Bunse, Mirko http://orcid.org/0000-0002-5515-6278
Pfahler, Lukas http://orcid.org/0000-0003-4012-4502
Chapter History
First Online: 17 September 2023
Ethical Implications
: Label noise, if not mitigated, can easily lead to the learning of incorrect prediction models, which is a particular danger for safety-critical applications. Moreover, label noise can perpetuate and amplify existing societal biases if appropriate countermeasures are not taken. The existence of these risks crucially requires research on the effects and the mitigation of different kinds of label noise. In this regard, we contribute a characterization and mitigation of PK-CCN, a novel instance of class-conditional label noise.Successful mitigation techniques can tempt stakeholders to take the risks of label noise even if alternative solutions exist. In fact, we advocate the employment of PK-CCN data in a use case where training data is otherwise obtained from simulations. In spite of such alternative solutions, a careful consideration of all risks is morally required. In our use case, the risks of learning from simulations are still vague while we have clearly described the effects of PK-CCN and have mitigated them through learning algorithms that are proven to be consistent. Our algorithms result in a reduction of computational requirements, which translates to a reduction in energy consumption. This improvement is a desirable property for combating climate change. We emphasize that other cases of label noise can involve risks that require different considerations.Astroparticle physics is a research field that is concerned with advancing our understanding of the cosmos and fundamental physics. While a deep understanding of the cosmos can inspire us to appreciate nature and take better care of our planet, the understanding of fundamental physics can eventually contribute to the development of technologies that improve the lives of many.
Conference Information
Conference Acronym: ECML PKDD
Conference Name: Joint European Conference on Machine Learning and Knowledge Discovery in Databases
Conference City: Turin
Conference Country: Italy
Conference Year: 2023
Conference Start Date: 18 September 2023
Conference End Date: 22 September 2023
Conference Number: 23
Conference ID: ecml2023
Conference URL: https://2023.ecmlpkdd.org/
Peer Review Information (provided by the conference organizers)
Type: Double-blind
Conference Management System: CMT
Number of Submissions Sent for Review: 829
Number of Full Papers Accepted: 196
Number of Short Papers Accepted: 0
Acceptance Rate of Full Papers: 24% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.
Average Number of Reviews per Paper: 3.63
Average Number of Papers per Reviewer: 4.5
External Reviewers Involved: Yes
Additional Info on Review Process: Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.