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pro vyhledávání: '"Bower, Amanda"'
Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user. In recent years, methods relying on stochastic ordering have been developed to create "fairer" rankings that reduce in
Externí odkaz:
http://arxiv.org/abs/2209.05000
Publikováno v:
2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22), June 21--24, 2022, Seoul, Republic of Korea
When a model's performance differs across socially or culturally relevant groups--like race, gender, or the intersections of many such groups--it is often called "biased." While much of the work in algorithmic fairness over the last several years has
Externí odkaz:
http://arxiv.org/abs/2205.05770
Autor:
Lazovich, Tomo, Belli, Luca, Gonzales, Aaron, Bower, Amanda, Tantipongpipat, Uthaipon, Lum, Kristian, Huszar, Ferenc, Chowdhury, Rumman
The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of systems such as machine learning (ML) models amplifying existing societal biases. Most metrics attempting to quantify disparities resulting from
Externí odkaz:
http://arxiv.org/abs/2202.01615
We develop an algorithm to train individually fair learning-to-rank (LTR) models. The proposed approach ensures items from minority groups appear alongside similar items from majority groups. This notion of fair ranking is based on the definition of
Externí odkaz:
http://arxiv.org/abs/2103.11023
Autor:
Bower, Amanda, Balzano, Laura
Publikováno v:
ICML 2020
We consider the problem of estimating a ranking on a set of items from noisy pairwise comparisons given item features. We address the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is tha
Externí odkaz:
http://arxiv.org/abs/2002.09615
We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs. For example, the performance of a resume screening system should be invariant under chang
Externí odkaz:
http://arxiv.org/abs/1907.00020
We propose a regression-based approach to removing implicit biases in representations. On tasks where the protected attribute is observed, the method is statistically more efficient than known approaches. Further, we show that this approach leads to
Externí odkaz:
http://arxiv.org/abs/1807.00461
Autor:
Bower, Amanda, Kitchen, Sarah N., Niss, Laura, Strauss, Martin J., Vargas, Alexander, Venkatasubramanian, Suresh
This work facilitates ensuring fairness of machine learning in the real world by decoupling fairness considerations in compound decisions. In particular, this work studies how fairness propagates through a compound decision-making processes, which we
Externí odkaz:
http://arxiv.org/abs/1707.00391
Zeckendorf proved that any integer can be decomposed uniquely as a sum of non-adjacent Fibonacci numbers, $F_n$. Using continued fractions, Lekkerkerker proved the average number of summands of an $m \in [F_n, F_{n+1})$ is essentially $n/(\varphi^2 +
Externí odkaz:
http://arxiv.org/abs/1402.3912
Many problems in additive number theory, such as Fermat's last theorem and the twin prime conjecture, can be understood by examining sums or differences of a set with itself. A finite set $A \subset \mathbb{Z}$ is considered sum-dominant if $|A+A|>|A
Externí odkaz:
http://arxiv.org/abs/1212.2930