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  2. Algorithmic bias - Wikipedia

    en.wikipedia.org/wiki/Algorithmic_bias

    Bias can emerge from many factors, including but not limited to the design of the algorithm or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected or used to train the algorithm. [2] For example, algorithmic bias has been observed in search engine results and social media platforms.

  3. Artificial Unintelligence: How Computers Misunderstand the ...

    en.wikipedia.org/wiki/Artificial_Unintelligence:...

    Her research focuses on the role of artificial intelligence in journalism. Broussard has published features and essays in many outlets including The Atlantic, Harper’s Magazine, and Slate Magazine. Broussard has published a wide range of books examining the intersection of technology and social practice.

  4. Boosting (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Boosting_(machine_learning)

    Compared with binary categorization, multi-class categorization looks for common features that can be shared across the categories at the same time. They turn to be more generic edge like features. During learning, the detectors for each category can be trained jointly.

  5. Ethics of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Ethics_of_artificial...

    On June 26, 2019, the European Commission High-Level Expert Group on Artificial Intelligence (AI HLEG) published its "Policy and investment recommendations for trustworthy Artificial Intelligence". [78] This is the AI HLEG's second deliverable, after the April 2019 publication of the "Ethics Guidelines for Trustworthy AI".

  6. Fairness (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Fairness_(machine_learning)

    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).

  7. Bias–variance tradeoff - Wikipedia

    en.wikipedia.org/wiki/Bias–variance_tradeoff

    linear and Generalized linear models can be regularized to decrease their variance at the cost of increasing their bias. [11] In artificial neural networks, the variance increases and the bias decreases as the number of hidden units increase, [12] although this classical assumption has been the subject of recent debate. [4]

  8. Automated planning and scheduling - Wikipedia

    en.wikipedia.org/wiki/Automated_planning_and...

    Automated planning and scheduling, sometimes denoted as simply AI planning, [1] is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles.

  9. AI capability control - Wikipedia

    en.wikipedia.org/wiki/AI_capability_control

    The AI-box experiment is an informal experiment devised by Eliezer Yudkowsky to attempt to demonstrate that a suitably advanced artificial intelligence can either convince, or perhaps even trick or coerce, a human being into voluntarily "releasing" it, using only text-based communication.