Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies
Overview of the Discussion
2. Responsible and Explainable AI
"… systems that can explain their rationale to a human user, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future"
3. Behaviour and Causal Models
4. Informed Consent
“Voluntary agreement to or acquiescence in what another proposes or desires; compliance, concurrence, permission.”(https://www.oed.com/oed2/00047775) (accessed on 12 May 2021)
“[The] circumstances under which buyers of software or visitors to a public Web site can make use of that software or site”.
“[the] process of informed consent occurs when communication between a patient and physician results in the patient’s authorization or agreement to undergo a specific medical intervention”.(https://www.ama-assn.org/delivering-care/ethics/informed-consent) (accessed on 12 May 2021)
“Respect for persons requires that subjects, to the degree that they are capable, be given the opportunity to choose what shall or shall not happen to them. This opportunity is provided when adequate standards for informed consent are satisfied.”(Part C (1), )
“any freely given, specific, informed and unambiguous indication of the data subject’s wishes by which he or she, by a statement or by a clear affirmative action, signifies agreement to the processing of personal data relating to him or her”.(Art. 4(11), )
4.1. Applied Research Ethics
- Participant Autonomy or Respect for the Individual: a guarantee to protect the dignity of the participant as well as respecting their willingness or otherwise to take part. This assumes that the researcher (including those involved with clinical research) have some expectation of outcomes and can articulate them to the potential participant. Deception is possible under appropriate circumstances , including the use of placebos. Big data requires careful thought, since it shifts the emphasis away from individual perspectives . In so doing, a more societally focused view may be more appropriate . Originally, autonomy related directly to the informed consent process. However, the potential for big data and AI-enabled approaches suggests that this may need rethinking.
- Beneficence and non-malevolence: ensuring that the research will treat the participant well and avoid harm. This principle, most obvious for medical ethics, puts the onus on the researcher (or data scientist) to understand and control outcomes (see also [15,17]). Although there is no suggestion that the researcher would deliberately wish to cause harm, unsupervised learning may impact expected outcomes. Calls for transparency and the human-in-the-loop to intervene if necessary [8,17] imply a recognition that predictions may not be fixed in advance. Once again, the informed nature of consent might be difficult to satisfy.
- Justice: to ensure the fair distribution of benefits. The final principle highlights a number of issues. The CARE Principles , originally conceived in regard to indigenous populations, stress the importance of ensuring that all stakeholders within research at least are treated equitably. For all its limitations, the trolley car dilemma  calls into question the assessment of justice. During a public health emergency, and inherent in contact tracing, the issue is whether justice is better served by protecting the rights of the individual especially privacy over a societal imperative.
4.2. Issues with Consent
4.3. Technology Acceptance
“… the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party.”Ref. 
- Contact tracing: During the COVID-19 pandemic, there has been some discussion about the technical implementation  and how tracing fits within a larger socio-technical context . Introduction of such applications is not without controversy in socio-political terms [76,102]. At the same time, there is a balance to be struck between individual rights and the public good ; in the case of the COVID-19 pandemic, the social implications of the disease are almost as important as its impact on public and individual health . Major challenges include:
- Public Opinion;
- Inadvertent disclosure of third party data;
- Public/Individual responses to alerts.
- Big Data Analytics: this includes exploiting the vast amounts of data available typically via the Internet to attempt to understand behavioural and other patterns [105,106]. Such approaches have already shown much promise in healthcare , and with varying degrees of success for tracing the COVID-19 pandemic . There are, however, some concerns about the impact of big data on individuals and society [109,110]. Major challenges include:
- Identification of key actors;
- Mutual understanding between those actors;
- Influence of those actors on processing (and results).
- Public Health Emergency Research: multidisciplinary efforts to understand, inform and ultimately control the transmission and proliferation of disease (see for instance ) as well as social impacts [99,104], and to consider the long-term implications of the COVID-19 pandemic and other PHEs . Major challenges include:
- Changes in research focus;
- Changes introduced as research outcomes become available;
- Respect for all potential groups;
- Balancing individual and community rights;
- Unpredicted benefits of research data and outcomes (e.g., in future).
6. Discussion and Recommendations
6.1. Recommendations for Research Ethics Review
- The research proposal should first describe in some detail the trustworthiness basis for the research engagement. I have used characteristics from the literature—integrity, benevolence, and competence—though others may be more appropriate such as reputation and evidence of participant reactions in related work.
- The context of the proposed research should be disclosed, including the identification of the types of contextual effects which might be expected. These may include the general socio-political environment, existing relationships that the research participant might be expected to be aware of (such as clinician–patient), and any dynamic effects, such as implications for other cohorts, including future cohorts. Any such contextual factors should be explained, justified and appropriately managed by the researcher.
