Responsible AI means keeping humans in the loop
By , School of Psychology; and , ×îÐÂÌÇÐÄVlogn Institute for Machine Learning (AIML).
This article is an extract from , a report published in partnership with the .
If AI is developed and implemented responsibly, it has the potential to be a positive force for ×îÐÂÌÇÐÄVlogns by reducing the strain caused by inefficiencies in our social systems. For instance, AI systems for health could minimise the millions of dollars lost yearly due to adverse events in the health system by allowing us to invest in better care in different areas. However, this can only be achieved if AI is recognised as being one part of a complex network of working parts, known as a socio-technical system. Although these principles can be generalised to many applications of AI, here we focus on health, which is the field that is currently most influenced by AI and a major focus of the tech industry.
"Responsible AI reflects the values, needs and goals of humans by augmenting human lives and respecting human rights." — Professor Carolyn Semmler.
Socio-technical systems
All technology exists in a socio-technical system. In the context of the health sector, this system is made up of doctors, nurses, other healthcare professionals, the technical infrastructure, and the community they serve. However, a poor understanding of how AI fits into this system, combined with poor implementation, is increasing the potential for harm and error. To illustrate how this looks in the healthcare system, we might consider the example of doctors taking advice from AI when it conflicts with clinical best practice (over-reliance) or doctors choosing not to use AI when it could encourage adherence to clinical best practice (under-reliance). Further, recent research by the ×îÐÂÌÇÐÄVlogn Institute for Machine Learning has demonstrated that bias against minority populations can be transferred through AI systems, which can then negatively impact certain patients.
Why there is no simple fit between AI and a complex socio-technical system like healthcare
Although doctors have specialised skills, they are privy to the same limitations as all humans, of which cognitive science can provide a basis for understanding. This begins by recognising how AI will never be a simple fit into a complex socio-technical system. First, the understanding of the tasks that AI can undertake is misguided. Too often, AI models are built without apprehension of the task that should be solved; and their development lacks input from the people who will use them, in this case, practitioners within the health system. Second, AI models have no ability to use context or meaning to inform their decisions. This is problematic because context critically determines the quality of outcomes for patients.
For example, AI algorithms to detect sepsis have previously missed a large proportion of cases by being unaware of the population characteristics in which they were deployed. However, a doctor working in that same population will be able to draw on their knowledge (grounded in experience) of the different rates of sepsis among different populations to recognise the symptoms needed to accurately diagnose sepsis. Furthermore, the datasets used to train AI are often not kept up to date to reflect the diversity of the population or the diseases they are trying to classify, significantly limiting the technology’s adaptability and shelf life.
"Responsible AI is AI that is developed with the purpose and understanding of the human system it seeks to serve." — Lana Tikhomirov
Designing AI to augment the human in the system
To overcome the problems we have identified, we need to take a radically different approach to the design of AI systems. This can be achieved by understanding how expert human decision-makers like doctors do their work, using the methods and knowledge of cognitive sciences. For example, cognitive scientists have developed a deep understanding of how radiologists can extract the features of a pathological condition from an image within milliseconds of seeing it. This understanding can help to guide when and where AI tools are needed to improve the skills and training of healthcare professionals. Doctors, unlike AI, have a responsibility to their patients and must maintain professional standards of care. Indeed, they are the pinnacle profession that needs to demonstrate responsibility. Therefore, ensuring the appropriate use of AI in their work represents a significant challenge. If AI is implemented poorly, it may add to their burden of responsibility and potentially expose doctors to the risk of poor decision-making. Alternatively, AI implemented with a responsible design informed by cognitive science will allow doctors to offload their cognitive tasks to the AI when appropriate and focus their attention on patients.
This article is an extract from , a report published in partnership with the .
So what does this mean for ×îÐÂÌÇÐÄVlogns?
Responsible AI means giving all ×îÐÂÌÇÐÄVlogns, whose lives will be impacted by AI, information about its intention, data and decision-making processes. Further, responsible AI requires the development of legal frameworks to protect ×îÐÂÌÇÐÄVlogns from the potential harms arising from poorly developed AI and inappropriate deployment in socio-technical systems. Most importantly, ×îÐÂÌÇÐÄVlogns have the right to be informed about the limitations of AI to allow them to decide which aspects of their lives could benefit from it. AI could be a positive force; but only if our understanding of human cognition remains central to AI development.