As chatbots become increasingly widespread, personalization has emerged as a key feature for accommodating diverse users and use cases. Personalization enables better responses rather than generic answers, but when using demographic attributes to drive recommendations, can also amplify harmful stereotypes. For instance, individuals from different cultural backgrounds may meaningfully want culturally distinct food preferences and values incorporated, but not different job recommendations. Yet, little is known about how these user preferences relate to actual platform behavior. Through a survey of user preferences and a novel field study methodology where users input our standardized questions to their own personalized chatbot interfaces then share the outputs back to us, we contribute real-world personalization insights on the gaps between what users want versus actually receive. We generally find that there is minimal race- and gender-based personalization in practice, which conflicts with users’ desires in some domains, e.g., cuisines. We conceptualize our findings through the framing of a personalization double bind: Black people and women desire more demographic-based personalization than White people and men in order to avoid overly generic responses (indirect discrimination), yet simultaneously express greater concern about stereotypical responses (direct discrimination).

Advances in large language models (LLMs) have enabled significant capabilities in audio processing, resulting in state-of-the-art models now known as Large Audio Language Models (LALMs). However, minimal work has been done to measure audio understanding beyond automatic speech recognition (ASR). This paper closes that gap by proposing a benchmark suite, SCENEBench (Spatial, Cross-lingual, Environmental, Non-speech Evaluation), that targets a broad form of audio comprehension across four real-world categories: background sound understanding, noise localization, cross-linguistic speech understanding, and vocal characterizer recognition. In addition to performance, we also measure model latency. The purpose of this benchmark suite is to assess the audio beyond just what words are said— rather, in how they are said and the non-speech components of the audio. To strengthen ecological validity, we include a small human-recorded evaluation split per category. Based on the needs articulated by audio understanding use-cases of accessibility technology and industrial noise monitoring, this benchmark reveals critical gaps in current LALMs. The performance in each task is quite varied, with some tasks having performance far below random chance and others with high accuracy. We also provide a structured error taxonomy to characterize standard failure modes across tasks. These results provide direction for targeted improvements in model capabilities.

Algorithmic fairness has conventionally adopted the mathematically convenient perspective of racial color-blindness (i.e., difference unaware treatment). However, we contend that in a range of important settings, group difference awareness matters. For example, differentiating between groups may be necessary in legal contexts (e.g., the U.S. compulsory draft applies to men but not women) and harm assessments (e.g., referring to girls as “terrorists” may be less harmful than referring to Muslim people as such). Thus, in contrast to most fairness work, we study fairness through the perspective of treating people differently — when it is contextually appropriate to. We first introduce an important distinction between descriptive (fact-based), normative (value-based), and correlation (association-based) benchmarks. This distinction is significant because each category requires separate interpretation and mitigation tailored to its specific characteristics. Then, we present a benchmark suite composed of eight different scenarios for a total of 16k questions that enables us to assess difference awareness. Finally, we show results across ten models that demonstrate difference awareness is a distinct dimension to fairness where existing bias mitigation strategies may backfire.