Machine learning (ML) models are not inherently objective. ML practitioners train models by feeding them a dataset of training examples, and human involvement in the provision and curation of this data can make a model's predictions susceptible to bias.
When building models, it's important to be aware of common human biases that can manifest in your data, so you can take proactive steps to mitigate their effects.
Reporting bias
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Definition
Reporting bias occurs when the frequency of events, properties, and/or outcomes captured in a dataset does not accurately reflect their real-world frequency. This bias can arise because people tend to focus on documenting circumstances that are unusual or especially memorable, assuming that the ordinary does not need to be recorded.
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Example
A sentiment-analysis model is trained to predict whether book reviews are positive or negative based on a corpus of user submissions to a popular website. The majority of reviews in the training dataset reflect extreme opinions (reviewers who either loved or hated a book), because people were less likely to submit a review of a book if they did not respond to it strongly. As a result, the model is less able to correctly predict sentiment of reviews that use more subtle language to describe a book.
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Historical bias
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Definition
Historical bias occurs when historical data reflects inequities that existed in the world at that time.
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Example
A city housing dataset from the 1960s contains home-price data that reflects discriminatory lending practices in effect during that decade.
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Automation bias
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Definition
Automation bias is a tendency to favor results generated by automated systems over those generated by non-automated systems, irrespective of the error rates of each.
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Example
ML practitioners working for a sprocket manufacturer were eager to deploy the new "groundbreaking" model they trained to identify tooth defects, until the factory supervisor pointed out that the model's precision and recall rates were both 15% lower than those of human inspectors.
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Selection bias
Selection bias occurs if a dataset's examples are chosen in a way that is not reflective of their real-world distribution. Selection bias can take many different forms, including coverage bias, non-response bias, and sampling bias.
Coverage bias
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Definition
Coverage bias occurs if data is not selected in a representative fashion.
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Example
A model is trained to predict future sales of a new product based on phone surveys conducted with a sample of consumers who bought the product. Consumers who instead opted to buy a competing product were not surveyed, and as a result, this group of people was not represented in the training data.
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Non-Response bias
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Definition
Non-response bias (also knows as participation bias) occurs if data ends up being unrepresentative due to participation gaps in the data-collection process.
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Example
A model is trained to predict future sales of a new product based on phone surveys conducted with a sample of consumers who bought the product and with a sample of consumers who bought a competing product. Consumers who bought the competing product were 80% more likely to refuse to complete the survey, and their data was underrepresented in the sample.
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Sampling bias
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Definition
Sampling bias occurs if proper randomization is not used during data collection.
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Example
A model is trained to predict future sales of a new product based on phone surveys conducted with a sample of consumers who bought the product and with a sample of consumers who bought a competing product. Instead of randomly targeting consumers, the surveyor chose the first 200 consumers that responded to an email, who might have been more enthusiastic about the product than average purchasers.
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Group attribution bias
Group attribution bias is a tendency to generalize what is true of individuals to the entire group to they belong to. Group attribution bias often manifests in the two following forms.
In-group bias
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Definition
In-group bias is a preference for members of your own group you also belong, or for characteristics that you also share.
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Example
Two ML practitioners training a résumé-screening model for software developers are predisposed to believe that applicants who attended the same computer-science academy as they both did are more qualified for the role.
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Out-group homogeneity bias
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Definition
Out-group homogeneity bias is a tendency to stereotype individual members of a group to which you do not belong, or to see their characteristics as more uniform.
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Example
Two ML practitioners training a résumé-screening model for software developers are predisposed to believe that all applicants who did not attend a computer-science academy don't have sufficient expertise for the role.
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Implicit Bias
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Definition
Implicit bias occurs when assumptions are made based on one's own model of thinking and personal experiences that don't necessarily apply more generally.
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Example
An ML practitioner training a gesture-recognition model uses a head shake as a feature to indicate a person is communicating the word "no." However, in some regions of the world, a head shake actually signifies "yes."
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Confirmation bias
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Definition
Confirmation bias occurs when model builders unconsciously process data in ways that affirm pre-existing beliefs and hypotheses.
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Example
An ML practitioner is building a model that predicts aggressiveness in dogs based on a variety of features (height, weight, breed, environment). The practitioner had an unpleasant encounter with a hyperactive toy poodle as a child, and ever since has associated the breed with aggression. When curating the model's training data, the practitioner unconsciously discarded features that provided evidence of docility in smaller dogs.
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Experimenter's bias
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Definition
Experimenter's bias occurs when a model builder keeps training a model until it produces a result that aligns with their original hypothesis.
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Example
An ML practitioner is building a model that predicts aggressiveness in dogs based on a variety of features (height, weight, breed, environment). The practitioner had an unpleasant encounter with a hyperactive toy poodle as a child, and ever since has associated the breed with aggression. When the trained model predicted most toy poodles to be relatively docile, the practitioner retrained the model several more times until it produced a result showing smaller poodles to be more violent.
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