AI Ethics Paper Examines Vulnerability in Platform Data Practices Through YouTube Family Vlog Case Study
A study published on arXiv (identifier 2604.15990v1) suggests a new perspective on vulnerability within AI and data science, proposing that it arises from data practices instead of being an inherent trait. The paper addresses the ethical dilemmas posed by the abundance of data in a platform-driven society, emphasizing the decisions researchers make when analyzing existing datasets. It contends that ethical integrity is contingent upon the way data pipelines convert individuals into vulnerable data subjects. A case study involving AI for Social Good (AI4SG) illustrates this point, focusing on a journalist's request to apply computer vision to assess children in monetized YouTube 'family vlogs' for advocacy, highlighting a 'protection paradox' and the need for more robust reflexive ethical frameworks.
Key facts
- The paper is published on arXiv with the identifier 2604.15990v1.
- It proposes a conceptual shift from viewing vulnerability as static to seeing it as enacted through data practices.
- The ethical focus is on contexts of data abundance in platformized life.
- Ethical integrity is argued to depend on how technical pipelines transform individuals into data subjects.
- A case study involves AI for Social Good (AI4SG).
- The case study examines using computer vision to analyze child presence in monetized YouTube 'family vlogs'.
- This analysis was requested by a journalist for regulatory advocacy purposes.
- The case reveals a 'protection paradox' in such data practices.
Entities
Institutions
- arXiv