Sandra Baum
Modtager af et rejselegat på 30.000 kr.
Det menneskelige element: Anvendelse af universelt design til at mindske selektionsbias i datasæt til medicinsk AI
Selektionsbias udgør en væsentlig trussel mod AI-modellers effektivitet og retfærdighed og kan føre til fejldiagnoser og forsinket behandling af underrepræsenterede grupper, eksempelvis mennesker med funktionsnedsættelser relateret til sygdom. For at imødegå dette problem er der et presserende behov for forskning og innovation inden for udviklingen af mere inkluderende algoritmer og udvælgelsesmetoder med det formål at sikre, at medicinske AI-systemer kommer alle dele af samfundet til gode.
Sandra Baum planlægger at undersøge problemstillingen ved at:
- Undersøge årsagerne til og konsekvenserne af selektionsbias samt mulige løsninger gennem en gennemgang af den eksisterende akademiske litteratur.
- Vurdere problemstillingerne på området op imod målene for universelt design.
- Udarbejde en praktisk ramme, der kobler de identificerede problemstillinger med principperne for universelt design.
- Anvende principperne for universelt design til at bidrage til at mindske selektionsbias i AI-baserede medicinske enheder.
Sandra planlægger at gennemføre sin undersøgelse på University of Maryland i sommeren 2024.
Se pitchen nedenfor.
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Sandra Baum
's undersøgelse
Sandra er gået i gang med sin undersøgelse i USA. Nedenfor kan du læse indlæg fra hendes rejse undervejs, hvor hun beskriver sine erfaringer og den indsigt, hun indtil videre har opnået.
April 2024
How are underrepresented groups, such as people with disabilities represented in medical data?
Scholarship Travel grant winner Sandra Baum is currently visiting University of Maryland, she is pictured here with her host Professor Paul H Yi.
Sandra reached out to Professor Yi because of his impressive work on the topic of selection bias. The Professors medical background also makes him ideal as a mentor in her investigation of how to reduce bias in AI models used in healthcare, for a more accessible and equitable world for all.
Right now Sandra is investigating how different historically underrepresented groups, such as people with disabilities are represented in the data. This involves investigating various medical information datasets to see what kind of information is available on the participants. Sandra is analyzing large sets of data, using tools such as web scraping as well as studying literature on people with disabilities in medical datasets.
May 2024
June 2024
Sandra Baum: We need to integrate Universal Design more broadly
Marginalized groups are challenged by selection bias in medical AI systems, as it can lead to misdiagnoses. By visiting the University of Maryland, IT student Sandra Baum investigated how to address this problem.
Main findings
“My project draws attention to the need to integrate Universal Design more broadly. Not only would it be mentioned in policy documents, but it would also be included in research where it could affect the actual practices of creating and evaluating models in the medical field.
Additionally, my project shows that voluntary disclosure programs, including transparency on the groups used to train and evaluate AI algorithms, can be effective. Furthermore, non-technical approaches can be effective in mitigating bias.”
The use of Universal Design
“My project focused on showing how Universal Design as a paradigm can be instrumentalized to reintroduce the human focus into otherwise impersonal, abstracted, and profit-driven systems that shape our way of life in less visible ways.
More practically, this will benefit and protect the health and safety of many data-underrepresented groups, such as older people not being included in training data for a device meant to predict the risk of heart disease. This shows the real-world challenges caused by short-sighted design approaches.”
Why others should apply
“The program is uniquely flexible. People can choose their knowledge space and country freely, and the choice of project topics is also free, as long as it focuses on Universal Design.”