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- How do we study work practices that combine AI and humans?
- What is an effective research design that combines big data (ML, simulations, AI etc) with small data (observations, interviews etc) to study work practices in the digital age?
- When framing RQ, consider levels: individual variables vs. dyadic vs. group (eg. In-group vs. out-group)
- What are the implications for skills and training and workforce development?
- What are the implications for competencies? How can we prepare employees for developing new competencies if we don’t know yet which ones these will be?
- What are the educational foundations that make learning to integrate AI into your work similar to the the foundations that give us platforms and triggers for thinking about whether we do some work as individuals, as team members, outsourced, etc. Acknowledging that we don’t have those examples fully nailed either.
- What are the legal implications of involving virtual characters in work processes?
- Who is accountable for AI errors?
- Ethics - who should be held responsible for a failure in AIA/incorrect diagnosis or recommendation that leads to negative impacts?
- How do we alleviate privacy concerns when data (needed for machine learning algorithms) is collected for worker development.
- Issues of misuse of data and how it affects the algorithmic actions: What are the implications for privacy ?
- How is work on AI being funded and performed, and what are the implications of this for work involving these agents?
- How is the process of designing the agents organized: who decides what system to design, for whom and why, what are the assumptions behind and what else is involved in making these decisions?
- Will AI lead to even more power and concentration among the leading platforms?
- How does new physical infrastructure such as autonomous vehicles affect social activities such as dining out?
- What do we do with all the workers that are being replaced by AI agents?
- What impact will AI have on organizational learning and knowledge management?
- What are the enabling conditions so that organizations in non-high-tech fields (e.g. manufacturing) use systems?
- How do AI systems fit in the organizational chart of a firm? In what respect are they (not) own position?
- Who is writing behavioral modules for these characters, if not the architects of the overall system?
What are the boundaries between them and those building a functional/saleable system?
- What are the work-related implications of this modularization?
For example, what happens if these are/are not open sourced?
And what will modularization mean for the abilities of these systems to operate in contextually-appropriate ways?
- What types of jobs/occupations are likely to be displaced by this technology?
- Is there a difference across different occupations / expertise in terms of impact AI has on their roles? If so, how can we explain this variability?
- Where is deskilling and upskilling like to emerge?
- If machines perform highly skilled jobs, what happens to workers that have spent years learning highly specialized skills?
- If virtual characters do the entry level jobs, how does someone get started in the profession?
- How are work practices redefined/ reshaped?
- What is the process of work transformation?
- What are the differences between the designs that have been performed collaboratively with end users and the ones that have been done without user-involvement? What is the difference between their content, performance and effects?
- What roles in teams could be effectively filled by an agent?
- How will the type of tasks a human agent (employee) performs change with the introduction of an intelligent machine, e.g. a virtual agent?
- How does the number of machine actors a human interacts with impact their trust/performance/acquisition of skills? What if the machine actors interact with each other in an observable fashion (like the 2 museum guides), as opposed to being seen as independent entities. Think: Alexa, Google Home and Siri debating what the best answer to a question is you have posed.
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