Framing data inequality as an information justice lens: Access, representation, and control in algorithmic decision-making
DOI:
https://doi.org/10.47989/ir31iConf64175Keywords:
Data inequality, Information justice, Data access inequality, Data representation inequality, Data control inequalityAbstract
Introduction. This study investigates how data inequality manifests within algorithmic systems and explores how an information justice framework can be applied to reframe these inequalities.
Method. In-depth interviews were conducted with 36 participants representing three distinct roles — algorithm design-related users, algorithm-for-work users, and algorithm-for-daily-life users — within the Chinese internet context. The qualitative data gathered capture their perceptions and lived experiences of data inequality in algorithmic systems.
Analysis. Thematic analysis was employed to identify and categorize forms of data inequality. These categories were then interpreted through the lens of information justice using the data, information, knowledge, wisdom (DIKW) model to illuminate their moral implications.
Results. The study provides empirical evidence for three forms of data inequality — access inequality, representation inequality, and control inequality. Each of these undermines information justice in distinct ways, particularly with respect to iParticipatory justice and iRecognitional justice, manifesting as exclusion from participation, distortion of identity, or erosion of data sovereignty.
Conclusion. Data inequality represents a systematic violation of information justice. Reframing data inequality as an information justice lens not only advances research on digital inequality but also deepens theoretical discussions at the intersection of data ethics, algorithm studies, and information science.
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