Signal Detection Theory: Understanding Bias

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Title: Signal Detection Theory: Understanding Bias

Description:

Signal detection theory (SDT) can be used to evaluate performance and bias in decision making by combinations of humans and computers. SDT provides a set of techniques to measure the success of a system, such as a machine learning algorithm or human intelligence analyst, that is trying to detect a signal or potential threat in the presence of noise, or all possible signals. SDT can also measure bias or systemic errors in judgement that could have positive or negative consequences. Consider how SDT could be applied to understanding the performance of algorithmic and human intelligence analysts. To reach a conclusion, review open source tools and examples.  

Type: All Access Education

Target Audience Experience Level: Advanced

Industry Application: Industry Agnostic

Globally Focused Session: Globally Applicable

Learning Objective #1: Understand the signal detection framework.

Learning Objective #2: Learn how signal detection could be applied to understanding the performance of algorithmic and human intelligence analysts.

Learning Objective #3: Review calculation methods and tools for measuring decision making outcomes and biases.


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