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When Artificial Intelligence Fails

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Technology such as algorithms or artificial intelligence are often portrayed as logical entities that completely eliminate the errors in thinking that are common to humans such as biases. This is magical thinking that doesn't reflect the reality of computing. The following are a few examples of how computing produces illogical and irrational results.

Machine Biases

Artificial intelligence learns and self-improves. For example, a machine may build statistical models that can be used to make predictions. If you show an artificial intelligence a movie in which all the white people are bankers, the machine may develop a model that predicts most white people are bankers. As with human learning, artificial intelligence can develop biases based on factors such as data that is used in training.

Machine Learning

Machine learning is by no means an exact science nor is it likely to become an exact science because learning is based on broad philosophical questions. Artificial intelligence produces vastly different results depending on the learning algorithm that is used. In other words, machines will disagree just as humans do.

Committee Machines

In order to compensate for differences in machine learning algorithms, designers of artificial intelligence may use techniques such as committee machines that aggregate results from multiple learners. This is similar to social processes in humans and may open up the potential for the machine equivalent of social biases such as the abilene paradox.


Both algorithms and artificial intelligence are commonly based on heuristics that come up with a good guess as opposed to an answer that is definitely correct. This is much how humans think and isn't anywhere close to purely logical.

Fuzzy Logic

If you did try to build an artificial intelligence based on traditional true/false logic that can be shown to be correct, such a machine would be incredibly unintelligent. Real life has grey areas between true and false that humans commonly understand. Machine techniques such as fuzzy logic can also handle grey areas. It order to account for grey areas you have to give up certainty whereby logic is "correct."


Artificial intelligence is often based on statistical models that aren't very good at explaining outliers in data. In other words, machines may stumble on obscure exceptions. Human abilities such as creativity and synthesis currently outdo machines in handling special cases and extreme situations such as black swan events.

Biases as Complexity

Bacteria don't have as many cognitive biases as humans. Although biases are commonly viewed as unintelligent they occur because humans are extremely complex thinkers. It is possible that an advanced artificial intelligence would have far greater problems with biases than humans. Such biases would be very difficult to understand and might require a new field of practice such as machine psychiatry.

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
More ...
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A few logic terms explained.

Law Of Excluded Middle

A classical law of logic first established by Aristotle.

Fuzzy Logic

Logic that allows for partial truths.

Logic vs Intelligence

The difference between logic and intelligence.


The definition of causality with examples.

Magical Thinking

The definition of magical thinking with examples.


The definition of scientism with examples.


A list of logical fallacies.

Mutually Exclusive

The definition of mutually exclusive with examples.

False Balance

The definition of false balance with examples.

Types Of Artificial Intelligence

A few common types of artificial intelligence.

Technological Singularity

Technological singularity explained.

Affective Computing

Artificial intelligence and emotion.

Artificial Life

An overview of artificial life.

Deep Learning

A definition of deep learning with examples.

Supervised Learning vs Unsupervised Learning

The difference between supervised and unsupervised learning with an example.

Natural Language Processing

The common types of natural language processing.

Autonomous Systems

Common types of autonomous systems.

Artificial Intelligence Examples

Common examples of artificial intelligence.
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