How to achieve integration?

Spindler, Mone; Booz, Sophia; Gieseler, Helya; Runschke, Sebastian; Wydra, Sven; Zinsmaier, Judith

doi:10.1007/978-3-658-26342-3_11

Cited by 7 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a rule of thumb, the extend and inclusiveness of this coalition correlates with the level of criticality of a particular application of algorithmic decision making (Wendehorst et al, 2019 ). By following such an integrated approach, unintended cognitive biases in the selection off intended training data biases can be tracked, but it can also be ensured that the selection of values and traits stands in accordance with democratic consensuses (Floridi et al, 2018 ; Brundage et al 2020 ; Daly et al 2019b ; Danaher et al 2017 ; Spindler et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Linking Human And Machine Behavior: A New Approach to Evaluate Training Data Quality for Beneficial Machine Learning

Hagendorff

2021

Minds & Machines

View full text Add to dashboard Cite

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 n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia.

show abstract

Section: Discussionmentioning

confidence: 99%