论文标题

Exosoul:数字世界中的道德分析

Exosoul: ethical profiling in the digital world

论文作者

Alfieri, Costanza, Inverardi, Paola, Migliarini, Patrizio, Palmiero, Massimiliano

论文摘要

越来越多的自主数字技术在我们的社会中的发展和传播在数据保护和侵犯隐私之外提出了新的道德挑战。用户在与数字技术的互动中不受保护,同时自主系统可以自由地占据每个人具有特权的决策空间。在这种情况下,多学科项目Exosoul旨在开发一个个性化的软件外骨骼,该软件根据用户的道德偏好来介导数字世界中的动作。外骨骼依赖于用户的道德分析,目的类似于文献中提出的隐私分析,但旨在反映和预测一般的道德偏好。我们的方法是混合的,首先是基于自上而下的方式识别配置文件,然后基于通过个性化数据驱动的方法对配置文件进行的完善。在这项工作中,我们报告了有关构建此类自上而下概况的最初实验。我们考虑伦理立场(唯心主义和相对主义)人格特征(诚实/谦卑,尽我所能,马基雅维利主义和自然主义)与世界观(规范主义)之间的相关性,然后我们使用聚类的方法来创建对用户的数字行为对用户的数字行为的预测性侵犯,侵犯副本侵权,侵权,侵权。通过对317名年轻人的调查表进行调查表来收集数据。在本文中,我们讨论了两种集群解决方案,一种数据驱动和一个模型驱动的,就数字行为的有效性和预测能力而言。

The development and the spread of increasingly autonomous digital technologies in our society pose new ethical challenges beyond data protection and privacy violation. Users are unprotected in their interactions with digital technologies and at the same time autonomous systems are free to occupy the space of decisions that is prerogative of each human being. In this context the multidisciplinary project Exosoul aims at developing a personalized software exoskeleton which mediates actions in the digital world according to the moral preferences of the user. The exoskeleton relies on the ethical profiling of a user, similar in purpose to the privacy profiling proposed in the literature, but aiming at reflecting and predicting general moral preferences. Our approach is hybrid, first based on the identification of profiles in a top-down manner, and then on the refinement of profiles by a personalized data-driven approach. In this work we report our initial experiment on building such top-down profiles. We consider the correlations between ethics positions (idealism and relativism) personality traits (honesty/humility, conscientiousness, Machiavellianism and narcissism) and worldview (normativism), and then we use a clustering approach to create ethical profiles predictive of user's digital behaviors concerning privacy violation, copy-right infringements, caution and protection. Data were collected by administering a questionnaire to 317 young individuals. In the paper we discuss two clustering solutions, one data-driven and one model-driven, in terms of validity and predictive power of digital behavior.

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