Large Language Models (LLMs) can impersonate the writing style of authors, characters, and groups of people, but can these personas represent their opinions?If so, it creates opportunities for businesses to obtain early feedback on ideas from a synthetic customerbase.In this paper, we test whether LLM synthetic personas can answer financial wellbeing questions similarly to the responses of a financial wellbeing survey of more than 3,500 Australians.We focus on identifying salient biases of 765 synthetic personas using four state-of-the-art LLMs built over 35 categories of personal attributes.We noticed clear biases related to age, and as more details were included in the personas, their responses increasingly diverged from the survey toward lower financial wellbeing.With these findings, it is possible to understand the areas in which creating synthetic LLM-based customer personas can yield useful feedback for faster product iteration in the financial services industry and potentially other industries.

Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks. Researchers studying classification have generally considered fairness to be a matter of achieving equality of outcomes (or some other metric) between a protected and unprotected group and built algorithmic interventions on this basis. We argue that fairness in real-world application settings in general, and especially in the context of personalized recommendation, is much more complex and multi-faceted, requiring a more general approach. To address the fundamental problem of fairness in the presence of multiple stakeholders, with different definitions of fairness, we propose the Social Choice for Recommendation Under Fairness–Dynamic architecture, which formalizes multistakeholder fairness in recommender systems as a two-stage social choice problem. In particular, we express recommendation fairness as a combination of an allocation and an aggregation problem, which integrate both fairness concerns and personalized recommendation provisions, and derive new recommendation techniques based on this formulation. We demonstrate the ability of our framework to dynamically incorporate multiple fairness concerns using both real-world and synthetic datasets.

Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders that may have competing interests. Previous work in this area has often been limited by fixed, single-objective definitions of fairness, built into algorithms or optimization criteria that are applied to a single fairness dimension or, at most, applied identically across dimensions. These narrow conceptualizations limit the ability to adapt fairness-aware solutions to the wide range of stakeholder needs and fairness definitions that arise in practice. Our work approaches recommendation fairness from the standpoint of computational social choice, using a multi-agent framework. In this paper, we explore the properties of different social choice mechanisms and demonstrate the successful integration of multiple, heterogeneous fairness definitions across multiple data sets.