Machine Learning Human Rights and Wrongs: How the Successes and Failures of Supervised Learning Algorithms Can Inform the Debate About Information Effects
Machine Learning Human Rights and Wrongs: How the Successes and Failures of Supervised Learning Algorithms Can Inform the Debate About Information Effects
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机器学习人权与错误：监督学习算法的成功和失败如何为有关信息效应的争论提供信息
DOI：
10.1017/pan.2018.11
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发表时间：
2019
影响因子：
5.4
通讯作者：
Colaresi, Michael
中科院分区：
文献类型：
--
作者：
Greene, Kevin T.;Park, Baekkwan;Colaresi, Michael
关于过去 30 年来人权标准是否发生了变化一直存在争论。支持或反对这种转变的证据依赖于人类编码员阅读人权报告文本所创建的指标。为了帮助解决这一争论，我们建议将改变标准的问题转化为监督学习问题。从这个角度来看，随着时间的推移应用一致的标准意味着从报告中的文本特征到人类编码分数的时间常数映射。或者，如果滥用的含义随着时间的推移而演变，那么相同的文本特征将在不同的时间被标记为不同的数字分数。当然，虽然从自然语言到数字人权分数的映射是一个高度复杂的函数，但我们表明，当我们根据旧的和新的观察集训练各种算法以学习如何自动用分数标记文本时，这两种不同的数据生成过程意味着不同的总体准确性模式。我们的结果与人权标准随着时间而变化的预期是一致的。
There is an ongoing debate about whether human rights standards have changed over the last 30 years. The evidence for or against this shift relies upon indicators created by human coders reading the texts of human rights reports. To help resolve this debate, we suggest translating the question of changing standards into a supervised learning problem. From this perspective, the application of consistent standards over time implies a time-constant mapping from the textual features in reports to the human coded scores. Alternatively, if the meaning of abuses have evolved over time, then the same textual features will be labeled with different numerical scores at distinct times. Of course, while the mapping from natural language to numerical human rights score is a highly complicated function, we show that these two distinct data generation processes imply divergent overall patterns of accuracy when we train a wide variety of algorithms on older versus newer sets of observations to learn how to automatically label texts with scores. Our results are consistent with the expectation that standards of human rights have changed over time.
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影响因子：
3.7
作者：
Fariss CJ;Linder FJ;Jones ZM;Crabtree CD;Biek MA;Ross AS;Kaur T;Tsai M
通讯作者：
Tsai M
影响因子：
1
作者：
Clark, Ann Marie;Sikkink, Kathryn
通讯作者：
Sikkink, Kathryn
影响因子：
1.6
作者：
M. Ward
通讯作者：
M. Ward
重温“言辞与现实”
影响因子：
1.9
作者：
David L. Richards
通讯作者：
David L. Richards
影响因子：
6.8
作者：
Fariss, Christopher J.
通讯作者：
Fariss, Christopher J.