Governance and Regulations Implications on Machine Learning (Brief Announcement)

机译：机器学习的治理和法规含义（简短公告）

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摘要

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.

机译：机器学习系统的功效高度依赖于它们的训练数据以及在生产过程中接收到的数据。但是，当前的数据治理政策和隐私法律规定了何时以及如何使用个人和其他敏感数据。这会影响用于培训的个人数据的数量和质量，从而可能在模型中引入偏差和其他不准确性。当今的机制既没有提供（a）模型开发人员了解这一点的方法，也没有提供（b）减轻偏差的方法。本文解决了这两个挑战。

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来源
《International symposium on cyber security cryptography and machine learning》|2019年|208-211|共4页
会议地点 Beer-Sheva(IL)
作者
Sima Nadler; Orna Raz; Marcel Zalmanovici;
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作者单位

IBM Research Haifa Israel;

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会议组织
原文格式 PDF
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关键词
Data governance; Implications; Privacy; Machine learning;

机译：数据治理；含义；隐私;机器学习;

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Governance and Regulations Implications on Machine Learning (Brief Announcement)

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