Researchers and developers increasingly rely on toxicity scoring to moderate
generative language model outputs, in settings such as customer service,
information retrieval, and content generation. However, toxicity scoring may
render pertinent information inaccessible, rigidify or "value-lock" cultural
norms, and prevent language reclamation processes, particularly for
marginalized people. In this work, we extend the concept of algorithmic
recourse to generative language models: we provide users a novel mechanism to
achieve their desired prediction by dynamically setting thresholds for toxicity
filtering. Users thereby exercise increased agency relative to interactions
with the baseline system. A pilot study ($n = 30$) supports the potential of
our proposed recourse mechanism, indicating improvements in usability compared
to fixed-threshold toxicity-filtering of model outputs. Future work should
explore the intersection of toxicity scoring, model controllability, user
agency, and language reclamation processes -- particularly with regard to the
bias that many communities encounter when interacting with generative language
models.

通过为毒性过滤设置动态阈值，我们提供了一种新的机制，使用户能够实现他们希望的预测，从而增加了与基线系统的交互中的机构性。一项初步研究支持我们提出的救济机制的潜力，表明与固定阈值毒性过滤模型输出相比，可用性有所改善。未来的工作应该探讨毒性评分、模型可控性、用户机构性和语言重建过程之间的交叉点，特别是关于当与生成性语言模型互动时，许多社区遇到的偏见。

追索索偿：与生成语言模型对话

Recourse for reclamation: Chatting with generative language models

Despite the acknowledgment that the perception of explanations may vary
considerably between end-users, explainable recommender systems (RS) have
traditionally followed a one-size-fits-all model, whereby the same explanation
level of detail is provided to each user, without taking into consideration
individual user's context, i.e., goals and personal characteristics. To fill
this research gap, we aim in this paper at a shift from a one-size-fits-all to
a personalized approach to explainable recommendation by giving users agency in
deciding which explanation they would like to see. We developed a transparent
Recommendation and Interest Modeling Application (RIMA) that provides on-demand
personalized explanations of the recommendations, with three levels of detail
(basic, intermediate, advanced) to meet the demands of different types of
end-users. We conducted a within-subject study (N=31) to investigate the
relationship between user's personal characteristics and the explanation level
of detail, and the effects of these two variables on the perception of the
explainable RS with regard to different explanation goals. Our results show
that the perception of explainable RS with different levels of detail is
affected to different degrees by the explanation goal and user type.
Consequently, we suggested some theoretical and design guidelines to support
the systematic design of explanatory interfaces in RS tailored to the user's
context.

本文旨在研究一种从 “一刀切” 的解释推荐过渡到个性化解释推荐的新方法，通过开发一个透明的基于个人兴趣模型的应用程序，提供可按需定制的解释，以满足不同类型终端用户的需求。研究表明，不同目标解释和用户类型对于不同级别的详细解释的阐释推荐系统的感知程度有不同的影响。因此，我们提出了一些理论和设计指南，以支持个性化设计的解释系统。

更多是否总是更好的？个人特征和细节水平对推荐系统解释感知的影响

Is More Always Better? The Effects of Personal Characteristics and Level  of Detail on the Perception of Explanations in a Recommender System

In the attention economy, video apps employ design mechanisms like autoplay
that exploit psychological vulnerabilities to maximize watch time.
Consequently, many people feel a lack of agency over their app use, which is
linked to negative life effects such as loss of sleep. Prior design research
has innovated external mechanisms that police multiple apps, such as lockout
timers. In this work, we shift the focus to how the internal mechanisms of an
app can support user agency, taking the popular YouTube mobile app as a test
case. From a survey of 120 U.S. users, we find that autoplay and
recommendations primarily undermine sense of agency, while search and playlists
support it. From 13 co-design sessions, we find that when users have a specific
intention for how they want to use YouTube they prefer interfaces that support
greater agency. We discuss implications for how designers can help users
reclaim a sense of agency over their media use.

该研究关注如何提高 YouTube 用户对于内部机制的控制力，结果显示自动播放和推荐对于用户的控制力有负面影响，而搜索和播放列表则支持用户的控制力，设计师可以通过界面设计等方法帮助用户掌控使用媒体的感觉。