Zobrazeno 1 - 10
of 47
pro vyhledávání: '"JAGADEESAN, MEENA"'
Emerging marketplaces for large language models and other large-scale machine learning (ML) models appear to exhibit market concentration, which has raised concerns about whether there are insurmountable barriers to entry in such markets. In this wor
Externí odkaz:
http://arxiv.org/abs/2409.03734
As AI systems enter into a growing number of societal domains, these systems increasingly shape and are shaped by user preferences, opinions, and behaviors. However, the design of AI systems rarely accounts for how AI and users shape one another. In
Externí odkaz:
http://arxiv.org/abs/2404.12366
When deployed in the world, a learning agent such as a recommender system or a chatbot often repeatedly interacts with another learning agent (such as a user) over time. In many such two-agent systems, each agent learns separately and the rewards of
Externí odkaz:
http://arxiv.org/abs/2403.00188
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. These interactions form feedback loops: LLM outputs affect the
Externí odkaz:
http://arxiv.org/abs/2402.06627
A common explanation for negative user impacts of content recommender systems is misalignment between the platform's objective and user welfare. In this work, we show that misalignment in the platform's objective is not the only potential cause of un
Externí odkaz:
http://arxiv.org/abs/2401.05304
As the scale of machine learning models increases, trends such as scaling laws anticipate consistent downstream improvements in predictive accuracy. However, these trends take the perspective of a single model-provider in isolation, while in reality
Externí odkaz:
http://arxiv.org/abs/2306.14670
In content recommender systems such as TikTok and YouTube, the platform's recommendation algorithm shapes content producer incentives. Many platforms employ online learning, which generates intertemporal incentives, since content produced today affec
Externí odkaz:
http://arxiv.org/abs/2306.07479
Competition between traditional platforms is known to improve user utility by aligning the platform's actions with user preferences. But to what extent is alignment exhibited in data-driven marketplaces? To study this question from a theoretical pers
Externí odkaz:
http://arxiv.org/abs/2208.14423
Algorithmic recommender systems such as Spotify and Netflix affect not only consumer behavior but also producer incentives. Producers seek to create content that will be shown by the recommendation algorithm, which can impact both the diversity and q
Externí odkaz:
http://arxiv.org/abs/2206.13489
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. We relate performative power to t
Externí odkaz:
http://arxiv.org/abs/2203.17232