How to Identify and Mitigate Bias in Machine Learning Models

Even the best models can still be subject to unwanted bias. Fortunately, many technical awareness and debiasing tools can help limit the risk of bias in machine learning models.

Feature bias can happen when a model is trained on data that contain disproportionately large numbers of certain features. This type of bias has received considerable attention in the literature, particularly about gender, race and regional bias.

Use a Diverse and Representative Training Dataset

The data used to train ML models can introduce bias in several ways. One common type of bias is representation bias, which occurs when the model misrepresents a particular population. For example, suppose your training dataset contains only data points from people who work in a city center and no information from those who work at train stations. In that case, the model may predict that people working in cities will be more likely to default on their loans than those from train stations.

This bias in machine learning algorithms results from existing social prejudices, which can influence the decisions made by the algorithm. To reduce this bias, the data should be gathered using methods that ensure diverse opinions are represented.

This can include incorporating multiple data collection methods, removing explicit biases from the data (such as excluding names or gendered pronouns), and employing strategies that mitigate second-order associations between words that can form undesired signals for algorithms. In addition, implementing techniques like human-in-the-loop decision-making can reduce bias by providing humans with alternatives or recommendations that the algorithm might miss.

Remove Sensitive Variables

ML models rely on data to make decisions, and this data often reflect social biases. This data can be hard to spot, but ways exist to identify and mitigate bias in ML models.

Removing sensitive variables from the training dataset is one way to mitigate bias in machine learning models. This is important because it prevents the model from using those features in its decision-making. It is important to remember that this does not eliminate bias. A new feature could replace the old one and have a different effect.

Another way to mitigate bias in ML models is to use a post-processing technique. This can help equalize the relationship between input features and output labels, reducing measurement bias (Corbett-Davies & Goel, 2018; d’Alessandro et al., 2017). It can also reveal one-time phenomena that may affect the data and must be removed to prevent representation bias. Examples of these techniques include averaging the value of the feature or randomly selecting data points (Dwork et al., 2017).

Use Bias Mitigation Techniques

When ML models detect or predict certain biases, there are several ways to mitigate these harmful effects. For example, feature engineering bias occurs when the data set contains deleterious features for a model’s results or predictions (Mullainathan & Obermeyer, 2017). It is important to scale the data features to be measured similarly to prevent this bias.

Additionally, it is important to remove sensitive variables from the training dataset. This can help reduce bias in several ways, including removing societally unacceptable or illegal correlations. For instance, if an algorithm picks up on a statistical correlation between an individual’s age and mortgage default risk, this may be seen as discrimination and could lead to legal action.

Using a diverse research team is another good way to prevent bias during an ML project’s data collection, modeling and deployment phases. This can help reduce measurement and representation biases and avoid confirmation or preference biases. It can also help ensure that a model is appropriate for its intended use context.

Regularly Evaluate the Model

ML models can be used in many applications, such as data classification and clustering, pattern recognition and anomaly detection, and natural language processing. However, it is important to identify all forms of bias that could impact the data set you are using for training your model.

For example, suppose you are using historical data that contains bias. In that case, it can lead to unintended consequences such as the bandwagon effect (in which the model is trained to confirm preexisting beliefs or hypotheses) and experimenter bias (in which the model is kept training until it matches the experimenter’s initial hypothesis). It is also possible for data to be biased due to errors in measurement or observation.

For instance, if a data scientist accidentally removes certain features from the dataset, it can cause a representation bias (in which the resulting model is not representative of the relevant population) or exclusion bias (in which specific data elements are removed). While a growing number of technical awareness and debiasing tools can mitigate these types of bias, it is critical to regularly evaluate your model to prevent unwanted biases from developing.

Use Human Oversight

While ML algorithms are not immune to bias, human oversight can be used to reduce it. This can be done by establishing diverse teams to mitigate measurement and representation bias during an ML project’s design and data preparation phases. Additionally, ML tools can help identify how input features influence prediction accuracy and highlight potential sources of bias in the data. Finally, using transparent and interpretable models prevents interpretation bias by revealing the relationship between input variables and output classes.

Other mitigation strategies include using targeted data augmentation to reduce representation bias and counterfactual methods to avoid encoding bias into the model (e.g., Silvia Chiappa’s path-specific counterfactual method). Regularly evaluating the model and using human oversight are also effective in mitigating these biases.

When identifying and mitigating bias in machine learning, it is important to remember that the model’s results may significantly impact people’s lives. Despite this, it is still possible to reduce bias in ML models and use them for ethical purposes.

*This is a collaboration post