Lily Campbell
@Lily.Campbell · Updated 23 Feb. 2022

Machine learning models have been used for a variety of purposes, from predicting the stock market to detecting cancer. These models are trained on data that can be biased. For example, if the training dataset is biased towards one gender or ethnicity, the model will learn to discriminate against those groups as well.

The idea that machine learning models can overcome bias in datasets is controversial, and some say it’s impossible. However, there are ways of making machine learning models less biased. The use of more diverse training datasets and better training algorithms can help to reduce bias in these models.

Jack Penn
@Jack.Penn · Posted 23 Feb. 2022

Machine-learning models are not immune to bias. They can learn from biased datasets and reflect this bias in their predictions.

This means that there is no guarantee that machine-learning models will be unbiased, even if the data they are trained on is unbiased.

Kelly Jackson
@Kelly.Jackson · Updated 23 Feb. 2022

Machine learning models are made to learn from data. The problem is that the data can be biased. How can we make sure that the machine learning models do not pick up these biases?

The answer to this question is not straightforward. This is because there are many ways to make sure that machine learning models don't pick up biases, and they all have their own pros and cons.