“Putting the Control back with the Humans”

• Whether the users can check correctness of understanding of their actions (creativity phases; engagement with video watching for learning; personality traits; the record held about them);
• Ability to choose what data to provide, and for how long;
• Users can change the pace of the interaction with the system by changing their behavior;
• To give the user the choice of reverting the decision made by the adaptation;
• User for car personal assistant can still drive/ passengers and their presence;
• Can choose to switch off certain adaptation;
• Can decline to give mandatory data by explaining why

• Providing a playback of users’ interactions;
• The relationship between the interaction and the type of content;
• Need an environment to collect constant feedback from user in the interactions (in the context of time and space);
• Need a meta understanding of interactions (open model, glass box model);
• The current technology is quite limited;
• To identify what game applies in this maintenance of equilibrium states of interaction

• Should be able to reveal the model underneath and give the power to users for influencing the model and decisions taken;
• The decision behind the pedagogical interventions – providing the answer to why;
• Better movement capture while interacting with the system and how it relates to the elements of the games;
• More simulation studies (e.g. with health and safety in mind);
• Need progressive enhancement for missing data (e.g. not provide income, then check if that can be deduced from other sources);
• How to enable the users to trust the system by explaining the adaptation strategies (and also address the privacy issues);
• To devise a simulation environment to study these challenges of ESI with a set of small but multidimensional data

Interactive session in working groups

• Research vs. general purpose ethics (as researchers we are, and have been for a while, covered by ethical approval issued by institutional ethics boards, which already cover most aspects of GDPR)
• Issues with data from modalities like audio/video – how do we anonymise these?
• Variety of data that falls under personally identifiable information (PII) and other ‘sensitive’ information
• Misinformation using PII (e.g. targeting specific demographics during elections)
• Under GDPR you can’t have algorithms making important decisions regarding people (e.g. should someone be approved for mortgage)
  • explainable AI (XAI) could help make this process both scalable, but still fair, transparent, and accountable
• There is still a lot of uncertainty around the enforcement of GDPR; as such we are not sure how strictly the rules will be enforced, and what will this mean for UMAP research
• Functional algorithms (~ functional cookies, i.e. the cookies which are necessary for the functioning of the website; if the user doesn’t consent to using functional cookies, then they can’t use the website), which obtain informed consent (but is it actually informed?)
• Progressive enhancement (users can opt out of giving certain data, but the algorithms and system would still need to provide fair access and usage even with such missing data) – is this workable?

• Interacting with the same entity (e.g., service) across various realities (platforms and contexts)
  • Recommendations (+ data processing, handling) coming from central point of access
  • Intelligent digital assistance
  • o Models at higher levels of abstraction capturing the holistic user actions
• Agent systems providing recommendations (more obvious control on the agent, e.g., automate the process and actions and then approve or not, e.g., automotive area)
• Degree of control applicability highly dependent on the domain of activity
• User models that consider users’ learning curve for recommending
• How recommendations may differ in regard to more “physical” interactions e.g., augmented reality