# Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies
## 4 Informed Consent
### Technology Acceptance  
How users decide to engage with technology, especially for services like contact tracing, needs to consider issues of consent. This is not always the case (see [23]). Traditionally, causal models predicting technology adoption have focused particularly on features of the technology itself, such as perceived ease of use and perceived usefulness [67]. The _Context_ (see Section 3) derives solely from the technology. Other variables associated with technology uptake such as the demographics of potential adopters, any facilitating context and social influence have been identified as moderators, however [68]. [PERSON], [PERSON] and their colleagues combine these technology-based approaches with models of trust, however [69; 70; 71]. In so doing, they extend the influence of social norms identified by [PERSON] [68] to include a trust relationship with relevant human agents in the overall context within which a particular technology is used.  
For the COVID-19 Public Health Emergency (PHE), contact tracing was seen as an effective tool in managing the pandemic. Early on in the pandemic, it was identified as one method among many which would be of use, given the balance between individual rights and societal benefit [72]. Concerns around privacy simply need careful management [73]. Later empirical studies have demonstrated a willingness to engage with the technology: perceived usefulness in managing personal risk outweighs both effort and privacy concerns [74; 75]. Perceived ease of use as predicted by standard causal models for technology acceptance [67], therefore, was not seen as an issue.  
By contrast the overall context for the introduction of contact tracing needs to be considered. The perceived failure in France, for instance, was due in part to a lack of trust in the government and how it was encouraging uptake [76]. Indeed, [PERSON] and his colleagues attribute this failure to a lack of cross-disciplinary planning and knowledge sharing, with attempts to force public acceptance of contact tracing perceived as coercive and therefore likely to lead to public distrust [76]. Introducing trust here is entirely consistent with [PERSON] and [PERSON]'s work (see above, refs. [69; 70; 71]). Without trust in the main stakeholders, as [PERSON] et al. found, adoption will be compromised.  
Examining trust in the context of contact tracing usage brings the discussion back to consent. Given the shortcomings of consent across multiple contexts including terms of use, [PERSON] and [PERSON] [23] argue for an approach which does not ignore basic ethical principles such as autonomy but rather empowers stakeholders to engage appropriately. They conclude that existing consent approaches should be replaced with a legally-based trust process. This would allow, they claim, obligations to protect and be discrete with participants and their data and to avoid manipulative practices. The present discussion takes this idea one stage further. In the next section, I review a common social psychological definition of trust as a continuous socially constructed agreement between parties. After that, this is applied to specific technology scenarios relevant to contact tracing, AI-enabled technologies and PHE.