Data shapes the development of Artificial Intelligence (AI) as we currently know it, and for many years centralized networking infrastructures have dominated both the sourcing and subsequent use of such data. Research suggests that centralized approaches result in poor representation, and as AI is now integrated more in daily life, there is a need for efforts to improve on this. The AI research community has begun to explore managing data infrastructures more democratically, finding that decentralized networking allows for more transparency which can alleviate core ethical concerns, such as selection-bias. With this in mind, herein, we present a mini-survey framed around data representation and data infrastructures in AI. We outline four key considerations (auditing, benchmarking, confidence and trust, explainability and interpretability) as they pertain to data-driven AI, and propose that reflection of them, along with improved interdisciplinary discussion may aid the mitigation of data-based AI ethical concerns, and ultimately improve individual wellbeing when interacting with AI.
Keywords: artificial intelligence; decentralization; ethical AI; machine learning; selection-bias.
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