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AI researchers have argued that AI technologies could also be used to help decision-making. Researchers and practitioners have launched within the 2020s the time period and idea of AI guardrails to explain different frameworks or tools designed to ensure that AI methods remain protected, as well as aligned with moral or legal expectations. Some scholars have urged that this framework falls short. Deontological ethics, which emphasizes adherence to moral guidelines, has been proposed as a framework for aligning AI techniques with human values. AI safety analysis has additionally examined defensive strategies for protecting machine studying techniques from information poisoning assaults throughout coaching. These frameworks are often built-in into AI techniques to enhance security and reliability. AI security evaluations can broadly be categorized as both testing the direct output of the model (model security evaluations), or analyzing how access to the model can impact human conduct (contextual security evaluations). Evaluations can assist determine both the chance posed by the raw model outputs, as properly as the potential actual-world outcomes of deploying such models. AI safety evaluations (often referred to as "evals") are exams designed by researchers to judge the risk posed by a given artificial intelligence mo


↑ Buchanan, Ben; Bansemer, John; Cary, Dakota; Lucas, Jack; Musser, Micah (2020). "[Automating Cyber](http://jinhon-info.com.tw3000/gladyssherriff/7340568/wiki/Technology-News-Iphone-Apps) Assaults: Hype and Actuality". ↑ Räuker, Tilman; Ho, Anson; Casper, Stephen; Hadfield-Menell, Dylan (2022-09-05). "Towards Clear AI: A Survey on Interpreting [The UK Shopping Network](https://danskemassagepiger.dk/author-profile/lucillecoolidg/) Inside Constructions of Deep Neural Networks". ↑ Bengio, Yoshua; Privitera, Daniel; Bommasani, Rishi; Casper, Stephen; Goldfarb, Danielle; Mavroudis, Vasilios; Khalatbari, Leila; Mazeika, Mantas; Hoda, Heidari (2024-05-17). "International Scientific Report on the Safety of Advanced AI" (PDF). ↑ Hendrycks, Dan; Mazeika, Mantas; Dietterich, Thomas (2019-01-28). "Deep Anomaly Detection with Outlier Exposure". 1 2 Hendrycks, Dan; Mazeika, Mantas (2022-09-20). "X-Threat Analysis for AI Research". ↑ Hendrycks, Dan; Gimpel, Kevin (2018-10-03). "A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks". ↑ Urbina, Fabio; Lentzos, Filippa; Invernizzi, Cédric; Ekins, Sean (2022). "Dual use of synthetic-intelligence-powered drug discovery". 1 2 Ngo, Richard; Chan, Lawrence; Mindermann, Sören (2022). "The Alignment Downside from a Deep Learning Perspective". ↑ Meng, Kevin; Bau, David; Andonian, Alex; Belinkov, Yonatan (2022). "Locating and editing factual associations in GPT". ↑ Goodfellow, Ian; Papernot, Nicolas; Huang, Sandy; Duan, Rocky; Abbeel, Pieter; Clark, Jack (2017-02-24). "Attacking Machine Learning with Adversarial Examples". ↑ Sheatsley, Ryan; Papernot, Nicolas; Weisman, Michael; Verma, Gunjan; McDaniel, Patrick (2022-09-09). "Adversarial Examples in Constrained Domai


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