Oct 2026· Current Opinion in Psychology· 43 references
AI and HR TechnologiesEthics and Social Impacts of AI
Abstract
As artificial intelligence (AI) reshapes the workplace, its impact on diversity, equity, and inclusion (DEI) has been examined primarily through the lens of what AI actually does (i.e., system design, outputs). This review shifts the focus to humans' subjective beliefs about AI, identifying mechanisms through which three dimensions of AI-beliefs (competence, warmth, and morality) may affect DEI goals: improving representation of underrepresented groups, closing career gaps across groups, and fostering inclusion. Our synthesis shows that, independently of what AI does, AI-beliefs can disadvantage underrepresented groups in some contexts, while creating opportunities for bridging human subgroups in others. This suggests that achieving DEI goals in AI-integrated workplaces requires attention not only to the properties and behavior of AI, but also to employees’ beliefs about AI itself.
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
