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Collaborative human-AI (HAI) teaming combines the unique skills and capabilities of humans and machines in sustained teaming interactions leveraging the strengths of each. In tasks involving regular exposure to novelty and uncertainty, collaboration between adaptive, creative humans and powerful, precise artificial intelligence (AI) promises new solutions and efficiencies. User trust is essential to creating and maintaining these collaborative relationships. Established models of trust in traditional forms of AI typically recognize the contribution of three primary categories of trust antecedents: characteristics of the human user, characteristics of the technology, and environmental factors. The emergence of HAI teams, however, requires an understanding of human trust that accounts for the specificity of task contexts and goals, integrates processes of interaction, and captures how trust evolves in a teaming environment over time. Drawing on both the psychological and computer science literature, the process framework of trust in collaborative HAI teams (CHAI-T) presented in this paper adopts the tripartite structure of antecedents established by earlier models, while incorporating team processes and performance phases to capture the dynamism inherent to trust in teaming contexts. These features enable active management of trust in collaborative AI systems, with practical implications for the design and deployment of collaborative HAI teams., Comment: 36 pages, 2 figures

As consensus across the various published AI ethics principles is approached, a gap remains between high-level principles and practical techniques that can be readily adopted to design and develop responsible AI systems. We examine the practices and experiences of researchers and engineers from Australia's national scientific research agency (CSIRO), who are involved in designing and developing AI systems for many application areas. Semi-structured interviews were used to examine how the practices of the participants relate to and align with a set of high-level AI ethics principles proposed by the Australian Government. The principles comprise: (1) privacy protection and security, (2) reliability and safety, (3) transparency and explainability, (4) fairness, (5) contestability, (6) accountability, (7) human-centred values, (8) human, social and environmental wellbeing. Discussions on the gained insights from the interviews include various tensions and trade-offs between the principles, and provide suggestions for implementing each high-level principle. We also present suggestions aiming to enhance associated support mechanisms., Comment: submitted to IEEE Transactions on Technology & Society

This study examines the nature of the impact of vocational education and training (VET), and its project-based activities and partnerships, on the development of sustainable communities in regional Australia. It finds that VET plays a critical role as the entry point to learning and builds considerable social and other forms of capital in regional communities. The study points to new opportunities to integrate these assets into strategic regional development. Specific key findings include: (1) Vocational education and training, VET partnerships and project-based training have created and mobilized social capital, as well as human, environmental, cultural and built (that is, physical) capital in regional Australia--These various types of capital are core requirements for sustainable regional development; (2) Vocational education and training and VET partnerships play a critical role in creating individual and community confidence and are often the key entry points to learning and employment; (3) There is a need for a more holistic approach to training, giving consideration to the continuing learning pathway along which an individual may travel, as opposed to focusing on discrete packages and modules--This is especially apparent in Indigenous communities where training can impose community obligations; (4) There are excellent examples of VET leadership in regional development but, in general, vocational education and training and VET partnerships are not full participants in regional development plans and strategies--The changing regional landscapes will benefit from more input from the VET sector; and (5) VET has responded well to a growing consolidation of larger regional service centres, where the "higher order" skills associated with new technologies and electronics are often required--Less well resolved is the role of VET in smaller centres. There is a need for effective regional delivery models which link these service centers. Appended are: (1) Glossary of terms; and (2) CRRI-Q interview prompts and questions. (Contains 1 table.) [This report was published with funding provided through the Australian Department of Education, Science and Training. The author/project team was funded to undertake this research via a grant under the National Vocational Education and Training Research and Evaluation (NVETRE) Program. For "Building Learning Communities: Partnerships, Social Capital and VET Performance. Support Document," see ED493936.]