Itzelle Medina Perea
Biographic Data
| ID | 8575063 |
|---|---|
| NAME | Itzelle Medina Perea |
| GIVEN NAMES | Itzelle Medina |
| FAMILY NAME | Perea |
| SIGNATURE | PEREA I M |
| AFFILIATIONS | University of Sheffield |
| ORCID | 0000-0003-1702-1484 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Navigating the automation of the arts
AI-generated art has its roots in the 1960s, driven by pioneering artists experimenting with algorithmic and rule-based systems. In contrast, over the past decade, new AI art generators, fuelled by increased computing power, are often associated with extractive data practices spearheaded and monopolised by big tech companies. Both historical and contemporary approaches involve learning patterns from existing data to generate new content; however,…
Feeding the machine
This paper examines the human implications of AI's ‘data dilemma' in three different and contrasting sectors: pharmaceuticals, higher education, and the arts. The ‘data dilemma' refers to the challenge of obtaining sufficient and suitable data to effectively train AI algorithms. The research, conducted in the UK, involved interviews, focus groups, and observations with 65 practitioners employed across these three sectors. The findings reveal that…
What ifs
This paper explores how non-experts reflect on and come to understand ‘data uses’, a phrase we used to refer to data collection, analysis and sharing. In recent years, research into what people think and feel about data uses has proliferated, whereas this paper focuses on how they do their thinking and feeling. We argue that imagining – that is, building or creating a mental image of something that is not present – is an important aspect of refle…
Data saves lives’
In this paper, we bring together the concepts of data valences and data journeys to examine how ideational and material factors work together to shape the movement of health data from the UK healthcare sector to universities for reuse in research. Specifically, we focus on the interaction of university-based researchers’ constructs about data with the material conditions of health data circulation in the UK and how these dynamics drive greater ci…
How people connect fairness and equity when they talk about data uses
As a mechanism for addressing data-related harms, fairness has been subjected to considerable criticism, seen as failing to acknowledge the power relationships that produce said harms, or as a ‘floating signifier’ devoid of specific meaning. In contrast to fairness, it is argued that equity does a better job of recognising data-related harms. Criticisms such as these emerge in specific cultural contexts and rarely acknowledge everyday understandi…
Beliefs, Values and Emotions in Practitioners’ Engagements With Learning Analytics in Higher Education
Internet entrepreneurs, EdTech companies, AI enthusiasts, and other powerful stakeholders around the world have promoted the idea that big data and learning analytics (LA) have the potential to revolutionise education. LA, defined as the continuous measurement, collection, analysis and reporting of data about learners and their context (Gašević et al., 2015, p. 1), is increasingly being used to track and evaluate what students do in internet-medi…
No prominent works on this page.
Beliefs, Values and Emotions in Practitioners’ Engagements With Learning Analytics in Higher Education
Internet entrepreneurs, EdTech companies, AI enthusiasts, and other powerful stakeholders around the world have promoted the idea that big data and learning analytics (LA) have the potential to revolutionise education. LA, defined as the continuous measurement, collection, analysis and reporting of data about learners and their context (Gašević et al., 2015, p. 1), is increasingly being used to track and evaluate what students do in internet-medi…
What ifs
This paper explores how non-experts reflect on and come to understand ‘data uses’, a phrase we used to refer to data collection, analysis and sharing. In recent years, research into what people think and feel about data uses has proliferated, whereas this paper focuses on how they do their thinking and feeling. We argue that imagining – that is, building or creating a mental image of something that is not present – is an important aspect of refle…
Data saves lives’
In this paper, we bring together the concepts of data valences and data journeys to examine how ideational and material factors work together to shape the movement of health data from the UK healthcare sector to universities for reuse in research. Specifically, we focus on the interaction of university-based researchers’ constructs about data with the material conditions of health data circulation in the UK and how these dynamics drive greater ci…
How people connect fairness and equity when they talk about data uses
As a mechanism for addressing data-related harms, fairness has been subjected to considerable criticism, seen as failing to acknowledge the power relationships that produce said harms, or as a ‘floating signifier’ devoid of specific meaning. In contrast to fairness, it is argued that equity does a better job of recognising data-related harms. Criticisms such as these emerge in specific cultural contexts and rarely acknowledge everyday understandi…
Feeding the machine
This paper examines the human implications of AI's ‘data dilemma' in three different and contrasting sectors: pharmaceuticals, higher education, and the arts. The ‘data dilemma' refers to the challenge of obtaining sufficient and suitable data to effectively train AI algorithms. The research, conducted in the UK, involved interviews, focus groups, and observations with 65 practitioners employed across these three sectors. The findings reveal that…
Navigating the automation of the arts
AI-generated art has its roots in the 1960s, driven by pioneering artists experimenting with algorithmic and rule-based systems. In contrast, over the past decade, new AI art generators, fuelled by increased computing power, are often associated with extractive data practices spearheaded and monopolised by big tech companies. Both historical and contemporary approaches involve learning patterns from existing data to generate new content; however,…
Ethics and Social Impacts of AI (3 works) · Sociology (3 works) · Computer Science (2 works) · Data Analysis and Archiving (2 works) · Data science (2 works) · Ethics in Clinical Research (2 works) · Information Systems Theories and Implementation (2 works) · Psychology (2 works) · Aesthetic Perception and Analysis (1 work) · Analytics (1 work)