What is Trustworthy and Ethical Assurance?

Assurance is about building trust.

Consider the following scenario. You are in the market for a new car and go to a local dealership. One of the sales advisors convinces you to buy a second hand car that later turns out to have an issue with the engine. Frustrated, you take the car back and the sales advisor apologises. They explain that all their second hand cars undergo a thorough assessment before they are placed on the market but, nevertheless, go on to process a return and get you a different car. You are reassured and happy, but only for a short period of time. Yet again, the car turns out to have a problem with the engine—the same problem as before! The sales advisor tries to convince you that this is just a series of unlucky incidents, but without clear evidence to support their claims, this time around you do not trust them and take your business elsewhere.

Assurance involves communicating reasons and evidence that help people understand and evaluate the trustworthiness of a claim (or series of claims) about a system or technology.

In the above example, the sales advisor needed to provide assurance that their cars were safe or reliable, but the claims they made about the assessment process were undermined by the contrary evidence (i.e. two unreliable cars).

In a different scenario, things may go differently. For instance, you may have a higher level of trust with friends or other professionals (e.g. doctors, teachers) than with car sales persons. And, as such, you may be more likely to accept claims in the absence of evidence or in spite of repeated instances of contrary evidence. However, the relationship between trust and assurance is significant in nearly all contexts, and especially so in some domains (e.g. safety-critical engineering).

Therefore, having clear methods, processes, and tools for communicating assurance and building trust is crucial. And, this is increasingly important in the design, development, and deployment of data-driven technologies.

Building Trust and Communicating Trustworthiness for Data-Driven Technologies

Trustworthy and Ethical Assurance could apply to many systems, but the TEA platform specifically addresses data-driven technologies, such as artificial intelligence or digital twins.

There are many benefits and risks associated with the design, development, and deployment of data-driven technologies. And, therefore, many organisations and companies find themselves in a situation of needing to communicate to customers, users, or stakeholders how they have maximised the benefits and minimised the risks associated with their product, service, or system.

For example, an organisation building an autonomous vehicle may need to explain how their system is safe, secure, fair, explainable, among other goals. How they achieve this will depend on myriad contextual factors, including who they are communicating with (e.g. regulators, potential customers).

Consider the goal of safety with respect to the following questions:

• How was the performance of the autonomous vehicle evaluated, and how will it be monitored? There are many metrics that can be used to evaluate the performance of an autonomous vehicle, including metrics that assess the performance of components of the vehicle such as the object recognition system (e.g. its accuracy, robustness, interpretability) as well as metrics that consider broader societal or environmental impact (e.g. sustainability, usability and accessibility).
• Who carried out processes such as failure mode and effects analysis or stakeholder engagement? Diverse and inclusive teams can help reduce the likelihood of unintended consequences, especially those that may arise due to the presence of overlooked biases in the system (e.g. how were trade-offs in the design process handled and who was consulted).
• Who will use the system? Whether a system is safe depends, in part, on who the users are (e.g. trained professionals versus members of the public)—a key challenge in the area of human factors research.

These are just three examples of how claims made about the safety of a system, in response to a small set of possible questions, are highly contextual. If we were to consider different goals (e.g. fairness, explainability) or different areas of application (e.g. healthcare, defence and security), the types of claims that would be needed to provide assurance for the goal in question could be very different.

And yet, in spite of the contextual variation, there are similarities that span the assurance of data-driven technologies, both within and between different domains. There are, for instance, a recurring set of goals (or, principles) that people emphasise when asked about the ethical or societal issues related to data-driven technologies (e.g. fairness and bias, transparency and explainability)¹. And, furthermore, there is a growing set of techniques and practices in place for building trust through open, transparent and accessible forms of communication ².

Trustworthy and ethical assurance is a framework that is anchored in these similarities and existing techniques, but also recognises the importance of understanding variation and difference. At the centre of this framework is a methodology and tool for building assurance cases. These assurance cases communicate how some ethical goal has been established within the context of the design, development, or deployment of a data-driven technology. The methodology serves as a guide for developing the cases, while the platform helps to build and communicate them with the wider community or stakeholders.

The remaining parts of this section provide further context to help situate and motivate trustworthy and ethical assurance. However, if you'd prefer to jump straight in, you can jump to our user guidance section instead.

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1. Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1 ↩

2. The Turing Way Community. (2022). The Turing Way: A handbook for reproducible, ethical and collaborative research. Zenodo. doi: 10.5281/zenodo.3233853. ↩