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Research Suggests AI Models Can Deliver More Accurate Diagnoses Without Discrimination | HackerNoon

In this paper, we presented the notion of positive-sum fairness and argued that larger disparities are not necessarily harmful, as long as it does not come at the expense of a specific subgroup performance. The general performance, standard fairness and positive-sum fairness of four models was analyzed, each leveraging sensitive attributes in a different way. Our study highlights the need for a nuanced understanding of fairness metrics and their implications in real-world applications. Good incorporation

Demographic December 31, 2024

How AI Models Can Detect Lung Conditions Fairly | HackerNoon

Table of Links Abstract and Introduction Related work Methods 3.1 Positive-sum fairness 3.2 Application Experiments 4.1 Initial results 4.2 Positive-sum fairness Conclusion and References 4.2 Positive-sum fairness between protected subgroups for M2 compared with M1 cannot be considered harmful as every protected subgroup’s performance was individually increased. On the other hand, for lung lesions, model M4 improved fairness (smaller disparity between the most advantaged and least advantaged subgroups) as shown in the figure 3a. However,

Demographic December 31, 2024

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New Findings Show How Positive-Sum Fairness Changes the Performance of Medical AI Models | HackerNoon

Table of Links Abstract and Introduction Related work Methods 3.1 Positive-sum fairness 3.2 Application Experiments 4.1 Initial results 4.2 Positive-sum fairness Conclusion and References 4.1 Initial results According to traditional group fairness, in assessing the results of the four models shown in figure 3a one could conclude that: M2 improves the overall performance Our results show that M2 outperforms M1 in terms of AUROC. This is in line with our expectation as we are providing

Demographic December 31, 2024

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New Study Shows How Positive-Sum Fairness Impacts Medical AI Models in Chest Radiography | HackerNoon

Table of Links Abstract and Introduction Related work Methods 3.1 Positive-sum fairness 3.2 Application Experiments 4.1 Initial results 4.2 Positive-sum fairness Conclusion and References 4 Experiments Data We use chest radiographs from MIMIC-CXR-JPG [16,29]. The dataset has annotations for 14 findings. However, we focus on lung lesions, pneumonia, pleural effusion and consolidation as the diseases associated with these findings have been shown to be correlated with ethnicity [4,17,33]. We use only frontal images and split

Demographic December 30, 2024

How Sensitive Data Affects Fairness and Accuracy in Medical AI Models | HackerNoon

Table of Links Abstract and Introduction Related work Methods 3.1 Positive-sum fairness 3.2 Application Experiments 4.1 Initial results 4.2 Positive-sum fairness Conclusion and References 3.2 Application To put this fairness notion into practice and show the difference with traditional group fairness, we compare three models which use sensitive attributes to a baseline model. The way sensitive attributes are used by the model is known to have an impact on the fairness and performance of the

Demographic December 30, 2024

Introducing Positive-Sum Fairness: A New Way to Balance Performance and Equity in Medical AI | HackerNoon

Table of Links Abstract and Introduction Related work Methods 3.1 Positive-sum fairness 3.2 Application Experiments 4.1 Initial results 4.2 Positive-sum fairness Conclusion and References 3 Methods 3.1 Positive-sum fairness We introduce the principle of positive-sum fairness, which analyzes fairness from the prism of harmful and non harmful disparities. When looking at changes in model performance and disparities between protected subgroups, there are several explanations for a gap in performance between the most and least advantaged

Demographic December 30, 2024

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How Bias in Medical AI Affects Diagnoses Across Different Groups | HackerNoon

Table of Links Abstract and Introduction Related work Methods 3.1 Positive-sum fairness 3.2 Application Experiments 4.1 Initial results 4.2 Positive-sum fairness Conclusion and References Bias is commonly identified in medical image analysis applications [38,40]. For instance [6], a CNN trained on brain MRI resulted in a significant difference between ethnicities. Seyyed-Kalantari et al. [32] observed that minorities received higher rates of algorithmic underdiagnosis. Zong et al. [40] assessed bias mitigation algorithms inand out-of-distribution settings. The

Demographic December 29, 2024

Exploring Positive-Sum Fairness in Medical AI | HackerNoon

Authors: (1) Samia Belhadj∗, Lunit Inc., Seoul, Republic of Korea ([email protected]); (2) Sanguk Park [0009 −0005 −0538 −5522]*, Lunit Inc., Seoul, Republic of Korea ([email protected]); (3) Ambika Seth, Lunit Inc., Seoul, Republic of Korea ([email protected]); (4) Hesham Dar [0009 −0003 −6458 −2097], Lunit Inc., Seoul, Republic of Korea ([email protected]); (5) Thijs Kooi [0009 −0003 −6458 −2097], Kooi, Lunit Inc., Seoul, Republic of Korea ([email protected]). Table of Links Abstract and Introduction Related work Methods 3.1 Positive-sum

Demographic December 29, 2024

Demographic

Research Suggests AI Models Can Deliver More Accurate Diagnoses Without Discrimination | HackerNoon

How AI Models Can Detect Lung Conditions Fairly | HackerNoon

New Findings Show How Positive-Sum Fairness Changes the Performance of Medical AI Models | HackerNoon

New Study Shows How Positive-Sum Fairness Impacts Medical AI Models in Chest Radiography | HackerNoon

How Sensitive Data Affects Fairness and Accuracy in Medical AI Models | HackerNoon

Introducing Positive-Sum Fairness: A New Way to Balance Performance and Equity in Medical AI | HackerNoon

How Bias in Medical AI Affects Diagnoses Across Different Groups | HackerNoon

Exploring Positive-Sum Fairness in Medical AI | HackerNoon

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