Human Centered
Human-centered AI (HCAI) prioritizes human needs and values in the design, development, and deployment of AI systems. Current research focuses on improving AI explainability through methods like weight of evidence and counterfactual explanations, assessing AI's human-likeness in language and behavior using benchmarks informed by psycholinguistic principles and human feedback, and mitigating biases in AI models, particularly those stemming from Western-centric training data. This emphasis on human-centered design aims to enhance trust, transparency, and ultimately, the beneficial integration of AI into various applications, from healthcare and manufacturing to legal research and everyday tools.
Papers
Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI
Emily Jin, Jiaheng Hu, Zhuoyi Huang, Ruohan Zhang, Jiajun Wu, Li Fei-Fei, Roberto Martín-Martín
Exploring Counterfactual Alignment Loss towards Human-centered AI
Mingzhou Liu, Xinwei Sun, Ching-Wen Lee, Yu Qiao, Yizhou Wang
Explainable AI And Visual Reasoning: Insights From Radiology
Robert Kaufman, David Kirsh
ChatGPT: More than a Weapon of Mass Deception, Ethical challenges and responses from the Human-Centered Artificial Intelligence (HCAI) perspective
Alejo Jose G. Sison, Marco Tulio Daza, Roberto Gozalo-Brizuela, Eduardo C. Garrido-Merchán