What is bias in machine learning

Artificial Intelligence

We all have biases. They might be unconscious, but they’re there nonetheless. And while they might not always be bad, they can sometimes lead to unintended consequences in our lives and work. In this blog post, we will explore what bias is and what it means for the field of machine learning. We’ll also look at ways to identify and overcome our own biases so that we can make better decisions in the future.

What is bias in machine learning?

Bias in machine learning is a term that you can use it to describe the ways that artificial intelligence (AI) systems can be unintentionally discriminatory. Geoffrey Hinton, widely considered one of the fathers of AI, first coined the term. Different types of bias exist in machine learning.

All of them arise because designers program AI systems to learn solely from data and not from humans. Consequently, these systems can show bias towards specific data and interpretations.

The three main types of bias in machine learning are:

1) Selection bias: This occurs when the AI system chooses which data to focus on and ignores other data that may be relevant. This can lead to inaccurate predictions based on the chosen data.

2) Appraisal bias: This occurs when the AI system evaluates data in a biased way, often favouring information that it has been explicitly taught to recognise as being important. Valuable data can lead to inaccurate predictions or decisions.

3) Generalisation bias: This happens when the AI system makes assumptions about how things will behave based on limited experience or knowledge. These assumptions can lead to incorrect predictions or decisions if they are used in future situations where those assumptions may not hold true.

Types of bias in machine learning

Machine learning algorithms are often accused of exhibiting bias. What does this mean and why is it a problem?

In the simplest terms, bias in machine learning refers to any systematic deviation from neutral predictions caused by the algorithm itself. This might manifest as over- or under-fitting. The algorithm ‘forgets’ how particular features relate to prediction success or failure, respectively. In practice, this means that certain classes of data (e.g. those belonging to a certain group or those with certain features) will tend to be more accurately predictors of future outcomes than others. Why is this problematic?

First and foremost, incorrect predictions can arise. For instance, if designers program an algorithm to predict cancer rates and it mistakenly gives low predictions for people with dark skin color, it would be erroneous and could potentially endanger them.

Secondly, biased predictions can have a negative impact on individual users and groups of users alike. For example, if an algorithm predicts that certain people have a high chance of developing diabetes, it might unfairly target those already at risk, worsening their disease symptoms. Machine learning circles refer to this type of discrimination as ‘heterosexism’ or ‘racism’, and numerous studies have illustrated it.

Lastly, biased algorithms might prioritize specific types of data to enhance performance, a practice sometimes termed ‘data scaling’. This can have unforeseen consequences because different types of data tend to behave


Bias in machine learning refers to the unintended effects that can occur when a computer is making decisions based on data. These effects can dramatically reduce the accuracy and usefulness of predictions that a machine learning algorithm makes. It can even cause it to draw incorrect conclusions. In order to avoid bias, you need to understand how it works and take steps to reduce its impact. By doing so, you will be able to harness the power of machine learning while minimizing its potential flaws.


1. What is bias in machine learning?

Bias in machine learning refers to systematic errors in the algorithm’s predictions or decisions caused by incorrect assumptions in the learning process. These errors can result from the data used, the model chosen, or the training process itself, leading to skewed results that do not accurately reflect the real-world scenario.

Example: If a facial recognition system is trained primarily on images of people with lighter skin tones, it may perform poorly when recognizing individuals with darker skin tones, demonstrating a bias in the training data.

2. What are the different types of bias in machine learning?

There are several types of bias in machine learning, including:

  • Selection Bias: Occurs when the training data is not representative of the real-world population, leading to skewed predictions. Example: Training a model to predict customer preferences using data from only one geographic region.
  • Sampling Bias: Arises when the data samples collected are not randomly selected, causing certain groups to be over- or under-represented. Example: Using only social media users’ data to predict general public opinion.
  • Measurement Bias: Happens when there are inaccuracies in the data collection process, leading to incorrect data. Example: Using faulty sensors to collect temperature data for weather prediction models.
  • Algorithmic Bias: Results from the assumptions and limitations of the algorithms themselves, affecting their performance on different data. Example: A model that assumes linear relationships may perform poorly on data with complex, non-linear relationships.
  • Confirmation Bias: Occurs when the data or analysis process reinforces the researcher’s preconceived notions or hypotheses. Example: Selecting data that supports a specific outcome while ignoring data that contradicts it.

3. How does bias affect the performance of machine learning models?

Bias can significantly impact the performance and fairness of machine learning models by:

  • Reducing Accuracy: Models trained on biased data may perform well on the training data but poorly on new, unseen data, leading to inaccurate predictions.
  • Creating Unfair Outcomes: Bias can lead to discriminatory practices, such as unfair hiring decisions, biased loan approvals, or unequal access to services.
  • Decreasing Trust: Users may lose trust in machine learning systems if they perceive the outcomes as biased or unfair, affecting the adoption and acceptance of AI technologies.

Example: A biased loan approval system may disproportionately deny loans to certain demographic groups, perpetuating economic inequalities.

4. How can bias in machine learning be detected?

Bias in machine learning can be detected through various methods, including:

  • Data Analysis: Examining the training data for imbalances or anomalies that could indicate potential bias.
  • Model Evaluation: Assessing model performance across different subgroups to identify disparities in accuracy or error rates.
  • Fairness Metrics: Using specific metrics, such as demographic parity, equalized odds, and disparate impact, to measure and quantify bias in model predictions.

Example: Evaluating a hiring algorithm’s recommendations to ensure that candidates from different gender or ethnic groups receive similar treatment.

5. What are some strategies to mitigate bias in machine learning?

Several strategies can help mitigate bias in machine learning, including:

  • Collecting Diverse Data: Ensuring that training data is representative of the entire population and includes diverse samples.
  • Preprocessing Data: Using techniques such as re-sampling, re-weighting, and data augmentation to balance the training data.
  • Algorithmic Adjustments: Modifying algorithms to account for and reduce bias, such as incorporating fairness constraints or using bias-aware learning methods.
  • Post-processing: Adjusting model predictions to ensure fair outcomes, such as recalibrating probabilities or re-ranking results.
  • Continuous Monitoring: Regularly evaluating and monitoring models for bias throughout their lifecycle to identify and address any emerging issues.

Example: Implementing fairness constraints in a credit scoring model to ensure that loan approval rates are balanced across different demographic groups.



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