20 citations · 40 across the 8 of their papers we have counts for
9 papers
Representation-Aware Experimentation: Group Inequality Analysis for A/B Testing and Alerting
Rina Friedberg, Stuart Ambler, Guillaume Saint-Jacques
As companies adopt increasingly experimentation-driven cultures, it is crucial to develop methods for understanding any potential unintended consequences of those experiments. We m…
Choosing an algorithmic fairness metric for an online marketplace: Detecting and quantifying algorithmic bias on LinkedIn
YinYin Yu, Guillaume Saint-Jacques
In this paper, we derive an algorithmic fairness metric from the fairness notion of equal opportunity for equally qualified candidates for recommendation algorithms commonly used b…
Measuring Equity: Funnel Representation Measurement
Wentao Su, Guillaume Saint-Jacques
We present a methodology to measure the gender representation for online product funnels. It is a part of the overall equity framework to better understand our products through fun…
An Outcome Test of Discrimination for Ranked Lists
Jonathan Roth, Guillaume Saint-Jacques, YinYin Yu
This paper extends Becker (1957)'s outcome test of discrimination to settings where a (human or algorithmic) decision-maker produces a ranked list of candidates. Ranked lists are p…
Fairness through Experimentation: Inequality in A/B testing as an approach to responsible design
Guillaume Saint-Jacques, Amir Sepehri, Nicole Li +1
As technology continues to advance, there is increasing concern about individuals being left behind. Many businesses are striving to adopt responsible design practices and avoid an…
A Method for Measuring Network Effects of One-to-One Communication Features in Online A/B Tests
Guillaume Saint-Jacques, James Eric Sorenson, Nanyu Chen +1
A/B testing is an important decision making tool in product development because can provide an accurate estimate of the average treatment effect of a new features, which allows dev…
Using Ego-Clusters to Measure Network Effects at LinkedIn
Guillaume Saint-Jacques, Maneesh Varshney, Jeremy Simpson +1
A network effect is said to take place when a new feature not only impacts the people who receive it, but also other users of the platform, like their connections or the people who…
Estimating Network Effects Using Naturally Occurring Peer Notification Queue Counterfactuals
Craig Tutterow, Guillaume Saint-Jacques
Randomized experiments, or A/B tests are used to estimate the causal impact of a feature on the behavior of users by creating two parallel universes in which members are simultaneo…
Testing for arbitrary interference on experimentation platforms
Jean Pouget-Abadie, Martin Saveski, Guillaume Saint-Jacques +4
Experimentation platforms are essential to modern large technology companies, as they are used to carry out many randomized experiments daily. The classic assumption of no interfer…