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Related Concept Videos

Inductive Reasoning00:59

Inductive Reasoning

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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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Heuristics01:21

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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Deductive Reasoning01:16

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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
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Distribution Reliability and Automation01:25

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Reason and Intuition01:37

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Cause and Effect01:53

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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Visual Analytics for Explainable and Trustworthy Artificial Intelligence.

Angelos Chatzimparmpas, Sumanta N Pattanaik

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    Summary
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    Visual analytics (VA) enhances trust in artificial intelligence (AI) systems by combining AI models with interactive visualizations. This approach allows experts to refine AI models, improving their reliability and adoption in critical applications like healthcare.

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    Area of Science:

    • Artificial Intelligence
    • Human-Computer Interaction
    • Data Visualization

    Background:

    • Intelligent systems are crucial for complex problem-solving, including medical diagnostics, yet their opacity hinders expert trust and adoption.
    • Lack of transparency in AI systems leads to challenges in reliability and integration into critical fields, despite AI's potential to improve outcomes and reduce economic burdens.
    • Visual analytics (VA) offers a method to integrate AI with interactive visualizations, enabling expert input and bridging the gap between AI and human understanding.

    Purpose of the Study:

    • To define, categorize, and explore how visual analytics (VA) solutions can foster trust in artificial intelligence (AI) systems.
    • To propose a design space for innovative visualizations that enhance AI transparency and usability.
    • To present an overview of developed VA dashboards supporting various stages of the AI pipeline.

    Main Methods:

    • Literature review and conceptual framework development for VA in AI.
    • Exploration of VA techniques for enhancing transparency and interpretability of AI models.
    • Development and presentation of VA dashboards for AI pipeline stages: data processing, feature engineering, hyperparameter tuning, model understanding, debugging, refining, and comparison.

    Main Results:

    • VA effectively bridges the gap between AI predictions and human expertise through interactive visualizations.
    • A design space for innovative VA solutions is proposed, offering a structured approach to developing trust-building tools.
    • Developed VA dashboards demonstrate practical application in supporting critical tasks throughout the AI lifecycle, from data preparation to model evaluation.

    Conclusions:

    • Visual analytics is a powerful approach to increase trust and facilitate the adoption of AI systems in various domains, particularly in healthcare.
    • Interactive visualizations empower domain experts to understand, refine, and validate AI models, leading to more reliable and trustworthy intelligent systems.
    • The proposed VA design space and demonstrated dashboards provide a foundation for future development of transparent and user-centric AI solutions.