Considerations for a More Ethical Approach to Data in AI: On Data Representation and Infrastructure
Year: 2020
Type: review
Abstract: Data forms the development of Artificial Intelligence (AI) as we currently know it, and for many years centralised networking infrastructures have dominated both the sourcing and subsequent use of such data. Research suggests that centralised 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 now begun to explore managing data infrastructures more democratically, finding that decentralised networking allows for more transparency and therefore can alleviate core ethical concerns such as selection-bias. With this in mind, herein, we present a survey-based overview 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 incorporation of these aspects, along with improved interdisciplinary discussion may aid the mitigation of a plethora of ethical concerns relating to data and AI.
Source: Frontiers in Big Data
Authors: Alice Baird, Björn Schuller
Institutions: University of Augsburg, Imperial College London
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,150
OpenAlex ID: W3082892415