# Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies
## 2 Responsible and Explainable AI
With specific regard to big data, the Toronto Declaration puts the onus on data scientists and to some degree governance structures to protect individual rights [13]. However, human rights conventions often balance individual rights with what is right for the community. For example, although the first paragraph of Art. 8 on privacy upholds individual rights and expectations, the second provides for exceptions where required by the community [14]. Individual versus community rights are significant for contact tracing and similar initiatives associated with the pandemic. While calling upon technologists for transparency and fairness in their use of data, the UK Government Digital Services guidance also tries to balance community needs with individual human rights [15]. The UK Department of Health and Social Care introduces the idea that both clinicians and patients, that is multiple stakeholders, need to be involved and to understand the technology [16]. Similarly, the EU stresses that developers, organisations deploying a given technology, and end-users should all share some responsibility in specifying and managing AI-enabled technologies, without considering how such technologies might disrupt existing relationships [17]. The focus on transparency and explainability within the (explainable) AI literature is relevant to the idea that consent should be informed. Although the focus is often on technologies [5; 8], this assumes that all stakeholders--those affected by the output of the AI component, those using it for decision support, and those developing it (cf. [5])--share responsibility for the consent process. Even where studies have focused on stakeholder interactions and the co-construction of explainability [12], there is an evident need to consider the practicalities of the negotiation between parties to that process. For contact tracing, for example, who is responsible to the app user for the use and perhaps sharing of their data? Initially, the service provider would assume this role and make available appropriate terms of use, a privacy notice and privacy policy. However, surely the data scientist providing algorithms or models for such a system at least needs to explain the technology? A Service Level Agreement (SLA) for a machine-learning component would not typically involve detail about how a model was generated or its longer term performance. If it is not clear what stakeholders are responsible for, it becomes problematic to identify who should be informing the app user or participant of what to expect. Further, with advanced, AI-enabled technologies, not all stakeholders may be able to explain the technology. A clinician, for instance, is focused on care for their patients; they would not necessarily know how a machine-learning model had been generated or what the implications would be. There would have to be a paradigm shift perhaps before they consider trying to understand AI-technologies. Leading on from studies which situate explainable AI within a behavioural context ([6; 11; 12]), I take the more general discussion about the use and effects of advanced technologies into the context of planned behaviour (in Section 3) and extend discussions of trust [9; 11] into the practical consideration of informed consent in a number of different domains. Starting with contact tracing and similar applications of AI technologies (Section 5), this discussion seeks to explore the confusion around consent in a practical context, evaluate the feasibility of transparency, and review responsible stakeholders for different scenarios. Consent to engage with advanced technologies highlights, therefore, the impact of AI rather than specifically on how explainable the technology might be. Focusing on a new kind of ethics, this leads to the proposal for a trust-based alternative to consent.