Removing Bias with Machine Learning

Machine learning and automation have the potential to revolutionize the hiring process by removing unconscious bias and promoting gender and ethnicity equality in the workplace. However, it is essential to note that the implementation of these technologies must be done thoughtfully and with consideration for their potential biases and limitations.

Using machine learning algorithms in the hiring process can help remove unconscious bias by eliminating human error and subjectivity from the decision-making process. These algorithms can be trained on a diverse dataset of job candidates to ensure that they make unbiased decisions based on the qualifications and skills required for the position.

Here are five key points to consider when implementing machine learning and automation in the hiring process to promote gender and ethnicity equality:

1. Diversify the training dataset: To ensure that the algorithm does not perpetuate existing biases, it is important to train it on a diverse dataset of job candidates. This will ensure that the algorithm is not biased towards a particular group of people and can make unbiased decisions based on qualifications and skills.
2. Audit the algorithm: It is important to audit the algorithm to ensure it makes unbiased decisions regularly. This can be done by analyzing the algorithm’s decision-making process and testing it against different groups of people to identify any patterns of bias.
3. Use multiple algorithms: Using multiple algorithms can help to mitigate the risk of bias by providing multiple perspectives on a candidate’s qualifications and skills. This can help to ensure that the unbiased decision-making of another algorithm offsets any bias in one algorithm.
4. Transparency: Provide transparency in the algorithm and its decision-making process, making the criteria and factors used in the decision-making process available to the public and explaining the results.
5. Human oversight: Finally, it is important to have human oversight in the hiring process to ensure that any potential biases in the algorithm’s decision-making are identified and addressed. This can include having a diverse group of human reviewers review the algorithm’s decisions to ensure that they are unbiased and fair.

Here at Necta, we focus our two machine learning algo’s on unstructured and reinforcement learning. This means over time, our AI becomes more and more powerful, not only lowering the time to market to find the right talented individual but also individualizing the needs of your organisation through cultural fit, past choices and predictive matches. However, we do not feed our algorithms the gender, school, names, ethnicity or any data points that can allow the machine itself to start showing a bias.

This means our algo for initial shortlisting will help you remove unconscious bias from the shortlisting process, and the reinforcement learning will mean who you chose to shortlist will mean greater diversity in the years ahead.

In conclusion, the use of machine learning and automation in the hiring process has the potential to remove unconscious bias and promote gender and ethnicity equality in the workplace. However, it is crucial to implement these technologies thoughtfully and with consideration for their potential biases and limitations; processes and thought needs to be put into these technologies to ensure it is not a recipe for old ways of thinking.

By diversifying the training dataset, regularly auditing the algorithm, using multiple algorithms, providing transparency and having human oversight, we can ensure that these technologies are used to make fair and unbiased decisions. Improving diversity and ensuring better business outcomes.