互动任务学习的伦理问题和挑战

Matthias Scheutz
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摘要

离子来自低水平数据。此外,能够向人类讲师寻求帮助(例如,解释或行动的替代方案)在数据驱动的方法中不存在;如果答案不在数据中,那么世界上任何统计过程都无法从中提取答案。然而,ITL也有潜在的缺点,尤其是对人类而言。人类必须在可能较长的时间内与自动机器进行接触,从而使机器能够成功地获取知识。有些影响将局限于教学互动本身,而其他影响可能远远超出教学环境。在下面,我们检查三类道德方面,因为他们出现在ITL: 1。教授的内容是什么?相关的风险是什么?2. 人机指令的动态是什么?3.ITL将对人类教师和社会产生什么影响?
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ethical Aspects and Challenges for Interactive Task Learning
ions from low-level data. Moreover, being able to ask the human instructor for help (e.g., explanations or alternatives for action) does not exist in a data-driven approach; if the answer is not in the data, no statistical process in the world will be able to extract it. However, there are potential downsides to ITL as well, in particular for humans. Humans would have to engage with an autonomous machine over possibly extended periods of time in a way that would lead to successful knowledge acquisition for the machine. Some effects would be limited to the teaching interaction itself, whereas others could extend far beyond the teaching context. In the following, we examine three classes of ethical aspects as they arise in ITL: 1. What is being taught and what are the associated risks? 2. What are the dynamics of human-machine instruction? 3. What effects will ITL have on human instructors and society?
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