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This paper considers the cultivation of ethical identities among future engineers and computer scientists, particularly those whose professional practice will extensively intersect with emerging technologies enabled by artificial intelligence (AI). Many current engineering and computer science students will go on to participate in the development and refinement of AI, machine learning, robotics, and related technologies, thereby helping to shape the future directions of these applications. Researchers have demonstrated the actual and potential deleterious effects that these technologies can have on individuals and communities. Together, these trends present a timely opportunity to steer AI and robotic design in directions that confront, or at least do not extend, patterns of discrimination, marginalization, and exclusion. Examining ethics interventions in AI and robotics education may yield insights into challenges and opportunities for cultivating ethical engineers. We present our ongoing research on engineering ethics education, examine how our work is situated with respect to current AI and robotics applications, and discuss a curricular module in “Robot Ethics” that was designed to achieve interdisciplinary learning objectives. Finally, we offer recommendations for more effective engineering ethics education, with a specific focus on emerging technologies.
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Data-Related Ethics Issues in Technologies for Informal Professional Learning
Abstract Professional and lifelong learning are a necessity for workers. This is true both for re-skilling from disappearing jobs, as well as for staying current within a professional domain. AI-enabled scaffolding and just-in-time and situated learning in the workplace offer a new frontier for future impact of AIED. The hallmark of this community’s work has been i) data-driven design of learning technology and ii) machine-learning enabled personalized interventions. In both cases, data are the foundation of AIED research and data-related ethics are thus central to AIED research. In this paper we formulate a vision how AIED research could address data-related ethics issues in informal and situated professional learning. The foundation of our vision is a secondary analysis of five research cases that offer insights related to data-driven adaptive technologies for informal professional learning. We describe the encountered data-related ethics issues. In our interpretation, we have developed three themes: Firstly, in informal and situated professional learning, relevant data about professional learning – to be used as a basis for learning analytics and reflection or as a basis for adaptive systems - is not only about learners. Instead, due to the situatedness of learning, relevant data is also about others (colleagues, customers, clients) and other objects from the learner’s context. Such data may be private, proprietary, or both. Secondly, manual tracking comes with high learner control over data. Thirdly, learning is not necessarily a shared goal in informal professional learning settings. From an ethics perspective, this is particularly problematic as much data that would be relevant for use within learning technologies hasn’t been collected for the purposes of learning. These three themes translate into challenges for AIED research that need to be addressed in order to successfully investigate and develop AIED technology for informal and situated professional learning. As an outlook of this paper, we connect these challenges to ongoing research directions within AIED – natural language processing, socio-technical design, and scenario-based data collection - that might be leveraged and aimed towards addressing data-related ethics challenges.
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- PAR ID:
- 10295162
- Date Published:
- Journal Name:
- International Journal of Artificial Intelligence in Education
- ISSN:
- 1560-4292
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s40593-021-00259-x
