8 citations · 10 across the 10 of their papers we have counts for
11 papers
Shaping Human-AI Interactions to Provide Improvement Pathways and Balance Competing Objectives
Keziah Naggita
When an AI system is deployed, the individuals who use and or are evaluated by it form beliefs about how the system operates and use those beliefs to strategically present their pr…
Revealing Positive and Negative Role Models to Help People Make Good Decisions
Avrim Blum, Keziah Naggita, Matthew R. Walter +1
We consider a setting where agents take action by following their role models in a social network, and study strategies for a social planner to help agents by revealing whether the…
A case for data valuation transparency via DValCards
Keziah Naggita, Julienne LaChance
Following the rise in popularity of data-centric machine learning (ML), various data valuation methods have been proposed to quantify the contribution of each datapoint to desired…
PAC Learning with Improvements
Idan Attias, Avrim Blum, Keziah Naggita +3
One of the most basic lower bounds in machine learning is that in nearly any nontrivial setting, it takes samples to learn to error (and more, if the…
Learning Actionable Counterfactual Explanations in Large State Spaces
Keziah Naggita, Matthew R. Walter, Avrim Blum
Recourse generators provide actionable insights, often through feature-based counterfactual explanations (CFEs), to help negatively classified individuals understand how to adjust…
Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data
Keziah Naggita, Julienne LaChance, Alice Xiang
Biases in large-scale image datasets are known to influence the performance of computer vision models as a function of geographic context. To investigate the limitations of standar…
The Equity Framework: Fairness Beyond Equalized Predictive Outcomes
Keziah Naggita, J. Ceasar Aguma
Machine Learning (ML) decision-making algorithms are now widely used in predictive decision-making, for example, to determine who to admit and give a loan. Their wide usage and con…
Setting Fair Incentives to Maximize Improvement
Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum +1
We consider the problem of helping agents improve by setting short-term goals. Given a set of target skill levels, we assume each agent will try to improve from their initial skill…
On classification of strategic agents who can both game and improve
Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum +1
In this work, we consider classification of agents who can both game and improve. For example, people wishing to get a loan may be able to take some actions that increase their per…
Online Platforms and the Fair Exposure Problem Under Homophily
Jakob Schoeffer, Alexander Ritchie, Keziah Naggita +3
In the wake of increasing political extremism, online platforms have been criticized for contributing to polarization. One line of criticism has focused on echo chambers and the re…
The Strategic Perceptron
Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum +1
The classical Perceptron algorithm provides a simple and elegant procedure for learning a linear classifier. In each step, the algorithm observes the sample's position and label an…