Machine learning can be used to reduce implicit bias in the resume screening process, including the issue of career breaks. However, it’s important to note that this is not a trivial task, and it would require a specific and careful approach.

One way to reduce implicit bias in the resume screening process is to use machine learning to analyse resumes in a way that is not dependent on traditional features such as job titles and years of experience. This could include analysing text in resumes to identify relevant skills and experiences or using natural language processing (NLP) to analyse resumes in a more holistic way.

Another approach is to use machine learning to identify patterns in the data that may indicate bias, and then use this information to adjust the model or the data to reduce the bias.

Moreover, the use of bias correction techniques during the model training process can help in reducing bias, however these techniques are not foolproof and they may not be enough by themselves to eliminate all bias from the model predictions.

As mentioned before, the use of machine learning models alone is not enough to remove implicit bias in the recruitment process. A thorough human review and monitoring should be done in parallel to detect and correct any potential issues.