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MM conceived the original research idea. EC and MW led the study. EC developed the first draft of the protocol. EC and MW oversaw the development and revision of the protocol.
All authors reviewed each draft and contributed to the revisions. All authors reviewed and approved the final draft. Correspondence to Emma Coles. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions. Coles, E. The influence of contextual factors on healthcare quality improvement initiatives: what works, for whom and in what setting?
Protocol for a realist review. Syst Rev 6, Download citation. Received : 18 July Accepted : 15 August On the other hand, where the nature of the data is social, or subjective, individuals are more likely to have concerns about what data was taken into account for the decision, and the suitability or fairness of this in influencing an AI-assisted decision about them. In these circumstances, the data and fairness explanations will help address these concerns by telling people what the input data was, where it was from, and what measures you put in place to ensure that using this data to make AI-assisted decisions does not result in bias or discrimination.
What people want to know about a decision can change depending on how little or much time they have to reflect on it. The urgency factor recommends that you give thought to how urgent the AI-assisted decision is. Think about whether or not a particular course of action is often necessary after the kind of decisions you make, and how quickly you need to take that action.
Where urgency is a key factor, it is more likely that individuals will want to know what the consequences are for them, and to be reassured that the AI model helping to make the decision is safe and reliable. Therefore, the impact and safety and performance explanations are suitable in these cases. This is because these explanations will help individuals to understand how the decision affects them, what happens next, and what measures and testing were implemented to maximise and monitor the safety and performance of the AI model.
The groups of people you make decisions about, and the individuals within those groups have an effect on what type of explanations are meaningful or useful for them. What level of expertise eg about AI do they have about what the decision is about?
Are a broad range of people subject to decisions you make eg the UK general public , which indicates that there might also be a broad range of knowledge or expertise? Or are the people you make decisions about limited to a smaller subset eg your employees , suggesting they may be more informed on the things you are making decisions about?
Also consider whether the decision recipients require any reasonable adjustments in how they receive the explanation Equality Act As a general rule, it is a good idea to accommodate the explanation needs of the most vulnerable individuals.
You should ensure that these decision recipients are able to clearly understand the information that you are giving them. Using plain, non-technical language and visualisation tools, where possible, may often help.
Note as well that, while we are focusing on the decision recipient, you are also likely to have to put significant forethought into how you will provide other audiences with appropriate information about the outputs of your AI model.
Likewise, in instances where models and their results are being reviewed by auditors, you will have to provide information about these systems at a level and depth that is fit for the purpose of the relevant review. If the people you are making AI-assisted decisions about are likely to have some domain expertise, you might consider using the rationale explanation. This is because you can be more confident that they can understand the reasoning and logic of an AI model, or a particular decision, as they are more familiar with the topic of the decisions.
Additionally, if people subject to your AI-assisted decisions have some technical expertise, or are likely to be interested in the technical detail underpinning the decision, the safety and performance explanation will help. This is so that people can be reassured about the safety of the system, and know who to contact to ask about an AI decision.
Of course, even for those with little knowledge of an area, the rationale explanation can still be useful to explain the reasons why a decision was made in plain and simple terms.
The expert can then review and come to their own informed conclusions about the validity or suitability of the reasons for the decision eg a doctor in a healthcare setting. OSO version 0. University Press Scholarship Online. Sign in. Not registered? Sign up. Publications Pages Publications Pages. Recently viewed 0 Save Search. Users without a subscription are not able to see the full content. Find in Worldcat. Go to page:. Your current browser may not support copying via this button.
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