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Human–robot interaction is a multidisciplinary field with contributions from human–computer interaction, artificial intelligence, robotics, natural language processing, design, psychology and philosophy. A subfield known as physical human–robot interaction (pHRI) has tended to focus on device design to enable people to safely interact ...
Its quality and consistency may vary depending on the task, interface, and the preferences and biases of individual humans. [15] [39] The effectiveness of RLHF depends on the quality of human feedback. For instance, the model may become biased, favoring certain groups over others, if the feedback lacks impartiality, is inconsistent, or is ...
Form function attribution bias In human–robot interaction, the tendency of people to make systematic errors when interacting with a robot. People may base their expectations and perceptions of a robot on its appearance (form) and attribute functions which do not necessarily mirror the true functions of the robot. [96] Fundamental pain bias
Automation bias is the propensity for humans to favor suggestions from automated decision-making systems and to ignore contradictory information made without automation, even if it is correct. [1] Automation bias stems from the social psychology literature that found a bias in human-human interaction that showed that people assign more positive ...
Robot ethics intersect with the ethics of AI. Robots are physical machines whereas AI can be only software. [16] Not all robots function through AI systems and not all AI systems are robots. Robot ethics considers how machines may be used to harm or benefit humans, their impact on individual autonomy, and their effects on social justice.
Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions made by such models after a learning process may be considered unfair if they were based on variables considered sensitive (e.g., gender, ethnicity, sexual orientation, or disability).
The robot may begin with certainty that it is at position (0,0). However, as it moves further and further from its original position, the robot has continuously less certainty about its position; using a Bayes filter, a probability can be assigned to the robot's belief about its current position, and that probability can be continuously updated ...
A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute found that "over [a] 60-year time frame there is a strong bias towards predicting the arrival of human-level AI as between 15 and 25 years from the time the prediction was made". They analyzed 95 predictions made between 1950 and 2012 on when human-level ...