TY - GEN
T1 - Bias in Context
T2 - 18th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2024
AU - Heaney, Andrea
AU - Murphy, Emma
AU - Hickey, Eugene
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.
PY - 2025
Y1 - 2025
N2 - Women have been underrepresented in medical research in the past; this, in conjunction with implicit biases women receive, can lead to a decrease in the standard of care. Artificial intelligence (AI) systems have the potential to aid in creating fairer healthcare for women. However, there is still a need to give more definition to the problem to fully understand when women are biased against unfairly and, conversely when sex is a factor for a good reason. 15 semi-structured interviews were conducted with healthcare practitioners to gather their perceptions on women’s healthcare. A semantic thematic analysis of these interviews yielded the following themes: Gender Influencing Health, Pregnancy, Social Factors, General Health, Treatment, Research and Training. These themes highlight that context is key to understanding the biases in women’s health and that this context is critical when developing AI models for healthcare.
AB - Women have been underrepresented in medical research in the past; this, in conjunction with implicit biases women receive, can lead to a decrease in the standard of care. Artificial intelligence (AI) systems have the potential to aid in creating fairer healthcare for women. However, there is still a need to give more definition to the problem to fully understand when women are biased against unfairly and, conversely when sex is a factor for a good reason. 15 semi-structured interviews were conducted with healthcare practitioners to gather their perceptions on women’s healthcare. A semantic thematic analysis of these interviews yielded the following themes: Gender Influencing Health, Pregnancy, Social Factors, General Health, Treatment, Research and Training. These themes highlight that context is key to understanding the biases in women’s health and that this context is critical when developing AI models for healthcare.
KW - Artificial Intelligence Bias
KW - Gender Bias
KW - Health Equity
UR - https://www.scopus.com/pages/publications/105003902859
U2 - 10.1007/978-3-031-85572-6_11
DO - 10.1007/978-3-031-85572-6_11
M3 - Conference contribution
AN - SCOPUS:105003902859
SN - 9783031855719
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 179
EP - 197
BT - Pervasive Computing Technologies for Healthcare - 18th EAI International Conference, PervasiveHealth 2024, Proceedings
A2 - Kondylakis, Haridimos
A2 - Triantafyllidis, Andreas
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 17 September 2024 through 18 September 2024
ER -