Handling trust is a core requirement of effective interaction between people and AI agents. Thus, any decision-making framework designed to work with people must be able to estimate human trust. In this paper, we propose a mental model-based framework of trust that captures multidimensional aspects of trust and can be used to infer human trust. This framework can also be used as a foundation for any trust-aware decision-making framework. Through our mental model-based framework of trust, we propose a formal framework that captures intuitions about trust present in extant literature. We show how our framework captures various dimensions of trust perception and then use the framework to define trust evolution, human reliance, and decision-making. Additionally, we also propose a formalization of the appropriate level of trust in the agent.
Using human subject studies, we evaluate if (1) changes in trust can be achieved by adjusting human beliefs about the agent according to predictions made by our mental model framework and (2) adjustments in human belief lead to corresponding modifications in trust perceptions; performance, process, and purpose, by controlling different aspects of the model.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6
