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麻烦还是合理的威胁?(社会)科学家对机器学习的批评Bugbears or Legitimate Threats? (Social) Scientists' Criticisms of Machine Learning |
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| 课程网址: | http://videolectures.net/kdd2014_mullainathan_machine_learning/ |
| 主讲教师: | Sendhil Mullainathan |
| 开课单位: | 哈佛大学 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Social scientists increasingly criticize the use of machine learning techniques to understand human behavior. Criticisms include: (1) They are atheoretical and hence of limited scientific value; (2) They do not address causality and are hence of limited policy value; and (3) They are uninterpretable and hence of limited generalizability value (outside contexts very narrowly similar to the training dataset). These criticisms, I argue, miss the enormous opportunity offered by ML techniques to fundamentally improve the practice of empirical social science. Yet each criticism does contain a grain of truth and overcoming them will require innovations to existing methodologies. Some of these innovations are being developed today and some are yet to be tackled. I will in this talk sketch (1) what these innovations look like or should look like; (2) why they are needed; and (3) the technical challenges they raise. I will illustrate my points using a set of applications that range from financial markets to social policy problems to computational models of basic psychological processes. This talk describes joint work with Jon Kleinberg and individual projects with Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, Anuj Shah, Chenhao Tan, Mike Yeomans and Tom Zimmerman. |
| 关 键 词: | 机器学习; 科学价值; 因果关系; 人类行为 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-04-03：chenxin01 |
| 最后编审: | 2023-05-22：chenxin01 |
| 阅读次数: | 17 |