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Found 19 papers, 5 papers with code
Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry
To study this issue formally, we define a multi-objective high-dimensional regression framework that captures reputational damage, and we characterize the number of data points that a new company needs to enter the market.
Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
As AI systems enter into a growing number of societal domains, these systems increasingly shape and are shaped by user preferences, opinions, and behaviors.
Impact of Decentralized Learning on Player Utilities in Stackelberg Games
To better understand such cases, we examine the learning dynamics of the two-agent system and the implications for each agent's objective.
Feedback Loops With Language Models Drive In-Context Reward Hacking
Language models influence the external world: they query APIs that read and write to web pages, generate content that shapes human behavior, and run system commands as autonomous agents.
Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms
To understand the total impact on the content landscape, we study a game between content creators competing on the basis of engagement metrics and analyze the equilibrium decisions about investment in quality and gaming.
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
A common explanation for negative user impacts of content recommender systems is misalignment between the platform's objective and user welfare.
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
As the scale of machine learning models increases, trends such as scaling laws anticipate consistent downstream improvements in predictive accuracy.
Incentivizing High-Quality Content in Online Recommender Systems
In content recommender systems such as TikTok and YouTube, the platform's recommendation algorithm shapes content producer incentives.
Competition, Alignment, and Equilibria in Digital Marketplaces
Nonetheless, the data sharing assumptions impact what mechanism drives misalignment and also affect the specific form of misalignment (e. g. the quality of the best-case and worst-case market outcomes).
Supply-Side Equilibria in Recommender Systems
Producers seek to create content that will be shown by the recommendation algorithm, which can impact both the diversity and quality of their content.
Performative Power
We introduce the notion of performative power, which measures the ability of a firm operating an algorithmic system, such as a digital content recommendation platform, to cause change in a population of participants.
Regret Minimization with Performative Feedback
Our main contribution is an algorithm that achieves regret bounds scaling only with the complexity of the distribution shifts and not that of the reward function.
Learning Equilibria in Matching Markets from Bandit Feedback
Large-scale, two-sided matching platforms must find market outcomes that align with user preferences while simultaneously learning these preferences from data.
Alternative Microfoundations for Strategic Classification
When reasoning about strategic behavior in a machine learning context it is tempting to combine standard microfoundations of rational agents with the statistical decision theory underlying classification.
Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm
We provide a function space characterization of the inductive bias resulting from minimizing the $\ell_2$ norm of the weights in multi-channel convolutional neural networks with linear activations and empirically test our resulting hypothesis on ReLU networks trained using gradient descent.
Individual Fairness in Pipelines
It is well understood that a system built from individually fair components may not itself be individually fair.
Individual Fairness in Advertising Auctions through Inverse Proportionality
This value stability constraint is expressed as a function that maps the multiplicative distance between value vectors to the maximum allowable $\ell_{\infty}$ distance between the corresponding allocations.
Multi-Category Fairness in Sponsored Search Auctions
Fairness in advertising is a topic of particular concern motivated by theoretical and empirical observations in both the computer science and economics literature.
Understanding Sparse JL for Feature Hashing
al (ICML '09) analyzes the accuracy of sparse JL with sparsity 1 on feature vectors with small $\ell_\infty$-to-$\ell_2$ norm ratio.
