Increasing Transparency in Algorithmic-Decision-Making with Explainable AI

Bernhard Waltl; Roland Vogl

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Increasing Transparency in Algorithmic-Decision-Making with Explainable AI

机译：可解释AI的算法决策中提高透明度

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Systems that can autonomously make decisions based on artificial intelligence are becoming ubiquitous. These decisions can affect sensitive areas in our daily life. For example, the price of goods in e-commerce transactions (e.g., via dynamic pricing), or the credit rating of customers (e.g., via automatic credit scoring). The need for more transparency of algorithmic-decision-making (ADM) is not only moving into the focus of lawmakers, but it is also desirable from an engineering perspective. This brief paper investigates different levels of transparency in ADM, and discusses how and to which degree auditing and testing can increase transparency of ADM.

机译：可以基于人工智能自主地做出决策的系统变得无处不在。这些决定会影响我们日常生活中的敏感区域。例如，电子商务交易中的商品价格（例如，通过动态定价），或客户的信用等级（例如，通过自动信用评分）。对算法决策制定（ADM）的更高透明度的需求不仅成为立法者的关注重点，而且从工程角度来看也是合乎需要的。本文简要介绍了ADM的不同透明度，并讨论了审计和测试如何以及在何种程度上可以提高ADM的透明度。

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《DuD：Datenschutz und Datensicherheit》 |2018年第10期|613-617|共5页
作者
Bernhard Waltl; Roland Vogl;
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Mitarbeiter an der Fakultät für Informatik der Technischen Universität München;

The Stanford Center for Legal Informatics and Lecturer in Law at Stanford Law School;

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Increasing Transparency in Algorithmic-Decision-Making with Explainable AI

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