Bias Detective: What Went Wrong?

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Machine learning systems do not intentionally discriminate, but they can reflect patterns and problems present in the data they learn from. In this activity, you will analyze real-world scenarios and identify what may have gone wrong. Think carefully about whether the issue is related to biased data, poor data preparation, flawed design, or something else entirely.

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International American Institute

Ecuador

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Bias Detective: What Went Wrong?Versión en línea Machine learning systems do not intentionally discriminate, but they can reflect patterns and problems present in the data they learn from. In this activity, you will analyze real-world scenarios and identify what may have gone wrong. Think carefully about whether the issue is related to biased data, poor data preparation, flawed design, or something else entirely. por International American Institute 1 A facial recognition system performs well on lighter-skinned faces but frequently misidentifies darker-skinned individuals. a Sampling bias in the training data b Poor data cleaning c Reinforcement learning error d Incorrect file formatting 2 A student performance prediction model uses grade records that contain many missing values and duplicated entries. a Sampling bias b Poor data cleaning and preprocessing c Overfitting d Reinforcement learning feedback 3 A loan approval system denies more applications from a specific neighborhood because historical data shows higher default rates there. a Hardware malfunction b Algorithm randomly selecting applicants c Historical bias reflected in the data d Incorrect programming syntax 4 A recommendation system repeatedly suggests similar content, making it harder for users to discover new topics. a Sampling bias b Feedback loop bias c Missing values d Incorrect file type 5 A health prediction model was trained mostly on data from adults but is used to predict conditions in teenagers. a Overfitting b Underfitting c Mismatched training data and real-world use d Database failure Feedback 1 This problem likely occurred because the training dataset did not include enough diverse examples. When certain groups are underrepresented, the model performs poorly on those groups. This is a classic example of sampling bias. 2 Missing and duplicated data can distort patterns the model learns. Without proper cleaning and preparation, the model may draw inaccurate conclusions from incomplete or messy data. 3 The model is learning patterns from past data. If historical decisions were influenced by inequality, the model may reproduce those patterns. This is an example of bias embedded in historical data. 4 When users interact mostly with similar content, the system continues reinforcing those patterns. This creates a feedback loop that limits diversity in recommendations. 5 If the training data does not represent the population where the model is applied, performance may be unreliable. The model lacks relevant examples for teenagers.

Bias Detective: What Went Wrong?Versión en línea

por International American Institute

A facial recognition system performs well on lighter-skinned faces but frequently misidentifies darker-skinned individuals.

Sampling bias in the training data

Poor data cleaning

Reinforcement learning error

Incorrect file formatting

A student performance prediction model uses grade records that contain many missing values and duplicated entries.

Sampling bias

Poor data cleaning and preprocessing

Overfitting

Reinforcement learning feedback

A loan approval system denies more applications from a specific neighborhood because historical data shows higher default rates there.

Hardware malfunction

Algorithm randomly selecting applicants

Historical bias reflected in the data

Incorrect programming syntax

A recommendation system repeatedly suggests similar content, making it harder for users to discover new topics.

Sampling bias

Feedback loop bias

Missing values

Incorrect file type

A health prediction model was trained mostly on data from adults but is used to predict conditions in teenagers.

Overfitting

Underfitting

Mismatched training data and real-world use

Database failure

Feedback

This problem likely occurred because the training dataset did not include enough diverse examples. When certain groups are underrepresented, the model performs poorly on those groups. This is a classic example of sampling bias.

Missing and duplicated data can distort patterns the model learns. Without proper cleaning and preparation, the model may draw inaccurate conclusions from incomplete or messy data.

The model is learning patterns from past data. If historical decisions were influenced by inequality, the model may reproduce those patterns. This is an example of bias embedded in historical data.

When users interact mostly with similar content, the system continues reinforcing those patterns. This creates a feedback loop that limits diversity in recommendations.

If the training data does not represent the population where the model is applied, performance may be unreliable. The model lacks relevant examples for teenagers.

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Bias Detective: What Went Wrong?Versión en línea

por International American Institute

A facial recognition system performs well on lighter-skinned faces but frequently misidentifies darker-skinned individuals.

A student performance prediction model uses grade records that contain many missing values and duplicated entries.

A loan approval system denies more applications from a specific neighborhood because historical data shows higher default rates there.

A recommendation system repeatedly suggests similar content, making it harder for users to discover new topics.

A health prediction model was trained mostly on data from adults but is used to predict conditions in teenagers.

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