Humans increasingly encounter artificial intelligence (AI) and machine learning (ML) systems. Human-centered AI and ML require algorithms to be designed with humans in mind. We argue that human-centered AI has two parts: AI systems that understand humans from a socio-cultural perspective and AI systems that help humans understand them. Further, fairness, accountability, interpretability, and transparency are social responsibility issues.
- AI is the study and design of algorithms that perform tasks or behaviors that a reasonable person would consider intelligent if performed by a human.
- Machine learning (ML) adapts intelligent systems’ behavior based on data.
- Human-centered AI also recognizes that intelligent systems cannot see humans.
Artificial intelligence (AI) is the study and design of algorithms that execute tasks or behaviors that a reasonable person would consider requiring intelligence if performed by a human (Riedl, 2019). A system designed to be indistinguishable from humans; a speech assistant such as Alexa, Siri, Cortana, or Google Assistant; a self-driving car; a recommender in an online commerce site; or a non-player character in a video game are all examples of intelligent systems (Nijholt, 2020). Intelligent systems are called ‘agents’ when they can decide based on their objectives. Machine learning (ML) is a method for designing intelligent systems in which the system’s behavior is adapted based on data. The success of ML algorithms has contributed to the recent expansion of AI’s commercialization (Injadat et al., 2021).