Considerations for a more ethical approach to data in AI: on data representation and infrastructure
- 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 ofData 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.…
Author: | Alice BairdGND, Björn SchullerORCiDGND |
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URN: | urn:nbn:de:bvb:384-opus4-794008 |
Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/79400 |
ISSN: | 2624-909XOPAC |
Parent Title (English): | Frontiers in Big Data |
Publisher: | Frontiers Media S.A. |
Type: | Article |
Language: | English |
Date of first Publication: | 2020/09/02 |
Publishing Institution: | Universität Augsburg |
Release Date: | 2020/09/03 |
Tag: | artificial intelligence; machine learning; ethical AI; decentralization; selection-bias |
Volume: | 3 |
First Page: | 25 |
DOI: | https://doi.org/10.3389/fdata.2020.00025 |
Institutes: | Fakultät für Angewandte Informatik |
Fakultät für Angewandte Informatik / Institut für Informatik | |
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Embedded Intelligence for Health Care and Wellbeing | |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Licence (German): | CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand) |