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Paper proposes reparative data work for AI safety

A new paper explores an alternative approach to data work, focusing on building datasets for AI safety systems through collaboration with individuals impacted by online harms. This method aims to reorient data work as a means of repair and redress, addressing issues of fair compensation and collective governance of AI datasets. The research highlights the importance of accountability in data production and advocates for centering those most affected by current dataset creation practices. AI

IMPACT Proposes a new framework for AI dataset creation that centers affected communities, potentially improving AI safety and ethical development.

RANK_REASON The cluster contains an academic paper published on arXiv.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Srravya Chandhiramowuli, Ding Wang, Alex Taylor ·

    Can Data Work be Reparative?

    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…

  2. arXiv cs.AI TIER_1 English(EN) · Alex Taylor ·

    Can Data Work be Reparative?

    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 d…