Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Liu, Lydia T."'
This study examines fairness within the rideshare industry, focusing on both drivers' wages and riders' trip fares. Through quantitative analysis, we found that drivers' hourly wages are significantly influenced by factors such as race/ethnicity, hea
Externí odkaz:
http://arxiv.org/abs/2407.20522
Predicting future outcomes is a prevalent application of machine learning in social impact domains. Examples range from predicting student success in education to predicting disease risk in healthcare. Practitioners recognize that the ultimate goal i
Externí odkaz:
http://arxiv.org/abs/2309.04470
Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout, to assisting in university admissions, and facilitating the rise of MOOCs. Given the rapid growth of these novel uses, there is a
Externí odkaz:
http://arxiv.org/abs/2209.03929
Strategic classification studies the design of a classifier robust to the manipulation of input by strategic individuals. However, the existing literature does not consider the effect of competition among individuals as induced by the algorithm desig
Externí odkaz:
http://arxiv.org/abs/2109.08240
We study two-sided matching markets in which one side of the market (the players) does not have a priori knowledge about its preferences for the other side (the arms) and is required to learn its preferences from experience. Also, we assume the playe
Externí odkaz:
http://arxiv.org/abs/2012.07348
Autor:
Rolf, Esther, Simchowitz, Max, Dean, Sarah, Liu, Lydia T., Björkegren, Daniel, Hardt, Moritz, Blumenstock, Joshua
While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic policies which explicitly trade off between a private objective (such as pro
Externí odkaz:
http://arxiv.org/abs/2003.06740
Autor:
Liu, Lydia T., Wilson, Ashia, Haghtalab, Nika, Kalai, Adam Tauman, Borgs, Christian, Chayes, Jennifer
The long-term impact of algorithmic decision making is shaped by the dynamics between the deployed decision rule and individuals' response. Focusing on settings where each individual desires a positive classification---including many important applic
Externí odkaz:
http://arxiv.org/abs/1910.04123
Stable matching, a classical model for two-sided markets, has long been studied with little consideration for how each side's preferences are learned. With the advent of massive online markets powered by data-driven matching platforms, it has become
Externí odkaz:
http://arxiv.org/abs/1906.05363
In photon-limited imaging, the pixel intensities are affected by photon count noise. Many applications, such as 3-D reconstruction using correlation analysis in X-ray free electron laser (XFEL) single molecule imaging, require an accurate estimation
Externí odkaz:
http://arxiv.org/abs/1812.08789
We clarify what fairness guarantees we can and cannot expect to follow from unconstrained machine learning. Specifically, we characterize when unconstrained learning on its own implies group calibration, that is, the outcome variable is conditionally
Externí odkaz:
http://arxiv.org/abs/1808.10013