Independent: Delegates in CSTD set aside their differences for equitable AI healthcare

 

Delegates in CSTD set aside their differences for equitable AI healthcare

The UN CSTD met this Saturday to discuss the future of AI in healthcare, arguing over access to technology, job opportunities, and more, coming to a shaky consensus at the end of the second session. 

By: a delegate from the Independent

With technology becoming ever-present in people’s lives, the question arises of how we can use technology, such as AI, to solve previously unsolvable problems. This Saturday, the United Nations Commission on Science and Technology for Development (CSTD) debated the topic of the Implications of Artificial Intelligence within the Healthcare System, beginning to form solidified blocs with similar areas of focus after hours of contentious debate. 

Delegates’ focus of committee was the implementation of equity in a variety of aspects, whether it was ensuring developing nations had access to AI technology, making sure healthcare workers did not lose job opportunities to AI, and maintaining equality in data collection. However, with many differences in priorities, countries often ended up in conflict with each other. 

One such conflict today was between developed and developing nations over the inequities in access to AI technology. The delegation of Venezuela explained their situation, “In our country, the medical supplies are not enough, and our technology is limited.” The delegation of Turkey had an agreeing viewpoint, adding on, “When you talk about equity and equality, you can give every country an equal amount but that’ll only benefit some countries. But if you give every country an equitable amount, you give each country needs, rather than what other countries need.” Many developing countries shared the viewpoint that richer, more developed countries, have disproportionately better access to both healthcare and new technology. As a result, they felt that debate could not move forward until this inequity was addressed. 

However, delegates were ultimately able to put aside their differences and form partnerships between developed countries, developing countries, and NGOs in order to help developing countries gain access to their fair share of technology. 

Delegates also debated on the impact of AI on job opportunities. They questioned the role of AI in healthcare with respect to our current medical workers. Should AI technology replace medical jobs entirely, or should it only be used as a tool to help today’s medical workers? How well equipped is our current medical workforce to handle the changes that would come with their jobs with increased use of AI in healthcare?

Some countries believed that AI would change the skills needed for jobs in healthcare, and as a result, education would be necessary for healthcare workers and the youth. The delegation of Vietnam explained, “In order to combat unemployment in the medical field because we need doctors to be able to adapt to AI and work hand in hand with it, we’re thinking about educating the youth so the next generation will be able to work hand in hand with AI.” In order to implement this, the delegation of Japan explained their solutions to train doctors to adjust to this change through an online course or workshops. 

The UK believes that AI should not have such a prevalent role in healthcare, explaining that “AI should supplement, rather than replace current healthcare.” The UK emphasized that AI should not endanger current job opportunities, and instead enhance them. 

While the delegates focused on equity to access AI technology and job opportunity, the chairs believed that there was a variety of topics that delegates didn’t discuss much, and dove into a more technical understanding of the resources necessary to use AI technology. 

The chairs gave a mini lecture about a variety of topics, from maintaining ethics in the use of AI, possible bias in data, preventing the abuse of data, and holding companies in AI accountable for issues. In particular, chair Ashwat emphasized biased data, saying that, “Models are only as good as the data used to train it.” There is commonly bias in that some groups, whether it is based on their age, race, or other factors, are significantly underrepresented in data, making it difficult to create effective models and treatments for everyone. 

While delegates did not debate on this during the second session, this will surely be a contentious topic in the next session. Delegates will need to define new standards for diverse data, determine how to allocate more resources to gather this data, and hold companies accountable for ensuring that these standards are being followed. Given the constant conflict between developing and developed countries so far in committee and the increased funding that these policies would need, diversifying data will surely bring up increased argument over who will be funding these solutions. As resolutions start to form and developed and developing countries are starting to concur, it will be interesting to see whether this conflict will split up blocs once again.