Malmö University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Ageing and sexing birds
Malmö University, Faculty of Culture and Society (KS), School of Arts and Communication (K3).ORCID iD: 0000-0001-5676-1931
2023 (English)Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

Ageing and sexing birds require specialist knowledge and training concerning which characteristics to focus on for different species. An expert can formulate an explanation for a classification using these characteristics and, additionally, identify anomalies. Some characteristics require practical training, for example, the difference between moulted and non-moulted feathers, while some knowledge, like feather taxonomy and moulting patterns, can be learned without extensive practical training. An explanation formulated for a classification, by a human, stands in sharp contrast to an explanation produced by a trained neural network. These machine explanations are more an answer to a how-question, related to the inner workings of the neural network, not an answer to a why-question, presenting domain-related characteristics useful for a domain expert. For machine-created explanations to be trustworthy neural networks require a static use context and representative independent and identically distributed training data. These prerequisites do seldom hold in real-world settings. Some challenges related to this are neural networks' inability to identify exemplars outside the training distribution and aligning internal knowledge creation with characteristics used in the target domain. These types of questions are central in the active research field of explainable artificial intelligence (XAI), but, there is a lack of hands-on experiments involving domain experts. This work aims to address the above issues with the goal of producing a prototype where domain experts can train a tool that builds on human expert knowledge in order to produce useful explanations. By using internalised domain expertise we aim at a tool that can produce useful explanations and even new insights for the domain. By working together with domain experts from Ottenby Observatory our goal is to address central XAI challenges and, at the same time, add new perspectives useful to determine age and sex on birds. 

Place, publisher, year, edition, pages
2023.
Keywords [en]
Birds, Explainable Artificial Intelligence, Neural Networks
National Category
Biological Sciences Human Computer Interaction Computer Engineering
Research subject
Interaktionsdesign
Identifiers
URN: urn:nbn:se:mau:diva-65068OAI: oai:DiVA.org:mau-65068DiVA, id: diva2:1828689
Conference
International Forum for Computer Vision in Ecology and Evolutionary Biology, Lund University, 18-20 September, 2023
Funder
The Crafoord Foundation, 20220631Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-19Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

https://docs.google.com/document/d/1LPqduaWjvVbqU8B-egflHXWIVaVLtnDshYe9R9gRIJc/edit#heading=h.7xb8ra84oay2

Authority records

Holmberg, Lars

Search in DiVA

By author/editor
Holmberg, Lars
By organisation
School of Arts and Communication (K3)
Biological SciencesHuman Computer InteractionComputer Engineering

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 165 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf