Abstract
Machine learning systems’ efficacy are highly dependent on their training data and the data they receive during production. However, current data governance policies and privacy laws dictate when and how personal and other sensitive data may be used. This affects the amount and quality of personal data included for training, potentially introducing bias and other inaccuracies into the model. Today’s mechanisms do not provide (a) a way for the model developer to know about this nor, (b) to alleviate the bias. This paper addresses both of these challenges.
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Sima Nadler, O.R., Zalmanovici, M.: Governance and regulations implications on machine learning. http://www.research.ibm.com/haifa/dept/vst/papers/Data_Governance.pdf
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Nadler, S., Raz, O., Zalmanovici, M. (2019). Governance and Regulations Implications on Machine Learning (Brief Announcement). In: Dolev, S., Hendler, D., Lodha, S., Yung, M. (eds) Cyber Security Cryptography and Machine Learning. CSCML 2019. Lecture Notes in Computer Science(), vol 11527. Springer, Cham. https://doi.org/10.1007/978-3-030-20951-3_19
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DOI: https://doi.org/10.1007/978-3-030-20951-3_19
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