  
The World ◽  
History Of ◽  
Learning Data

AbstractSome enlightenment regarding the project to mechanise reason. The assembly line of machine learning: data, algorithm, model. The training dataset: the social origins of machine intelligence. The history of AI as the automation of perception. The learning algorithm: compressing the world into a statistical model. All models are wrong, but some are useful. World to vector: the society of classification and prediction bots. Faults of a statistical instrument: the undetection of the new. Adversarial intelligence vs. statistical intelligence: labour in the age of AI.

The Social Contexts of Racial Discrimination in Sentencing

1986 ◽  
Vol 33 (3) ◽  
pp. 236-251 ◽  
Author(s):  
Martha A. Myers ◽  
Susette M. Talarico
Keyword(s):  
Racial Discrimination ◽  
Social Contexts ◽  
The Social

Entanglements between Agency and Vulnerability in the Phenomenon of Birth. Reflections on Children’s Expressions about ‘Being Born’

2020 ◽  
Vol 15 (2-2020) ◽  
pp. 135-146
Author(s):  
Catrin Heite ◽  
Veronika Magyar-Haas
Keyword(s):  
Social Studies ◽  
Social Construction ◽  
Well Being ◽  
Social Contexts ◽  
Living Environment ◽  
Social Actors ◽  
The Social ◽  
The Family ◽  
Cultural Embeddedness

Analogously to the works in the field of new social studies of childhood, this contribution deals with the concept of childhood as a social construction, in which children are considered as social actors in their own living environment, engaged in interpretive reproduction of the social. In this perspective the concept of agency is strongly stressed, and the vulnerability of children is not sufficiently taken into account. But in combining vulnerability and agency lies the possibility to consider the perspective of the subjects in the context of their social, political and cultural embeddedness. In this paper we show that what children say, what is important to them in general and for their well-being, is shaped by the care experiences within the family and by their social contexts. The argumentation for the intertwining of vulnerability and agency is exemplified by the expressions of an interviewed girl about her birth and by reference to philosophical concepts about birth and natality.

THE THE INDIGENOUS IDENTITY INTERPRETED AS A CATEGORY OF ANALYSIS IN POPULATION STUDIES

2017 ◽  
Vol 6 (Especial) ◽  
pp. 105
Author(s):  
Dante Choque-Caseres
Keyword(s):  
Indigenous Peoples ◽  
Methodological Approach ◽  
Indigenous Communities ◽  
Social Contexts ◽  
Indigenous Identity ◽  
Critical Thought ◽  
Northern Border ◽  
The Social ◽  
The Government ◽  
New Research

In Latin America, based on the recognition of Indigenous Peoples, the identification of gaps or disparities between the Indigenous and non-Indigenous population has emerged as a new research interest. To this end, capturing Indigenous identity is key to conducting certain analyses. However, the social contexts where the identity of Indigenous persons are (re)produced has been significantly altered. These changes are generated by the assimilation or integration of Indigenous communities into dominant national cultures. Within this context, limitations emerge in the use of this category, since Indigenous identity has a political and legal component related to the needs of the government. Therefore, critical thought on the use of Indigenous identity is necessary in an epistemological and methodological approach to research. This article argues that research about Indigenous Peoples should evaluate how Indigenous identity is included, for it is socially co-produced through the interaction of the State and its institutions. Thus, it would not necessarily constitute an explicative variable. By analyzing the discourse about Aymara Indigenous communities that has emerged in the northern border of Chile, this paper seeks to expose the logic used to define identity. Therefore, I conclude that the process of self-identification arises in supposed Indigenous people, built and/or reinforced by institutions, which should be reviewed from a decolonizing perspective and included in comparative research.

The Science and Art of Interviewing

2020 ◽  
Author(s):  
Kathleen Gerson ◽  
Sarah Damaske
Keyword(s):  
Social Research ◽  
Research Question ◽  
Research Process ◽  
Social Contexts ◽  
Science And Art ◽  
The Social ◽  
Interview Guide ◽  
Hidden Patterns ◽  
User Friendly ◽  
Depth Interviews

Qualitative interviewing is one of the most widely used methods in social research, but it is arguably the least well understood. To address that gap, this book offers a theoretically rigorous, empirically rich, and user-friendly set of strategies for conceiving and conducting interview-based research. Much more than a how-to manual, the book shows why depth interviewing is an indispensable method for discovering and explaining the social world—shedding light on the hidden patterns and dynamics that take place within institutions, social contexts, relationships, and individual experiences. It offers a step-by-step guide through every stage in the research process, from initially formulating a question to developing arguments and presenting the results. To do this, the book shows how to develop a research question, decide on and find an appropriate sample, construct an interview guide, conduct probing and theoretically focused interviews, and systematically analyze the complex material that depth interviews provide—all in the service of finding and presenting important new empirical discoveries and theoretical insights. The book also lays out the ever-present but rarely discussed challenges that interviewers routinely encounter and then presents grounded, thoughtful ways to respond to them. By addressing the most heated debates about the scientific status of qualitative methods, the book demonstrates how depth interviewing makes unique and essential contributions to the research enterprise. With an emphasis on the integral relationship between carefully crafted research and theory building, the book offers a compelling vision for what the “interviewing imagination” can and should be.

