BIAS ’23 – Day 1: Dr Kacper Sokol talk – The Difference Between Interactive AI and Interactive AI

This blog is written by CDT AI PhD student Beth Pearson

The first of the day 1 talks of the Bristol Interactive AI Summer School (BIAS) ended with a thought-provoking talk by Dr. Kacper Sokol on The Difference Between Interactive AI and Interactive AI. Kacper began by explaining that social sciences have decades worth of research on how humans reason and explain. Now, with an increasing demand for AI and ML systems to become more human-centered, with a focus on explainability, it makes sense to use insights from social sciences to guide the development of these models.

Humans often explain things in a contrastive and social manner, which has led to counterfactual explanations being introduced by AI and ML researchers. Counterfactuals are statements relating to what has not happened or is not the case, for example, “If I hadn’t taken a sip of this hot coffee, I wouldn’t have burned my tongue.” Counterfactual explanations have the advantage of being suitable for both technical and lay audiences; however, they only provide information about one choice that the model makes, so they can bias the recipient.

Kacper then described his research focus on pediatric sepsis. Sepsis is a life-threatening condition that develops from an infection and is the third leading cause of death worldwide. Pediatric sepsis specifically refers to cases occurring in children. Sepsis is a particularly elusive disease because it can manifest differently in different people, and patients respond differently to treatments, making it challenging to identify the best treatment strategy for a specific patient. Kacper hopes that AI will be able to help solve this problem in this day and age.

Importantly, the AI being applied to the pediatric sepsis problem is interactive and aims to support and work alongside humans rather than replace them. It is crucial that the AI aligns with the current clinical workflow so that it can be easily adopted into hospitals and GP practices. Kacper highlights that this is particularly important for pediatricians as they have been highly skeptical of AI in the past. However, now that AI has proven successful in adult branches of medicine, they are starting to warm to the idea.

Pediatric sepsis comes with many challenges. Pediatric sepsis has less data available than adult sepsis, and there is rapid deterioration, meaning that early diagnosis is vital. Unfortunately, there are many diseases in children that mimic the symptoms of sepsis, making it not always easy to diagnose. One of the main treatments for sepsis is antibiotics; however, since children are a vulnerable population, we don’t want to administer antibiotics unnecessarily. Currently, it is estimated that 98% of children receive antibiotics unnecessarily, which is contributing to antimicrobial resistance and can cause drug toxicity.

AI has the potential to help with these challenges; however, the goal is to augment, not disrupt, the current workflow. Humans can have great intuition and can observe cues that lead to excellent decision-making, which is particularly valuable in medicine. An experiment was carried out on nurses in neonatal care, which showed that nurses were able to correctly predict which infants were developing life-threatening infections without having any knowledge of the blood test results. Despite being able to identify the disease, the nurses were unable to explain their judgment process. The goal is to add automation from AI but still retain certain key aspects of human decision-making.

How much and where the automation should take place is not a simple question, however. You could replace biased humans with algorithms, but algorithms can also be biased, so this wouldn’t necessarily improve anything. Another option would be to have algorithms propose decisions and have humans check them; however, this still requires humans to carry out mundane tasks. Would it really be better than no automation at all? Kacper then asks: if you can prove an AI model is capable of predicting better than a human, and a human decides to use their own judgment to override the model, could it be considered malpractice?

Another proposed solution for implementing interactive AI is to have humans make the decision, with the AI model presenting arguments for and against that decision to help the human decide whether to change their mind or not.

The talk ends by discussing how interactive AI may be deployed in real-life scenarios. Since the perfect integration of AI and humans doesn’t quite exist yet, Kacper suggests that clinical trials might be a good idea, where suggestions made by AI models are marked as ‘for research only’ to keep them separated from other clinical workflows.

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