ArticleMinds and machines2021
Linking Human And Machine Behavior: A New Approach to Evaluate Training Data Quality for Beneficial Machine Learning.
Article in Minds and machines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Accelerated Design for Perovskite-Oxide-Based Photocatalysts Using Machine Learning Techniques.Materials (Basel, Switzerland) · 2024Article
- Outbound Data Legality Analysis in CPTPP Countries under the Environment of Cross-Border Data Flow Governance.Journal of environmental and public health · 2022Article
- From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation.Frontiers in robotics and AI · 2021Article
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Authors and funding
1 author.
Funding
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Abstract
Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that different social and psychological backgrounds of individuals correlate in practice with different modes of human-computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.