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Authors: Wolfgang Heidl ; Stefan Thumfart and Christian Eitzinger

Affiliation: Profactor GmbH, Austria

Keyword(s): Machine learning, Human diversity.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial Applications of AI ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems ; Uncertainty in AI

Abstract: While machine learning is most often learning from humans, training data is still considered to originate from a uniform black box. Under this paradigm systematic differences in training provided by multiple subjects are translated into unavoidable modeling error. When trained on a per-subject basis those differences indeed translate to systematic differences in the resulting model structure. We feel that the goal of creating humanlike capabilities or behavior in artificial systems can only be achieved if the diversity of humans is adequately considered.

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Paper citation in several formats:
Heidl, W.; Thumfart, S. and Eitzinger, C. (2012). HUMANS DIFFER: SO SHOULD MODELS - Systematic Differences Call for Per-subject Modeling. In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-8425-95-9; ISSN 2184-433X, SciTePress, pages 413-418. DOI: 10.5220/0003832904130418

@conference{icaart12,
author={Wolfgang Heidl. and Stefan Thumfart. and Christian Eitzinger.},
title={HUMANS DIFFER: SO SHOULD MODELS - Systematic Differences Call for Per-subject Modeling},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2012},
pages={413-418},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003832904130418},
isbn={978-989-8425-95-9},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - HUMANS DIFFER: SO SHOULD MODELS - Systematic Differences Call for Per-subject Modeling
SN - 978-989-8425-95-9
IS - 2184-433X
AU - Heidl, W.
AU - Thumfart, S.
AU - Eitzinger, C.
PY - 2012
SP - 413
EP - 418
DO - 10.5220/0003832904130418
PB - SciTePress

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