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Related Concept Videos

Heuristics01:21

Heuristics

154
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.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
154
Decision Making01:20

Decision Making

253
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...
253
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

204
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
204
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.2K
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...
4.2K
Reason and Intuition01:37

Reason and Intuition

6.9K
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...
6.9K
Law of Effect01:06

Law of Effect

1.7K
B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
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Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
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Rethinking Administrative Law for Algorithmic Decision Making.

Rebecca Williams

    Oxford Journal of Legal Studies
    |May 26, 2022
    PubMed
    Summary

    Public authorities increasingly use algorithmic decision-making (ADM). Administrative law must adapt to address challenges like opacity and errors, ensuring optimal and ethical ADM implementation.

    Area of Science:

    • Administrative Law
    • Algorithmic Decision-Making (ADM)
    • Information Governance

    Background:

    • Public authorities extensively utilize Algorithmic Decision-Making (ADM).
    • ADM presents significant challenges to administrative law, including technical evaluation metrics, system opacity, error scalability, and the distinction between correlation and causation.
    • Existing administrative law frameworks may be insufficient to govern ADM effectively.

    Purpose of the Study:

    • To explore the challenges Algorithmic Decision-Making (ADM) poses to administrative law.
    • To identify necessary advancements in administrative law to effectively guide the optimal use of ADM by public authorities.
    • To examine the potential of administrative law to support regulatory bodies and clarify key legal concepts within data protection.

    Main Methods:

    Keywords:
    algorithmic decision makingartificial intelligencejudicial reviewmachine learning

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    • Conceptual analysis of administrative law principles in the context of ADM.
    • Examination of technical challenges associated with ADM systems (e.g., metrics, opacity, error propagation).
    • Review of relevant legal concepts within the General Data Protection Regulation (GDPR).

    Main Results:

    • ADM introduces complex technical and legal challenges for administrative law.
    • Administrative law requires significant adaptation to effectively regulate ADM.
    • Enhanced administrative law can support regulatory oversight and clarify GDPR concepts like 'meaningful information' and 'proportionality'.

    Conclusions:

    • Administrative law must evolve to meet the demands of Algorithmic Decision-Making (ADM).
    • Adaptation of administrative law can ensure the responsible and effective use of ADM by public bodies.
    • A more nuanced administrative law can aid regulatory bodies like the Information Commissioner's Office and clarify GDPR interpretation.