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Is it ethical to avoid error analysis?
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0003-4973-9255
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0002-0535-1761
2017 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Machine learning algorithms tend to create more accurate models with the availability of large datasets. In some cases, highly accurate models can hide the presence of bias in the data. There are several studies published that tackle the development of discriminatory-aware machine learning algorithms. We center on the further evaluation of machine learning models by doing error analysis, to understand under what conditions the model is not working as expected. We focus on the ethical implications of avoiding error analysis, from a falsification of results and discrimination perspective. Finally, we show different ways to approach error analysis in non-interpretable machine learning algorithms such as deep learning.

Place, publisher, year, edition, pages
2017.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-37928OAI: oai:DiVA.org:hj-37928DiVA, id: diva2:1159962
Conference
2017 Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2017)
Funder
Knowledge Foundation, 20170032]Available from: 2017-11-22 Created: 2017-11-24 Last updated: 2018-01-13Bibliographically approved

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Lavesson, Niklas

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