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Risk Management and Healthcare Policy. 17: 1339– 1348. doi: 10.2147/RMHP.S461562. Liu, Feng; Ju, Qianqian; Zheng, Qijian; Peng, Yujia (2024). "Artificial intelligence in mental health: innovations brought by artificial intelligence techniques in stress detection and interventions of building resilience". Current Opinion in Behavioral Sciences.
Marvin Minsky et al. raised the issue that AI can function as a form of surveillance, with the biases inherent in surveillance, suggesting HI (Humanistic Intelligence) as a way to create a more fair and balanced "human-in-the-loop" AI. [61] Explainable AI has been recently a new topic researched amongst the context of modern deep learning.
Human feedback is commonly collected by prompting humans to rank instances of the agent's behavior. [15] [17] [18] These rankings can then be used to score outputs, for example, using the Elo rating system, which is an algorithm for calculating the relative skill levels of players in a game based only on the outcome of each game. [3]
Artificial intelligence utilises massive amounts of data to help with predicting illness, prevention, and diagnosis, as well as patient monitoring. In obstetrics, artificial intelligence is utilized in magnetic resonance imaging, ultrasound, and foetal cardiotocography. AI contributes in the resolution of a variety of obstetrical diagnostic issues.
Algorithms, particularly those utilizing machine learning methods or artificial intelligence (AI), play a growing role in decision-making across various fields. Examples include recommender systems in e-commerce for identifying products a customer might like and AI systems in healthcare that assist in diagnoses and treatment decisions. Despite ...
Automated decision-making involves using data as input to be analyzed within a process, model, or algorithm or for learning and generating new models. [7] ADM systems may use and connect a wide range of data types and sources depending on the goals and contexts of the system, for example, sensor data for self-driving cars and robotics, identity data for security systems, demographic and ...
Knowledge-representation is a field of artificial intelligence that focuses on designing computer representations that capture information about the world that can be used for solving complex problems. The justification for knowledge representation is that conventional procedural code is not the best formalism to use to solve complex problems.
This has led to advocacy and in some jurisdictions legal requirements for explainable artificial intelligence. [69] Explainable artificial intelligence encompasses both explainability and interpretability, with explainability relating to summarizing neural network behavior and building user confidence, while interpretability is defined as the ...