Month: June 2024

How does fairness work in organ transplantation? 

At present, the current organ allocation process is based on:

• First, relevant medical criteria – such as the severity of the potential recipient’s condition and blood type – as well as the geographic proximity of the donor and recipient.
• Secondly, the potential recipients are placed onto a list in which, after the previous criteria are taken into account, fairness in organ allocation is considered on a first come-first served model where those who have been on the waitlist the longest are prioritized to receive a matched organ. 

The issue at hand is that potential recipients matched with higher-risk kidneys often decline to accept such kidneys since they are likely to receive a more desirable kidney in the near future. This requires moving further down the wait-list, which is a time-consuming process and “time is tissue.” This project’s goal is to introduce an AI-assisted decision-making tool to operationalize fairness in the algorithm and provide a “first and best”-served model that reflects the same level of fairness as in standard organ allocation, but offers a more precise way of matching less desirable kidneys with recipients who are likely to accept them. In short, who is most likely to accept and to succeed with this kidney

Why AI? 

Algorithms can help simplify complex systems. However, algorithms use historical data to make estimates, which can introduce bias from previous decisions and changing policies. This is where the fairness of algorithms comes into play. We want to evaluate the models, and not the current process, to ensure the AI generalizations are actually helpful. Our AI tool aims to be a fair decision assistant. Surgeons and their patients have the final say and the power to override or ignore the algorithm’s prediction. Algorithms can’t see external factors, such as a support system for the recipient, or if the recipient is comfortable with the risk factors for a particular offer and other important factors for a successful transplant. Surgeons and transplant professionals have access to this. With the development of this AI, surgeons can see the recommendation and add additional factors to make the final decision of kidney transplantation in collaboration with their patient.

Project Overview:

This project aims to bring significant improvements to how kidneys are offered for transplant while examining and addressing biases in AI systems used to support decision-making. It involves developing fairer AI systems that balance the needs of transplant candidates, transplant centers, and OPOs. The main goals are to:

1. Understand different stakeholder preferences to identify biases in the AI system.
2. Combine these preferences to determine a fair approach.
3. Enhance the AI system’s fairness based on the combined preferences.

In simpler terms, the project looks at how biases in AI can affect decisions about kidney transplants and works to create a fairer system that considers the needs and views of everyone involved.

Contributors: Jason Eberl (SLU), Michael Miller (SLU), Venkata Sriram Siddhardh Nadendla (S&T), Mukund Telukunta (S&T)