Human-centered AI (HCAI), as a design philosophy, advocates prioritizing humans in designing, developing, and deploying intelligent systems, aiming to maximize the benefits of AI technology to humans and avoid its potential adverse effects. While HCAI has gained momentum, the lack of guidance on methodology in its implementation makes its adoption challenging. After assessing the needs for a methodological framework for HCAI, this paper first proposes a comprehensive and interdisciplinary HCAI methodological framework integrated with seven components, including design goals, design principles, implementation approaches, design paradigms, interdisciplinary teams, methods, and processes. THe implications of the framework are also discussed. This paper also presents a "three-layer" approach to facilitate the implementation of the framework. We believe the proposed framework is systematic and executable, which can overcome the weaknesses in current frameworks and the challenges currently faced in implementing HCAI. Thus, the framework can help put it into action to develop, transfer, and implement HCAI in practice, eventually enabling the design, development, and deployment of HCAI-based intelligent systems.

Research and application have used human-AI teaming (HAT) as a new paradigm to develop AI systems. HAT recognizes that AI will function as a teammate instead of simply a tool in collaboration with humans. Effective human-AI teams need to be capable of taking advantage of the unique abilities of both humans and AI while overcoming the known challenges and limitations of each member, augmenting human capabilities, and raising joint performance beyond that of either entity. The National AI Research and Strategic Plan 2023 update has recognized that research programs focusing primarily on the independent performance of AI systems generally fail to consider the functionality that AI must provide within the context of dynamic, adaptive, and collaborative teams and calls for further research on human-AI teaming and collaboration. However, there has been debate about whether AI can work as a teammate with humans. The primary concern is that adopting the "teaming" paradigm contradicts the human-centered AI (HCAI) approach, resulting in humans losing control of AI systems. This article further analyzes the HAT paradigm and the debates. Specifically, we elaborate on our proposed conceptual framework of human-AI joint cognitive systems (HAIJCS) and apply it to represent HAT under the HCAI umbrella. We believe that HAIJCS may help adopt HAI while enabling HCAI. The implications and future work for HAIJCS are also discussed. Insights: AI has led to the emergence of a new form of human-machine relationship: human-AI teaming (HAT), a paradigmatic shift in human-AI systems; We must follow a human-centered AI (HCAI) approach when applying HAT as a new design paradigm; We propose a conceptual framework of human-AI joint cognitive systems (HAIJCS) to represent and implement HAT for developing effective human-AI teaming

Human-centered AI (HCAI) is a design philosophy that advocates prioritizing humans in designing, developing, and deploying intelligent systems, aiming to maximize the benefits of AI to humans and avoid potential adverse impacts. While HCAI continues to influence, the lack of guidance on methodology in practice makes its adoption challenging. This paper proposes a comprehensive HCAI framework based on our previous work with integrated components, including design goals, design principles, implementation approaches, interdisciplinary teams, HCAI methods, and HCAI processes. This paper also presents a "three-layer" approach to facilitate the implementation of the framework. We believe this systematic and executable framework can overcome the weaknesses in current HCAI frameworks and the challenges currently faced in practice, putting it into action to enable HCAI further.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. All hell broke loose in the AI world after The Washington Post reported last week that a Google engineer thought that LaMDA, one of the company's large language models (LLM), was sentient. The news was followed by a frenzy of articles, videos and social media debates over whether current AI systems understand the world as we do, whether AI systems can be conscious, what are the requirements for consciousness, etc. We are currently in a state where our large language models have become good enough to convince many people -- including engineers -- that they are on par with natural intelligence. At the same time, they are still bad enough to make dumb mistakes, as these experiments by computer scientist Ernest Davis show.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. There are two contrasting but equally disturbing images of artificial intelligence. One warns about a future in which runaway intelligence becomes smarter than humanity, creates mass unemployment, and enslaves humans in a Matrix-like world or destroys them a la Skynet. A more contemporary image is one in which dumb AI algorithms are entrusted with sensitive decisions that can cause severe harm when they do go wrong. What both visions have in common is the absence of human control.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. There are two contrasting but equally disturbing images of artificial intelligence. One warns about a future in which runaway intelligence becomes smarter than humanity, creates mass unemployment, and enslaves humans in a Matrix-like world or destroys them a la Skynet. A more contemporary image is one in which dumb AI algorithms are entrusted with sensitive decisions that can cause severe harm when they do go wrong. What both visions have in common is the absence of human control.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. There are two contrasting but equally disturbing images of artificial intelligence. One warns about a future in which runaway intelligence becomes smarter than humanity, creates mass unemployment, and enslaves humans in a Matrix-like world or destroys them a la Skynet. A more contemporary image is one in which dumb AI algorithms are entrusted with sensitive decisions that can cause severe harm when they do go wrong. What both visions have in common is the absence of human control.

Gartner analysts predict that numerous AI initiatives will move quickly from the first stage of the hype cycle to the final one over the next two to five years. Users want more than artificial intelligence can provide at the moment but those capabilities are changing fast, according to Gartner's Hype Cycle for Artificial Intelligence 2021 report. Gartner analysts described 34 types of AI technologies in the report and also noted that the AI hype cycle is more fast-paced, with an above-average number of innovations reaching mainstream adoption within two to five years. Gartner analysts found more innovations in the innovation trigger phase of the hype cycle than usual. That means that end users are looking for specific technology capabilities that current AI tools can't quite deliver yet.