User-item fairness tradeoffs in recommendations

S Greenwood, S Chiniah, N Garg - arxiv preprint arxiv:2412.04466, 2024 - arxiv.org
In the basic recommendation paradigm, the most (predicted) relevant item is recommended
to each user. This may result in some items receiving lower exposure than they" should"; to …

How to Strategize Human Content Creation in the Era of GenAI?

SA Esmaeili, K Bhawalkar, Z Feng, D Wang… - arxiv preprint arxiv …, 2024 - arxiv.org
Generative AI (GenAI) will have significant impact on content creation platforms. In this
paper, we study the dynamic competition between a GenAI and a human contributor. Unlike …

[PDF][PDF] Expanding our Participatory Democracy Toolkit using Algorithms, Social Choice, and Social Science

B Flanigan - 2024 - reports-archive.adm.cs.cmu.edu
In most of the world's democracies, policy decisions are primarily made by elected political
officials. However, under mounting dissatisfaction with representative government due to …

[PDF][PDF] Fairness constraints and reward manipulation in stochastic multi-armed bandits

M Kontalexi - 2025 - dspace.lib.ntua.gr
Περίληψη Η παρούσα διπλωματική μελετά το multi-armed bandit πρόβλημα με στοχαστικές
ανταμοιβές, όπου ένας learner παίζει ένα σειριακό παιχνίδι με ένα περιβάλλον για T γύρους …

Risk preferences of learning algorithms

A Haupt, A Narayanan - Games and Economic Behavior, 2024 - Elsevier
Many economic decision-makers today rely on learning algorithms for important decisions.
This paper shows that a widely used learning algorithm—ε-Greedy—exhibits emergent risk …