Marta Gibin
Biographic Data
| ID | 6885487 |
|---|---|
| NAME | Marta Gibin |
| GIVEN NAMES | Marta |
| FAMILY NAME | Gibin |
| SIGNATURE | GIBIN M |
| AFFILIATIONS | University of Bologna |
| ORCID | 0000-0003-3546-465X |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Negotiating AI fairness
AI fairness is at the center of many debates, but there are different perspectives on what it entails. Currently, purely technical algorithmic fairness approaches dominate the scene, often neglecting a sufficiently well-rounded view of social implications and ignoring the voices of lay people. With the end goal of overcoming these issues, we investigate and synthesize the points of contact and differences among computer science, sociological and …
The risk of trust
This paper examines AI narratives in healthcare and, more specifically, how people attribute trust to the role of AI in breast cancer detection. To do so, it draws on a content analysis conducted on 701 online user comments to a New York Times article, using Sztompka’s trust framework to identify and scrutinise the different forms of trust at play. Findings offer critical insights for digital health studies and social science studies of risk and …
Training in Co-Creation as a Methodological Approach to Improve AI Fairness
Participatory design (PD) and co-creation (Co-C) approaches to building Artificial Intelligence (AI) systems have become increasingly popular exercises for ensuring greater social inclusion and fairness in technological transformation by accounting for the experiences of vulnerable or disadvantaged social groups; however, such design work is challenging in practice, partly because of the inaccessible domain of technical expertise inherent to AI d…
No prominent works on this page.
Training in Co-Creation as a Methodological Approach to Improve AI Fairness
Participatory design (PD) and co-creation (Co-C) approaches to building Artificial Intelligence (AI) systems have become increasingly popular exercises for ensuring greater social inclusion and fairness in technological transformation by accounting for the experiences of vulnerable or disadvantaged social groups; however, such design work is challenging in practice, partly because of the inaccessible domain of technical expertise inherent to AI d…
Negotiating AI fairness
AI fairness is at the center of many debates, but there are different perspectives on what it entails. Currently, purely technical algorithmic fairness approaches dominate the scene, often neglecting a sufficiently well-rounded view of social implications and ignoring the voices of lay people. With the end goal of overcoming these issues, we investigate and synthesize the points of contact and differences among computer science, sociological and …
The risk of trust
This paper examines AI narratives in healthcare and, more specifically, how people attribute trust to the role of AI in breast cancer detection. To do so, it draws on a content analysis conducted on 701 online user comments to a New York Times article, using Sztompka’s trust framework to identify and scrutinise the different forms of trust at play. Findings offer critical insights for digital health studies and social science studies of risk and …
Artificial Intelligence in Healthcare and Education (2 works) · Ethics and Social Impacts of AI (2 works) · AI in cancer detection (1 works) · Breast cancer (1 works) · Business (1 works) · Cancer (1 works) · Citizen journalism (1 works) · Computer Science (1 works) · Disadvantaged (1 works) · Engineering (1 works)