| title | booktitle | abstract | layout | series | publisher | issn | id | month | tex_title | firstpage | lastpage | page | order | cycles | bibtex_author | author | date | address | container-title | volume | genre | issued | extras | |||||||||||||||||||||||||||||||
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Information Discrepancy in Strategic Learning |
Proceedings of the 39th International Conference on Machine Learning |
We initiate the study of the effects of non-transparency in decision rules on individuals’ ability to improve in strategic learning settings. Inspired by real-life settings, such as loan approvals and college admissions, we remove the assumption typically made in the strategic learning literature, that the decision rule is fully known to individuals, and focus instead on settings where it is inaccessible. In their lack of knowledge, individuals try to infer this rule by learning from their peers (e.g., friends and acquaintances who previously applied for a loan), naturally forming groups in the population, each with possibly different type and level of information regarding the decision rule. We show that, in equilibrium, the principal’s decision rule optimizing welfare across sub-populations may cause a strong negative externality: the true quality of some of the groups can actually deteriorate. On the positive side, we show that, in many natural cases, optimal improvement can be guaranteed simultaneously for all sub-populations. We further introduce a measure we term information overlap proxy, and demonstrate its usefulness in characterizing the disparity in improvements across sub-populations. Finally, we identify a natural condition under which improvement can be guaranteed for all sub-populations while maintaining high predictive accuracy. We complement our theoretical analysis with experiments on real-world datasets. |
inproceedings |
Proceedings of Machine Learning Research |
PMLR |
2640-3498 |
bechavod22a |
0 |
Information Discrepancy in Strategic Learning |
1691 |
1715 |
1691-1715 |
1691 |
false |
Bechavod, Yahav and Podimata, Chara and Wu, Steven and Ziani, Juba |
|
2022-06-28 |
Proceedings of the 39th International Conference on Machine Learning |
162 |
inproceedings |
|
|