Human-Centered Artificial Intelligence and Machine Learning
This work tackles the problem of integrating human factors into AI design for broader societal impact, but it is incremental as it builds on existing discussions without introducing new methods or data.
The paper addresses the need for AI and ML systems to be designed with human considerations, proposing a human-centered perspective that focuses on systems understanding humans socioculturally and helping humans understand them, while highlighting issues like fairness and accountability.
Humans are increasingly coming into contact with artificial intelligence and machine learning systems. Human-centered artificial intelligence is a perspective on AI and ML that algorithms must be designed with awareness that they are part of a larger system consisting of humans. We lay forth an argument that human-centered artificial intelligence can be broken down into two aspects: (1) AI systems that understand humans from a sociocultural perspective, and (2) AI systems that help humans understand them. We further argue that issues of social responsibility such as fairness, accountability, interpretability, and transparency.