Social Bias in Machine Learning

Understanding Social Bias

Machine learning models are developed using training data that often mirrors the society they originate from. Since this data is derived from human behaviour and experiences, social biases inherently influence it. These biases—rooted in cultural, societal, and historical inequalities—manifest in the training data and, subsequently, in the models, potentially perpetuating or amplifying discrimination. Addressing social bias is essential to ensure that machine learning systems are equitable and aligned with ethical principles.

How Social Bias Affects ML Models 

When social biases are present in the training data, machine learning (ML) models tend to replicate and even amplify these biases. This occurs because ML models learn patterns from the data, including any inequities it contains, leading to outcomes that perpetuate or reinforce existing societal biases.

For example:

• A hiring algorithm trained on historical data may Favor male candidates if the past hiring decisions were biased toward men.
• Legal systems that historically exhibit racial bias may influence datasets used in predictive policing models.

Levels of Impact

As a result of social bias, the ML models may exhibit:

1. Explicit Decisions: Sometimes, individuals are aware of the biased decision but choose to adhere to it due to social pressures or norms (e.g., avoiding dissent in a committee meeting).
2. Cognitive Influence: Social biases can subtly influence thinking, leading to unconscious reinforcement of prejudices, such as confirmation bias, which seeks evidence that aligns with existing beliefs while ignoring contradictory information.

Designing Mitigations for Social Bias

Addressing social bias issues requires careful consideration of the societal context in which models are deployed, ensuring they do not unintentionally perpetuate harmful biases. Here are some social biases:

• Cultural Bias
• Gender Bias
• Racial Bias
• Cognitive Bias

Tackling social bias requires a systematic, proactive approach. You can get started with these resources:  

Free Resources for Social Bias Mitigation

Best Practice for Social Bias from problem definition to model deployment (click free downloads)

 

 

AI Bias Mitigation Package – £999

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Mitigate and resolve 15 Types of Bias specific to your project with detailed guidance from problem definition to model monitoring.

Packed with practical methods, research-based strategies, and critical questions specific to your use case.

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Sources

Baer, T., 2019. Understand, manage, and prevent algorithmic bias: A guide for business users and data scientists. New York, NY: Apress.

Fahse, T., Huber, V. and van Giffen, B., 2021. Managing bias in machine learning projects. In Innovation Through Information Systems: Volume II: A Collection of Latest Research on Technology Issues (pp. 94-109). Springer International Publishing.