Marianna Capasso
Biographic Data
| ID | 6880716 |
|---|---|
| NAME | Marianna Capasso |
| GIVEN NAMES | Marianna |
| FAMILY NAME | Capasso |
| SIGNATURE | CAPASSO M |
| AFFILIATIONS | Utrecht University |
| ORCID | 0000-0003-0005-2356 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Synthetic data as meaningful data. On Responsibility in data ecosystems
Synthetic data – algorithmically generated data – has been considered a novel solution to the data scarcity issue, and a ‘technical fix’ able to fill the gap in areas where real data is sensitive or biased. Different narratives about the nature of synthetic data as either mirroring or replacing real data, alongside diverse evaluation metrics for measuring the fidelity and utility of such data, have proliferated across the machine learning fairnes…
On the Right to Work in the Age of Artificial Intelligence
Algorithmic human resource management (AHRM), the automation or augmentation of human resources-related decision-making with the use of artificial intelligence (AI)-enabled algorithms, can increase recruitment efficiency but also lead to discriminatory results and systematic disadvantages for marginalized groups in society. In this paper, we address the issue of equal treatment of workers and their fundamental rights when dealing with these AI re…
Responsible Social Robotics and the Dilemma of Control
Social robotic platforms are increasingly entering into human social practices, but to date the question of control and direction of innovation processes surrounding social robotics is still highly debated. In this paper I seek to make a two-fold contribution. Firstly, I critically discuss the “Collingridge Dilemma”, also known as the “dilemma of control” in Science and Technologies Studies, and contemporary efforts to address such a dilemma. I d…
Maximizing team synergy in AI-related interdisciplinary groups
In this paper, we propose a methodology to maximize the benefits of interdisciplinary cooperation in AI research groups. Firstly, we build the case for the importance of interdisciplinarity in research groups as the best means to tackle the social implications brought about by AI systems, against the backdrop of the EU Commission proposal for an Artificial Intelligence Act . As we are an interdisciplinary group, we address the multi-faceted impli…
Responsible nudging for social good
Traditional medical practices and relationships are changing given the widespread adoption of AI-driven technologies across the various domains of health and healthcare. In many cases, these new technologies are not specific to the field of healthcare. Still, they are existent, ubiquitous, and commercially available systems upskilled to integrate these novel care practices. Given the widespread adoption, coupled with the dramatic changes in pract…
Technology as Driver for Morally Motivated Conceptual Engineering
New technologies are the source of uncertainties about the applicability of moral and morally connotated concepts. These uncertainties sometimes call for conceptual engineering, but it is not often recognized when this is the case. We take this to be a missed opportunity, as a recognition that different researchers are working on the same kind of project can help solve methodological questions that one is likely to encounter. In this paper, we pr…
No prominent works on this page.
Responsible nudging for social good
Traditional medical practices and relationships are changing given the widespread adoption of AI-driven technologies across the various domains of health and healthcare. In many cases, these new technologies are not specific to the field of healthcare. Still, they are existent, ubiquitous, and commercially available systems upskilled to integrate these novel care practices. Given the widespread adoption, coupled with the dramatic changes in pract…
Technology as Driver for Morally Motivated Conceptual Engineering
New technologies are the source of uncertainties about the applicability of moral and morally connotated concepts. These uncertainties sometimes call for conceptual engineering, but it is not often recognized when this is the case. We take this to be a missed opportunity, as a recognition that different researchers are working on the same kind of project can help solve methodological questions that one is likely to encounter. In this paper, we pr…
Responsible Social Robotics and the Dilemma of Control
Social robotic platforms are increasingly entering into human social practices, but to date the question of control and direction of innovation processes surrounding social robotics is still highly debated. In this paper I seek to make a two-fold contribution. Firstly, I critically discuss the “Collingridge Dilemma”, also known as the “dilemma of control” in Science and Technologies Studies, and contemporary efforts to address such a dilemma. I d…
Maximizing team synergy in AI-related interdisciplinary groups
In this paper, we propose a methodology to maximize the benefits of interdisciplinary cooperation in AI research groups. Firstly, we build the case for the importance of interdisciplinarity in research groups as the best means to tackle the social implications brought about by AI systems, against the backdrop of the EU Commission proposal for an Artificial Intelligence Act . As we are an interdisciplinary group, we address the multi-faceted impli…
On the Right to Work in the Age of Artificial Intelligence
Algorithmic human resource management (AHRM), the automation or augmentation of human resources-related decision-making with the use of artificial intelligence (AI)-enabled algorithms, can increase recruitment efficiency but also lead to discriminatory results and systematic disadvantages for marginalized groups in society. In this paper, we address the issue of equal treatment of workers and their fundamental rights when dealing with these AI re…
Synthetic data as meaningful data. On Responsibility in data ecosystems
Synthetic data – algorithmically generated data – has been considered a novel solution to the data scarcity issue, and a ‘technical fix’ able to fill the gap in areas where real data is sensitive or biased. Different narratives about the nature of synthetic data as either mirroring or replacing real data, alongside diverse evaluation metrics for measuring the fidelity and utility of such data, have proliferated across the machine learning fairnes…
Computer Science (5 works) · Engineering (4 works) · Ethics and Social Impacts of AI (3 works) · Knowledge management (3 works) · Management science (3 works) · Psychology (3 works) · Artificial Intelligence (2 works) · Economics (2 works) · Engineering ethics (2 works) · Epistemology (2 works)