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Decision Trees vs Artificial Intelligence

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A decision tree is a tree-like graph that can be used as an algorithm to automate decision making. Decision trees are considered human-readable. As such, they are compatible with human driven processes such as governance, ethics, law, audits and critical analysis.
Artificial intelligence is another common method of automating decisions using sophisticated tools that learn and self-improve. AI models of decision making can be based on decision trees. However, more typically they are based on statistical models that aren't easily human-readable.
It is challenging to govern and manage AI decisions that can't be represented in human-readable form. Additionally, the self-improving nature of AI means that decision criteria change with time making them all the more difficult for humans to evaluate.
Generally speaking, predefined decision trees are compatible with business and legal processes. Artificial intelligence is often difficult to incorporate into processes that require human judgment.
Overview: Decision Trees vs Artificial Intelligence
Decision Tree Definition
A tree-shaped graph used to determine an action or illustrate a statistical probability.
Artificial Intelligence Definition
Software that learns and self-improves often using statistical models that aren't easy for humans to intuitively understand.
Decision trees integrate well with human-driven processes.
AI decisions are potentially more optimized and dynamic as compared with predefined decision trees.
AI is generally difficult for humans to govern and control due to its self-improving nature.
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Artificial Intelligence

This is the complete list of articles we have written about artificial intelligence.
Affective Computing
Artificial Knowledge
Artificial Stupidity
Chinese Room
Decision Trees
Deep Learning
Deep Magic
Ensemble Learning
Event Processing
Forward Chaining
Fuzzy Logic
IT Biases
IT Examples
Machine Biases
Machine Unlearning
Predictive Analytics
Sentiment Analysis
Swarm Intelligence
Turing Test
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