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

Thilo Hagendorff¹

¹Cluster of Excellence "Machine Learning - New Perspectives for Science", University of Tuebingen, Tübingen, Germany.

Minds and Machines

|October 4, 2021

Summary

This summary is machine-generated.

This study introduces ethical data quality dimensions for machine learning, moving beyond technical metrics. It proposes a new filtering method for training data based on ethical assessments of user behavior.

Keywords:

Artificial intelligence Data quality Machine behavior Machine learning Technology ethics Training data

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Area of Science:

Machine Learning Ethics
Data Quality Assessment
Human-Computer Interaction

Background:

Machine learning performance is heavily influenced by data quality.
Current data quality metrics lack ethical considerations, despite the critical role of training data.
Ethical dimensions of data quality are essential for responsible AI development.

Purpose of the Study:

To introduce novel ethical dimensions for assessing data quality in supervised machine learning.
To propose a new framework for selecting training data based on ethical evaluations of user behavior.
To shift from a 'big data' approach to a more selective data processing methodology.

Main Methods:

Analysis of human-computer interaction patterns based on social and psychological factors.
Development of an ethical assessment framework for behavioral data.
Proposal of an innovative data filtering regime for machine learning training sets.

Main Results:

Identified new ethical dimensions of data quality beyond technical metrics.
Demonstrated the social relevance of varying data qualities in machine learning development.
Established a conceptual filter regime for ethically sourced training data.

Conclusions:

Ethical assessment of data is crucial for developing beneficial machine learning applications.
A selective approach to training data, guided by ethical principles, is necessary.
This research promotes responsible AI development for both industry and academia.