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Interactive robots are becoming more commonplace and complex, but their identity has not yet been a key point of investigation. Identity is an overarching concept that combines traits like personality or a backstory (among other aspects) that people readily attribute to a robot to individuate it as a unique entity. Given people's tendency to anthropomorphize social robots, "who is a robot?" should be a guiding question above and beyond "what is a robot?" Hence, we open up a discussion on artificial identity through this workshop in a multi-disciplinary manner; we welcome perspectives on challenges and opportunities from fields of ethics, design, and engineering. For instance, dynamic embodiment, e.g., an agent that dynamically moves across one's smartwatch, smart speaker, and laptop, is a technical and theoretical problem, with ethical ramifications. Another consideration is whether multiple bodies may warrant multiple identities instead of an "all-in-one" identity. Who "lives" in which devices or bodies? Should their identity travel across different forms, and how can that be achieved in an ethically mindful manner? We bring together philosophical, ethical, technical, and designerly perspectives on exploring artificial identity. 

Fitness trackers are undoubtedly gaining in popularity. As fitness-related data are persistently captured, stored, and processed by these devices, the need to ensure users’ privacy is becoming increasingly urgent. In this paper, we apply a data-driven approach to the development of privacy-setting recommendations for fitness devices. We first present a fitness data privacy model that we defined to represent users’ privacy preferences in a way that is unambiguous, compliant with the European Union’s General Data Protection Regulation (GDPR), and able to represent both the user and the third party preferences. Our crowdsourced dataset is collected using current scenarios in the fitness domain and used to identify privacy profiles by applying machine learning techniques. We then examine different personal tracking data and user traits which can potentially drive the recommendation of privacy profiles to the users. Finally, a set of privacy-setting recommendation strategies with different guidance styles are designed based on the resulting profiles. Interestingly, our results show several semantic relationships among users’ traits, characteristics, and attitudes that are useful in providing privacy recommendations. Even though several works exist on privacy preference modeling, this paper makes a contribution in modeling privacy preferences for data sharing and processing in the IoT and fitness domain, with specific attention to GDPR compliance. Moreover, the identification of well-identified clusters of preferences and predictors of such clusters is a relevant contribution for user profiling and for the design of interactive recommendation strategies that aim to balance users’ control over their privacy permissions and the simplicity of setting these permissions.