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Studying how people perceive and interact with AI across speech, vision, text, and gesture to build trustworthy systems.
Exploring how humans and AI systems reason differently and what this means for collaboration and decision-making.
Investigating how AI can align with diverse human values, preferences, and cultural contexts in areas without clear-cut answers.
Centering the people most affected by AI in the design process, especially communities traditionally excluded from tech development.
A quick guide to key terms shaping ARIA's work at the intersection of AI and human experience.
The challenge of ensuring that AI systems behave in ways consistent with human values, intentions, and expectations—particularly in domains where those values are subjective or contested.
The development of computer systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language understanding.
The interdisciplinary study of the mind and its processes, drawing on psychology, neuroscience, linguistics, philosophy, and computer science.
The degree to which the internal workings or outputs of an AI system can be understood by a human. Also called interpretability or transparency.
The study of how humans and robots communicate, collaborate, and coexist, with attention to social, cognitive, and physical dimensions of their interactions.
A subset of AI in which systems learn from data and improve their performance on specific tasks without being explicitly programmed for each case.
Communication between humans and AI systems that involves multiple channels or modes, such as speech, text, gesture, vision, and touch.
A field of AI focused on enabling computers to understand, interpret, and generate human language in useful ways.
A design approach that actively involves the people who will be affected by a system in the process of designing it, ensuring their needs, values, and perspectives shape the outcome.
The process by which a person adjusts their level of trust in an AI system based on its performance, transparency, and reliability over time.