Sep 2026· Cambridge University Press eBooks· pp. 15-22· 12 references
Abstract
A question that arises when contemplating artificial intelligence and empathy is whether it would matter to the empathy recipient if AI genuinely feels or cares. This is a seemingly novel issue, born of the capacity of computers to simulate human empathy. I will suggest, however, that questions concerning “artificial empathy” should not be limited to AI chatbots, as such forms of empathy that are void of feeling are widespread in human relationships as well. This chapter offers a short history of artificial empathy and how it became widely accepted, well before AI, beginning with the operationalization of empathy in mid-twentieth-century clinical psychology. Evaluations of AI’s empathetic abilities are often based on comparisons to human or “real” empathy – a romanticized form of empathy that is, sadly, less common than we appreciate. This chapter offers an alternative perspective that might prove valuable, comparing AI’s “artificial empathy” to human “artificial empathy.”
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
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...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
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.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
