Bias in machine learning systems. Managing technical and social impacts.

Introduction

Bias in machine learning (ML) isn’t just a technical problem, it’s a socio-technical one. Even highly accurate models can exhibit some level of bias. Social impacts of biased outcomes must be taken in consideration.

Bias in ML can come from systemic, statistical, and human sources. Systemic bias reflects historical and institutional inequalities embedded in data and design. Statistical bias includes issues like unrepresentative data and feedback loops that create unequal model performance across groups. Human bias includes cognitive distortions such as confirmation bias, groupthink, and anchoring, often affecting both system developers and users.

Mitigating bias requires diverse perspectives, including those from different demographics, disciplines, and users, especially people often excluded due to the digital divide or disability.

There are also legal implications, particularly in the U.S. and EU. Protected groups are legally shielded from discrimination. Disparate treatment occurs when someone is treated differently based on a protected trait. In some cases models perform better for some groups than others. There can be exclusion due to inaccessible design, particularly affecting people with disabilities.

Finally, you can’t address bias purely with code. Ethical, social, and legal understanding must guide the design, testing, and deployment of ML systems.

Bias from Machine Learning: who and why

Some groups are disproportionately affected. Older adults, people with disabilities, non-native language users, and people belonging to multiple marginalized groups often face more bias.

Bias happens because machine learning models reflect historical and societal biases. And that is  because they are trained on past data.

Also categorizing humans into simplified demographic groups can itself introduce bias.

Bias Testing in ML Systems: fundamentals

The main challenges are that bias testing is complex, not conclusive, and can miss harms that only emerge after deployment. Legal and ethical considerations are crucial.

Pay attention to demographic groups. Remove demographic markers. Even if direct demographic data is excluded, other features (like name or ZIP/postal code) may act as stand-ins and reintroduce bias. 

On the contrary, if demographics are required, ensure all demographic groups are adequately represented in training data.

Watch for disparities in how outcomes (e.g., success/failure) are distributed among groups.

Work with legal/compliance teams when dealing with protected characteristics.

Traditional testing approaches are checking outcomes. After ensuring the absence of bias in data and training a model, traditional tests are used to evaluate bias in model outcomes, especially across protected vs. control demographic groups.

Newer testing approaches can check for performance quality. Instead of only comparing outcomes, they assess error rates across groups to understand disparate performance impacts, offering deeper insights into how ML systems perform across demographic groups, and revealing biases traditional tests might miss. 

Combining both approaches is crucial for building AI systems.

Broader ecosystem including generative AI

The broader ML ecosystem includes generative models, recommender systems, and unsupervised learning, all of which demand different testing strategies. Let’s see how to evaluate bias across the lifecycle using both technical and human-centered methods.

Ideation Stage: involve diverse stakeholders to foresee risks.

Design Stage: plan for data collection, monitoring, and user recourse. Consult legal and UX experts for ethical and accessible design.

Data Preparation: remove demographic markers unless necessary. Test for fairness. Rebalance data if appropriate and legally sound.

Model Testing: for “simpler” models like a decision tree apply standard bias test. For complex models like Large Language Models (LLMs) apply tailor methods.

Deployment & Monitoring: continuously test for bias and collect user feedback. Address any discovered bias through governance and mitigation.

Conclusions

Machine Learning bias is deeply tied to societal structures and often replicates or magnifies real-world discrimination.

Effective bias management goes beyond technical checks. It requires thoughtful design, user feedback, and cross-disciplinary collaboration.

Bias testing must combine rigorous testing techniques with active user engagement and system governance. Real-world user impact must be take into account.

The ultimate goal isn’t just passing fairness metrics, but preventing real-world incidents.