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Related Concept Videos

Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Deductive Reasoning01:16

Deductive Reasoning

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 from inductive reasoning. It uses a general principle or law to predict specific results. From these general principles, a scientist can predict specific results that remain valid as long as the general principles are correct.For example, a researcher can make specific predictions from the hypothesis "butterflies are attracted...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Inductive Reasoning00:59

Inductive Reasoning

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...
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...

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Related Experiment Videos

DARE: An Explainable AI-visualization Framework for Ill-defined Decision Making.

Angelos Chatzimparmpas, Evanthia Dimara

    IEEE Transactions on Visualization and Computer Graphics
    |June 5, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces DARE, an explainable AI framework for complex decision-making. DARE enhances AI by incorporating deliberation, agency, resilience, and empathy, improving human-AI collaboration in uncertain environments.

    Related Experiment Videos

    Area of Science:

    • Artificial Intelligence
    • Human-Computer Interaction
    • Decision Science

    Background:

    • Real-world decision-making is often fluid, uncertain, and ill-defined, with shifting objectives and incomplete data.
    • Conventional AI and decision-support systems struggle with non-quantifiable factors like social values and ethics.
    • Existing systems fail to adequately address contexts with ill-defined problems and dynamic criteria.

    Purpose of the Study:

    • Introduce DARE, an explainable AI and visualization framework for complex decision-making.
    • Complement FAIR data principles with DARE principles: Deliberation, Agency, Resilience, and Empathy.
    • Support human-AI decision making in value-laden and ill-defined contexts.

    Main Methods:

    • Conceptualize decision making as an iterative alignment of human criteria with algorithmic representations.
    • Utilize weak supervision and concept-based modeling to connect human reasoning with interpretable model concepts.
    • Employ input visualization for capturing evolving, qualitative, and uncertain reasoning through interaction.

    Main Results:

    • DARE enables gradual emergence of decision structure through human-AI alignment.
    • Explainability arises from continuous visibility of co-developing human and algorithmic reasoning.
    • Uncertainty is managed as an inherent dimension of deliberation, not a bias.

    Conclusions:

    • DARE supports transparent, adaptable human-AI decision making grounded in human judgment.
    • The framework addresses limitations of conventional AI in fluid and ill-defined contexts.
    • DARE fosters a collaborative approach where human and algorithmic heuristics evolve through interaction.