# Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies
## 1 Introduction  
Although confusion over informed consent is not specific to a public health emergency, the COVID-19 pandemic has brought into focus issues with consent across multiple areas often affecting different stakeholders. Consent, or Terms of Use for technology artefacts including online services, is intended to record the voluntary willingness to engage. Further, it is assumed to be informed: that individuals understand what is being asked of them or that they have read and understood the Terms of Use. It is often unclear, however, what this entails. For the user, how voluntary is such consent, and for providers, how much of their technology can they represent to their users? As an example from health and social care, contact tracing--a method to track transmission and help combat COVID-19--illustrates some of the confusion. Regardless of the socio-political implications of non-use, signing up for the app would imply a contract between the user and the service provider based on appropriate use of the app and limiting the liability of the provider. However, since it would typically involve the processing of personal data, there may also be a request for the user (now a data subject) to agree to that processing. In the latter case, consent is one possible legal basis under data protection law for the collection and exploitation of personal data. In addition, though, the service provider may collaborate with researchers and wish to share app usage and user data with them. This too is referred to as (research) consent, that is the willingness to take part in research. Finally, in response to an indicationthat the app user has been close to someone carrying the virus, they may be invited for a test; they would need to provide (clinical) consent for the clinical intervention, namely undergoing the test. It is unclear whether individuals are aware of these different, though common, meanings of consent, or of the implications of each. Added to that, there may be a societal imperative for processing data about individual citizens, which implies that there is a balance to be struck between individual and community rights. Such examples emerge in other domains as well. Big Tech companies, for instance, may request user consent to process their personal data under data protection law. They may intend to share that data with third parties, however, to target advertising which involves some degree of profiling, which is only permitted under European data protection regulation in specific circumstances. Although legally responsible for the appropriate treatment of their users' data, the service provider may not understand enough of the technology to meet their obligations. Irrespective of technology, the user too may struggle to identify which purpose or purposes they are providing consent for. With social media platforms, the platform provider must similarly request data protection consent to store and process their users' personal data. They may also offer researchers access to the content generated on their platform or to digital behaviour traces for research purposes. This would come under research consent rather than specifically data protection consent. In these two cases, first the user must identify different purposes under the same consent that their (service) data may be used for, but secondly they may need to review different types of consent regarding their data as used for providing the service versus content they generate or activities they engage in used for research. In this paper, I will explore the confusions around consent in terms of common social scientific models. This provides a specifically behavioural conception of the dialogue associated with consent contextualised within an ecologically valid presentation of the underlying mechanisms. As such, it complements and extends the discussion on explainable artificial intelligence (AI).