Governance and Regulations Implications on Machine Learning (Brief Announcement)
| Autor: | Marcel Zalmanovici, Sima Nadler, Orna Raz |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Training set
business.industry Computer science media_common.quotation_subject Corporate governance Privacy laws of the United States Machine learning computer.software_genre Training (civil) Data governance Production (economics) Quality (business) Artificial intelligence business computer media_common |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030209506 CSCML |
| DOI: | 10.1007/978-3-030-20951-3_19 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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