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Real-World Examples of Bias in Machine Learning and Deep Learning

“All of this work shows that it is important how you choose your words. To me, this is actually a vindication of political correctness and affirmative action and all these things. Now, I see how important it is.”—Joanna Bryson, Computer Science Professor at the University of Bath

The Meaning of “Bias” and Different Types

“Bias” in data: when a sample is not representative of the underlying population it represents. When this happens, your statistical derivations become inaccurate.

“Bias” in statistics: the difference between expected and actual values. When this happens, even if your estimations are consistent and precise, they are incorrect.

“Bias” in sociology": tendency, known or unknown, to prefer one factor over another, preventing objectivity. When this happens something is influencing decisions or observations in a way that is deemed undesirable.

“Bias” in neural networks: a parameter for a <a href="https://en.wikipedia.org/wiki/Artificial_neural_network#Components_of_ANNs" target="_blank" rel="noopener noreferrer">neuron</a> which is used to tune a model. This is used to make trade-offs between bias and variance (using their statistical definitions for ML).

“Bias” as most people understand it: when past experiences or erroneous assumptions affect our perception in a way that negatively affects other people. When this happens we often say that something is unfair.

Navigating the Complexity of Bias in Machine Learning: Trade-offs, Ethics, and Regulation

The first system is quick to judgment, much like the “shortcuts” we described before as “biases.”

The second system is slower and weighs data more carefully before making a decision.

“Is it ok to discriminate if the data tells us that we’ll be right far more often than wrong? It would be naive to think that gender, age, race, ethnicity, religion, and country of origin collectively tell us nothing about anything related to crime. But what we can or should do with that kind of information is a philosophical and legal question, not a statistical one. If we can build a model that identifies offenders correctly 80 out of 100 times, what happens to the poor souls in the 20 percent? Our model is going to harass them over and over and over again.” —Wheelan, Naked Statistics

What aspects of our society do we wish to be ignored by Machine Learning models?

What biases in our society do we wish to see corrected or changed?

“AI systems should be designed in a way that respects the rule of law, human rights, democratic values and diversity, and they should include appropriate safeguards – for example, enabling human intervention where necessary – to ensure a fair and just society.”

“In any case, such processing should be subject to suitable safeguards, including specific information of the data subject and the right to obtain human intervention, to express his or her point of view, to get an explanation of the decision reached after such assessment and the right to contest the decision. In order to ensure fair and transparent processing in respect of the data subject, having regard to the specific circumstances and context in which the personal data are processed, the controller should use adequate mathematical or statistical procedures for the profiling, implement technical and organisational measures appropriate to ensure in particular that factors which result in data inaccuracies are corrected and the risk of errors is minimized, secure personal data in a way which takes account of the potential risks involved for the interests and rights of the data subject and which prevents inter alia discriminatory effects against individuals on the basis of race or ethnic origin, political opinions, religion or beliefs, trade union membership, genetic or health status, sexual orientation or that result in measures having such effect.”

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AI bias is unethical because it can violate individuals' rights to meaningful explanations in automated decision-making, perpetuate human prejudices, and undermine fairness and trust in AI systems. Addressing unwanted bias and upholding fairness requires a thoughtful focus on data, diverse teams, and empathy, as both an ethical imperative and a legal responsibility.

Why is AI bias unethical?

There are a few famous examples of AI bias, including the COMPAS system. This was a tool used in criminal justice, which has been found to unfairly assess African American defendants and potentially lead to unjust sentencing. Google Translate has also faced criticism for perpetuating gender stereotypes in translations, reflecting societal biases present in its training data. Additionally, Google Photos has been known to mislabel photos of African Americans, highlighting racial bias in facial recognition algorithms.

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