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 Any classifier which has the exact same overall performance and exact same performance per protected subgroup (race) as the baseline, would be at coordinate (0,0). Any classifier that has a negative x-coordinate, would have a lower general performance than M1 and any classifier that has a negative y-coordinate would have at least one protected subgroup with a lower AUROC than M1 (at least one subgroup negatively impacted by the changes brought to the baseline model). For lung lesions, figure 3b shows that M2 appears in the positive side of the x and y axes, meaning that the performance was improved without harming any subgroup’s performance. And this even though the figure 3a shows a decrease in fairness (larger disparity between the most advantaged and least advantaged subgroups) for M2 compared with M1. This matches the previous conclusion that the larger performance gap 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, the figure 3b, shows that M4 has negative y coordinates, meaning that at least one subgroup was harmed while trying to achieve a smaller disparity between protected subgroups. Authors:
(1) Samia Belhadj∗, Lunit Inc., Seoul, Republic of Korea (samia.belhadj@lunit.io);
(2) Sanguk Park [0009 −0005 −0538 −5522]*, Lunit Inc., Seoul, Republic of Korea (tony.superb@lunit.io);
(3) Ambika Seth, Lunit Inc., Seoul, Republic of Korea (ambika.seth@lunit.io);
(4) Hesham Dar [0009 −0003 −6458 −2097], Lunit Inc., Seoul, Republic of Korea (heshamdar@lunit.io);
(5) Thijs Kooi [0009 −0003 −6458 −2097], Kooi, Lunit Inc., Seoul, Republic of Korea (tkooi@lunit.io). This paper is available on arxiv under CC BY-NC-SA 4.0 license. 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 Abstract and Introduction Abstract and Introduction Abstract and Introduction Related work Related work Related work Methods 3.1 Positive-sum fairness 3.2 Application Methods 3.1 Positive-sum fairness 3.1 Positive-sum fairness 3.2 Application 3.2 Application Experiments 4.1 Initial results 4.2 Positive-sum fairness Experiments Experiments 4.1 Initial results 4.1 Initial results 4.2 Positive-sum fairness 4.2 Positive-sum fairness Conclusion and References Conclusion and References Conclusion and References 4.2 Positive-sum fairness Any classifier which has the exact same overall performance and exact same performance per protected subgroup (race) as the baseline, would be at coordinate (0,0). Any classifier that has a negative x-coordinate, would have a lower general performance than M1 and any classifier that has a negative y-coordinate would have at least one protected subgroup with a lower AUROC than M1 (at least one subgroup negatively impacted by the changes brought to the baseline model). For lung lesions, figure 3b shows that M2 appears in the positive side of the x and y axes, meaning that the performance was improved without harming any subgroup’s performance. And this even though the figure 3a shows a decrease in fairness (larger disparity between the most advantaged and least advantaged subgroups) for M2 compared with M1. This matches the previous conclusion that the larger performance gap 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, the figure 3b, shows that M4 has negative y coordinates, meaning that at least one subgroup was harmed while trying to achieve a smaller disparity between protected subgroups. Authors: (1) Samia Belhadj∗, Lunit Inc., Seoul, Republic of Korea (samia.belhadj@lunit.io); (2) Sanguk Park [0009 −0005 −0538 −5522]*, Lunit Inc., Seoul, Republic of Korea (tony.superb@lunit.io); (3) Ambika Seth, Lunit Inc., Seoul, Republic of Korea (ambika.seth@lunit.io); (4) Hesham Dar [0009 −0003 −6458 −2097], Lunit Inc., Seoul, Republic of Korea (heshamdar@lunit.io); (5) Thijs Kooi [0009 −0003 −6458 −2097], Kooi, Lunit Inc., Seoul, Republic of Korea (tkooi@lunit.io). Authors: Authors: (1) Samia Belhadj∗, Lunit Inc., Seoul, Republic of Korea (samia.belhadj@lunit.io); (2) Sanguk Park [0009 −0005 −0538 −5522]*, Lunit Inc., Seoul, Republic of Korea (tony.superb@lunit.io); (3) Ambika Seth, Lunit Inc., Seoul, Republic of Korea (ambika.seth@lunit.io); (4) Hesham Dar [0009 −0003 −6458 −2097], Lunit Inc., Seoul, Republic of Korea (heshamdar@lunit.io); (5) Thijs Kooi [0009 −0003 −6458 −2097], Kooi, Lunit Inc., Seoul, Republic of Korea (tkooi@lunit.io). This paper is available on arxiv under CC BY-NC-SA 4.0 license. This paper is available on arxiv under CC BY-NC-SA 4.0 license. available on arxiv

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How AI Models Could Detect Lung Conditions Fairly

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