This is the fourth blog, in a series of five, discussing the results of the Ethical AI workshop at MPNE Consensus Data 2024.
The participants from the wider MPNE community, drawn from across Europe, provided a wealth of subject matter expertise on topics such as governance, accountability, privacy, fairness, trust and transparency. The aim of the workshop was to identify key themes to incorporate into WP2 deliverables focused on assessing the legal, ethics, and societal impact of the iToBoS technologies.
The participants were divided into 4 groups to discuss different cartoons presented to them, each depicting a topic or theme related to emerging technology, health, and ethics. The focus of this blog is to examine the perspectives from participants assigned to Group 3, who were presented the cartoon below. 
Using Slido, the following 39 responses from 4 participants where collated when the participants freely, openly and honestly discussed the cartoon.
“Quality”, “Access”, “efficiency” and “current info” all describe the importance of having high quality data, whether that be relevant to how recent it is, or how well it represents what it is supposed to represent. For iToBoS, transparency is a key theme. Being transparent regarding how accurate the algorithms are and how this accuracy is achieved is important. Language such as “no standards”, “no guidelines” implies the participants feel results can be achieved in the broad health domain, but maybe not always to the highest quality. This perception highlights a lack of trust in the development of technology, or algorithms. There is a sense, perhaps, that participants feel organisations trade-off between accuracy and “Safety”, in exchange for “Lower costs” or “sales”.
Similarly, the discussion might also imply that participants feel that organisations take an “opportunity” to trade-off between “reliable”, “stable employee” and machines, (“Unemployment”) “without engagement“, for “low cost” saving measures.
However, “easier communication” suggests it might be easier and “faster” to work with IT systems instead of people in certain environments, including health. This might be true when waiting to receive or interpret results. This is possibly why language such as “priorities” and “Rules” were included as key themes, to ensure the system’s safety and accuracy is not compromised.
The results from this discussion implies concepts such as efficiency and accuracy are extremely important when implementing an AI system such as iToBoS. However, it also suggests that participants might feel organisations trade-off between safety and human employment, for lower costs and higher profit. If we are to consider this in respect to the future development of iToBoS, the project will need to ensure that transparency surrounding the efficiency and accuracy of the system is available to the wider public to instil trust, and no such trade-offs are involved when deploying the system into real life health service provision.
More about Ethical AI workshop at MPNE Consensus Data 2024 here.