Personality and Social Contexts as Sources of Change and Continuity Across the Life Span

2018 ◽  
pp. 750-776
Author(s):  
Abigail J. Stewart ◽  
Kay Deaux
Keyword(s):  
Social Psychology ◽  
Life Span ◽  
Social Movement ◽  
Social Environment ◽  
Social Systems ◽  
Social Contexts ◽  
Life Stages ◽  
The People ◽  
The Social ◽  
Movement Participation

This chapter provides a framework designed to address how individual persons respond to changes and continuities in social systems and historical circumstances at different life stages and in different generations. We include a focus on systematic differences among the people who experience these changes in the social environment—differences both in the particular situations they find themselves in and in their personalities. Using examples from research on divorce, immigration, social movement participation, and experiences of catastrophic events, we make a case for an integrated personality and social psychology that extends the analysis across time and works within socially and historically important contexts.

scholarly journals Individual behavioural traits not social context affects learning about novel objects in archerfish

2021 ◽  
Vol 75 (3) ◽  
Author(s):  
Nick A. R. Jones ◽  
Helen C. Spence-Jones ◽  
Mike Webster ◽  
Luke Rendell
Keyword(s):  
Social Context ◽  
Social Environment ◽  
Social Contexts ◽  
Negative Effects ◽  
Behavioural Phenotype ◽  
Social Species ◽  
The Social ◽  
Social Animals ◽  
Novel Objects ◽  
The Individual

Abstract Learning can enable rapid behavioural responses to changing conditions but can depend on the social context and behavioural phenotype of the individual. Learning rates have been linked to consistent individual differences in behavioural traits, especially in situations which require engaging with novelty, but the social environment can also play an important role. The presence of others can modulate the effects of individual behavioural traits and afford access to social information that can reduce the need for ‘risky’ asocial learning. Most studies of social effects on learning are focused on more social species; however, such factors can be important even for less-social animals, including non-grouping or facultatively social species which may still derive benefit from social conditions. Using archerfish, Toxotes chatareus, which exhibit high levels of intra-specific competition and do not show a strong preference for grouping, we explored the effect of social contexts on learning. Individually housed fish were assayed in an ‘open-field’ test and then trained to criterion in a task where fish learnt to shoot a novel cue for a food reward—with a conspecific neighbour visible either during training, outside of training or never (full, partial or no visible presence). Time to learn to shoot the novel cue differed across individuals but not across social context. This suggests that social context does not have a strong effect on learning in this non-obligatory social species; instead, it further highlights the importance that inter-individual variation in behavioural traits can have on learning. Significance statement Some individuals learn faster than others. Many factors can affect an animal’s learning rate—for example, its behavioural phenotype may make it more or less likely to engage with novel objects. The social environment can play a big role too—affecting learning directly and modifying the effects of an individual’s traits. Effects of social context on learning mostly come from highly social species, but recent research has focused on less-social animals. Archerfish display high intra-specific competition, and our study suggests that social context has no strong effect on their learning to shoot novel objects for rewards. Our results may have some relevance for social enrichment and welfare of this increasingly studied species, suggesting there are no negative effects of short- to medium-term isolation of this species—at least with regards to behavioural performance and learning tasks.

scholarly journals Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic

2021 ◽  
Author(s):  
Giandomenico Di Domenico ◽  
Annamaria Tuan ◽  
Marco Visentin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
New Technologies ◽  
Fake News ◽  
Conspiracy Theories ◽  
Social Media Platform ◽  
The Social ◽  
Media Platform ◽  
Textual Cues ◽  
Twitter Users

AbstractIn the wake of the COVID-19 pandemic, unprecedent amounts of fake news and hoax spread on social media. In particular, conspiracy theories argued on the effect of specific new technologies like 5G and misinformation tarnished the reputation of brands like Huawei. Language plays a crucial role in understanding the motivational determinants of social media users in sharing misinformation, as people extract meaning from information based on their discursive resources and their skillset. In this paper, we analyze textual and non-textual cues from a panel of 4923 tweets containing the hashtags #5G and #Huawei during the first week of May 2020, when several countries were still adopting lockdown measures, to determine whether or not a tweet is retweeted and, if so, how much it is retweeted. Overall, through traditional logistic regression and machine learning, we found different effects of the textual and non-textual cues on the retweeting of a tweet and on its ability to accumulate retweets. In particular, the presence of misinformation plays an interesting role in spreading the tweet on the network. More importantly, the relative influence of the cues suggests that Twitter users actually read a tweet but not necessarily they understand or critically evaluate it before deciding to share it on the social media platform.

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