arXiv:2606.09408v1 Announce Type: cross Abstract: We present an ethnographic study of an alternative approach to data work, developed by a civic-tech initiative that builds datasets for training and benchmarking online safety systems. They aim to respond to online safety concerns from a feminist perspective, by building safety datasets collaboratively with those most impacted by online harms. In this paper, we examine how this approach aims to reorient data work as a site for repair and redress, and trace the struggles they encounter in the process. Specifically, we draw attention to the chall
As AI systems become more prevalent and impactful, ethical considerations and efforts to mitigate harm, particularly for marginalized groups, are gaining urgency.
This highlights a growing focus on ethical AI and data governance, demonstrating attempts to create more equitable and less biased foundational datasets for AI models.
Approaches to data collection for AI, traditionally focused on scale and efficiency, may increasingly incorporate reparative and socially conscious methodologies.
- · Civic-tech initiatives
- · Marginalized communities
- · Ethical AI developers
- · Developers ignoring ethical data practices
- · AI systems perpetuating bias
Increased attention and funding for alternative, ethically-driven data annotation and dataset creation methods.
Development of industry standards and certifications for 'reparative' or 'ethical' datasets, influencing model development and deployment.
Long-term reduction in AI system harms and biases, potentially leading to greater public trust and broader adoption in sensitive areas.
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Read at arXiv cs.AI