- The proposed dialogue between researcher and research participant should be described, how it will be conducted, what it will cover, and how frequently the dialogue will be repeated. This may depend, for example, on when results start to become available. The frequency and delivery channel of this dialogue should be simple for the potential research participant. This must be justified, and the timescales realistic. This part of the trust-based consent process might also include how the researcher will manage research participant withdrawal.
6.2. Recommendations for the Ethical Use of Advanced Technologies
- Understand who the main actors are. Each domain (healthcare, eCommerce, social media, and so forth) will often be regulated with specific obligations. More importantly though, I maintain, would be the interaction between end user and provider, and the reliance of the provider on the data scientist or technologist. These actors would all influence the trust context. So how they contribute needs to be understood.
- Understand what their expectations are. Once the main actors have been identified, their individual expectations will influence how they view their own responsibilities and how they believe the other actors will behave. This will contextualise what each expects from the service or interaction, and from one another.
- Reinforce competence, integrity and benevolence (from ). As the defining characteristics of a trust relationship outlined above, each of the actors has a responsibility to support that relationship, and to avoid actions which would affect trust. Inadvertent or unavoidable problems can be dealt with ([88,89]). Further, occasional (though infrequent ) re-affirmation of the relationship is advantageous. So, ongoing communication between the main actors is important in maintaining trust (see also ).
7. Future Research Directions
Data Availability Statement
Conflicts of Interest
|Domain||Challenges||Informed Consent||Trust-Based Consent|
|Contact Tracing||The socio-political context within which the app is used or research is carried out. Media reporting, including fake news, can influence public confidence||One-off consent on research engagement or upon app download may not be sufficient as context changes. Retention may be challenging depending on trustworthiness perceptions of public authorities and responses to media reports leading to app/research study abandonment (i.e., the impact and relevance of context which may have nothing to do with the actual app/research)||Researchers (app developers) may need to demonstrate integrity and benevolence on an ongoing basis, and specifically when needed in response to any public concerns around data protection, and to any misuse or unforeseen additional use of data. Researchers must therefore communicate their own trustworthiness and position themselves appropriately within a wider socio-political context for which they may feel they have no responsibility. It is their responsibility, however, to maintain the relationship with relevant stakeholders, i.e., to develop and maintain trust.|
|Big Data Analytics||The potential disruption to an existing ecosystem—e.g., the actors who are important for delivery of service, such as patient and clinician for healthcare, or research participant and researcher for Internet-based research. Technology may therefore be disruptive to any such existing relationship. Further, unless the main actors are identified, it would be difficult to engage with traditional approaches to consent.||Researcher (data scientist) may not be able to disclose all information necessary to make a fully informed decision, not least because they may only be able to describe expected outcomes (and how data will be used) in general terms. The implications of supervised and unsupervised learning may not be understood. Not all beneficiaries can engage with an informed consent process (e.g., clinicians would not be asked to consent formally to data analytics carried out on their behalf; for Internet-based research, it may be impractical or ill-advised for researchers to contact potential research participants).||Data scientists need to engage in the first instance with domain experts in other fields who will use their results (e.g., clinicians in healthcare; web scientists etc. for Internet-based modelling; etc.) to understand each other’s expectations and any limitations. For a clinician or other researcher dependent on the data scientist, this will affect the perception of their own competence. This will also form part of trust-based engagement with a potential research participant. Ongoing communication between participants, data scientists and the other relevant domain experts should continue to maintain perceptions of benevolence and integrity.|
|Public Health Emergency||The difficulty in identifying the scope of research (in terms of what is required and who will benefit now, and especially in the future) and therefore identify the main stakeholders, not just participants providing (clinical) data directly||The COVID-19 pandemic has demonstrated that research understanding changed significantly over time: the research community, including clinicians, had to adapt. Policy decisions struggled to keep pace with the results. Informed consent would need constant review and may be undermined if research outcomes/policy decisions are not consistent. In the latter case, this may result in withdrawal of research participants. Further, research from previous pandemics was not available to inform current research activities||A PHE highlights the need to balance individual rights and the imperatives for the community (the common good). As well as the effects of fake news, changes in policy based on research outcomes may lead to concern about competence: do the researchers know what they are doing? However, there needs to be an understanding of how the research is being conducted and why things do change. So, there will also be a need for ongoing communication around integrity and benevolence. This may advantageously extend existing public engagement practices, but would also need to consider future generations and who might represent their interests. There is a clear need for an ongoing dialogue including participants where possible, but also other groups with a vested interest in the research data and any associated outcomes, including those who may have nothing to do with the original data collection or circumstances.|
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Pickering, B. Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies. Future Internet 2021, 13, 132. https://doi.org/10.3390/fi13050132
Pickering B. Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies. Future Internet. 2021; 13(5):132. https://doi.org/10.3390/fi13050132Chicago/Turabian Style
Pickering, Brian. 2021. "Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies" Future Internet 13, no. 5: 132. https://doi.org/10.3390/fi13050132