--- _id: '10799' abstract: - lang: eng text: "Because of the increasing popularity of machine learning methods, it is becoming important to understand the impact of learned components on automated decision-making systems and to guarantee that their consequences are beneficial to society. In other words, it is necessary to ensure that machine learning is sufficiently trustworthy to be used in real-world applications. This thesis studies two properties of machine learning models that are highly desirable for the\r\nsake of reliability: robustness and fairness. In the first part of the thesis we study the robustness of learning algorithms to training data corruption. Previous work has shown that machine learning models are vulnerable to a range\r\nof training set issues, varying from label noise through systematic biases to worst-case data manipulations. This is an especially relevant problem from a present perspective, since modern machine learning methods are particularly data hungry and therefore practitioners often have to rely on data collected from various external sources, e.g. from the Internet, from app users or via crowdsourcing. Naturally, such sources vary greatly in the quality and reliability of the\r\ndata they provide. With these considerations in mind, we study the problem of designing machine learning algorithms that are robust to corruptions in data coming from multiple sources. We show that, in contrast to the case of a single dataset with outliers, successful learning within this model is possible both theoretically and practically, even under worst-case data corruptions. The second part of this thesis deals with fairness-aware machine learning. There are multiple areas where machine learning models have shown promising results, but where careful considerations are required, in order to avoid discrimanative decisions taken by such learned components. Ensuring fairness can be particularly challenging, because real-world training datasets are expected to contain various forms of historical bias that may affect the learning process. In this thesis we show that data corruption can indeed render the problem of achieving fairness impossible, by tightly characterizing the theoretical limits of fair learning under worst-case data manipulations. However, assuming access to clean data, we also show how fairness-aware learning can be made practical in contexts beyond binary classification, in particular in the challenging learning to rank setting." alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Nikola H full_name: Konstantinov, Nikola H id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87 last_name: Konstantinov citation: ama: Konstantinov NH. Robustness and fairness in machine learning. 2022. doi:10.15479/at:ista:10799 apa: Konstantinov, N. H. (2022). Robustness and fairness in machine learning. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10799 chicago: Konstantinov, Nikola H. “Robustness and Fairness in Machine Learning.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:10799. ieee: N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute of Science and Technology Austria, 2022. ista: Konstantinov NH. 2022. Robustness and fairness in machine learning. Institute of Science and Technology Austria. mla: Konstantinov, Nikola H. Robustness and Fairness in Machine Learning. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10799. short: N.H. Konstantinov, Robustness and Fairness in Machine Learning, Institute of Science and Technology Austria, 2022. date_created: 2022-02-28T13:03:49Z date_published: 2022-03-08T00:00:00Z date_updated: 2023-10-17T12:31:54Z day: '08' ddc: - '000' degree_awarded: PhD department: - _id: GradSch - _id: ChLa doi: 10.15479/at:ista:10799 ec_funded: 1 file: - access_level: open_access checksum: 626bc523ae8822d20e635d0e2d95182e content_type: application/pdf creator: nkonstan date_created: 2022-03-06T11:42:54Z date_updated: 2022-03-06T11:42:54Z file_id: '10823' file_name: thesis.pdf file_size: 4204905 relation: main_file success: 1 - access_level: closed checksum: e2ca2b88350ac8ea1515b948885cbcb1 content_type: application/x-zip-compressed creator: nkonstan date_created: 2022-03-06T11:42:57Z date_updated: 2022-03-10T12:11:48Z file_id: '10824' file_name: thesis.zip file_size: 22841103 relation: source_file file_date_updated: 2022-03-10T12:11:48Z has_accepted_license: '1' keyword: - robustness - fairness - machine learning - PAC learning - adversarial learning language: - iso: eng month: '03' oa: 1 oa_version: Published Version page: '176' project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication_identifier: isbn: - 978-3-99078-015-2 issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '8724' relation: part_of_dissertation status: public - id: '10803' relation: part_of_dissertation status: public - id: '10802' relation: part_of_dissertation status: public - id: '6590' relation: part_of_dissertation status: public status: public supervisor: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 title: Robustness and fairness in machine learning type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2022' ... --- _id: '10802' abstract: - lang: eng text: "Addressing fairness concerns about machine learning models is a crucial step towards their long-term adoption in real-world automated systems. While many approaches have been developed for training fair models from data, little is known about the robustness of these methods to data corruption. In this work we consider fairness-aware learning under worst-case data manipulations. We show that an adversary can in some situations force any learner to return an overly biased classifier, regardless of the sample size and with or without degrading\r\naccuracy, and that the strength of the excess bias increases for learning problems with underrepresented protected groups in the data. We also prove that our hardness results are tight up to constant factors. To this end, we study two natural learning algorithms that optimize for both accuracy and fairness and show that these algorithms enjoy guarantees that are order-optimal in terms of the corruption ratio and the protected groups frequencies in the large data\r\nlimit." acknowledgement: The authors thank Eugenia Iofinova and Bernd Prach for providing feedback on early versions of this paper. This publication was made possible by an ETH AI Center postdoctoral fellowship to Nikola Konstantinov. article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Nikola H full_name: Konstantinov, Nikola H id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87 last_name: Konstantinov - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0002-4561-241X citation: ama: Konstantinov NH, Lampert C. Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. 2022;23:1-60. apa: Konstantinov, N. H., & Lampert, C. (2022). Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. ML Research Press. chicago: Konstantinov, Nikola H, and Christoph Lampert. “Fairness-Aware PAC Learning from Corrupted Data.” Journal of Machine Learning Research. ML Research Press, 2022. ieee: N. H. Konstantinov and C. Lampert, “Fairness-aware PAC learning from corrupted data,” Journal of Machine Learning Research, vol. 23. ML Research Press, pp. 1–60, 2022. ista: Konstantinov NH, Lampert C. 2022. Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. 23, 1–60. mla: Konstantinov, Nikola H., and Christoph Lampert. “Fairness-Aware PAC Learning from Corrupted Data.” Journal of Machine Learning Research, vol. 23, ML Research Press, 2022, pp. 1–60. short: N.H. Konstantinov, C. Lampert, Journal of Machine Learning Research 23 (2022) 1–60. date_created: 2022-02-28T14:05:42Z date_published: 2022-05-01T00:00:00Z date_updated: 2023-09-26T10:44:37Z day: '01' ddc: - '004' department: - _id: ChLa external_id: arxiv: - '2102.06004' file: - access_level: open_access checksum: 9cac897b54a0ddf3a553a2c33e88cfda content_type: application/pdf creator: kschuh date_created: 2022-07-12T15:08:28Z date_updated: 2022-07-12T15:08:28Z file_id: '11570' file_name: 2022_JournalMachineLearningResearch_Konstantinov.pdf file_size: 551862 relation: main_file success: 1 file_date_updated: 2022-07-12T15:08:28Z has_accepted_license: '1' intvolume: ' 23' keyword: - Fairness - robustness - data poisoning - trustworthy machine learning - PAC learning language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: 1-60 publication: Journal of Machine Learning Research publication_identifier: eissn: - 1533-7928 issn: - 1532-4435 publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: record: - id: '10799' relation: dissertation_contains status: public - id: '13241' relation: shorter_version status: public scopus_import: '1' status: public title: Fairness-aware PAC learning from corrupted data tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 23 year: '2022' ...