Algorithms play a crucial role in our daily lives, from social media ads to the way your job application is screened or which political ad shows up on your feed, almost everything revolves around them. However, algorithms aren’t infallible; they are only as good as the data that they are fed. And if the data is biased, then the algorithm is too. Let’s discuss the issue of biased algorithms, the factors we need to consider when speaking on bias in algorithms and how to combat them.
What are biased algorithms?
A biased algorithm is an algorithm that has been programmed to make decisions based on data that is skewed or incomplete. For example, if an algorithm is trained on data that is primarily composed of male candidates, it may make biased decisions when it comes to hiring female applicants. Biased algorithms can also be the result of conscious or unconscious prejudices of the people who program them.
Factors to consider when speaking on bias in algorithms
- The data used to train the algorithm: The data needs to be diverse and representative of the population that the algorithm will be used on. If the data is biased, then the algorithm will be biased too.
Human bias can contribute to biased algorithms. It refers to the unconscious or conscious prejudices that we all have based on our experiences and cultural backgrounds.
For example, if a hiring manager has a bias against a certain gender or race, they may be less likely to consider candidates from those groups, which can lead to biased hiring decisions. Similarly, if the data used to train an algorithm is biased, it can perpetuate and amplify these biases.
- The algorithms’ objectives: It’s essential to consider the goals and objectives of the algorithm. What outcomes is the algorithm trying to achieve, and how are those outcomes being measured? This can help identify potential areas of bias.
- The context in which the algorithm will be used: Different situations may require different data inputs or algorithms to ensure unbiased results.
- The diversity of the programming team: It’s essential to have a diverse programming team that can provide multiple perspectives when developing algorithms. This can help ensure that it is fair and unbiased.
“Not only programming, but also data engineering and management teams are important… to ensure that models are not biased, you need people from Social Science in your teams [as well]. Most of the time, a software engineer or a data scientist doesn’t count with the scientific background to ensure neutrality and data diversity.” – Fabricio Quagliariello, Lead Engineer
How leaders can enhance the perspectives of data fed into an algorithm
Leaders can play a critical role in ensuring that algorithms are unbiased.
Diverse teams: Building diverse teams that can provide multiple perspectives, this can help identify biases and guarantee that the algorithm is diverse too.
By building a team that represents a broad range of backgrounds, cultures, and experiences, leaders can promote diversity and equity in the development of algorithms. They can also help to mitigate the effects of human bias that can influence the data that is fed into algorithms. By incorporating multiple perspectives, leaders can help to ensure that algorithms are fair, serving all members of society equitably.
Transparency: Promoting transparency in the algorithms used by providing access to the data inputs and the algorithms’ results. This can help warrant accountability and identify potential biases.
Constant monitoring: Monitoring the algorithms regularly to identify biases and correct them as soon as possible. This can help ensure that the algorithm remains unbiased over time.
Bias testing: Testing the algorithms to identify potential biases systematically. This can help ensure that the algorithm is fair and unbiased in all situations.
How a biased algorithm affects a business
Research has shown that Facebook’s ad-distribution software is biased and tends to discriminate by race and gender, even when advertisers do not intend to do so. An investigation revealed that housing ads with the same text but different images of white and black families were served to vastly different audiences. The study found that Facebook’s ad system had learned to associate certain demographic groups with specific products or services, leading to discriminatory practices. This bias has been a cause for concern as it can perpetuate inequalities and limit opportunities for certain groups. Facebook has been urged to address this issue and ensure that their ad system is fair and unbiased by improving their machine learning algorithms.
How Athenaworks works towards unbiased algorithms
At Athenaworks we prioritize diversity and inclusion within our teams, ensuring that every employee represents a broad range of backgrounds, cultures, and experiences. We engage in rigorous bias testing and auditing to identify potential biases in the data and algorithms throughout our development process. Our team of experts review the algorithms for fairness, transparency, and accountability, making sure that they do not perpetuate discrimination and inequality. Athenaworks provides education and training on responsible AI practices and offers tools for our clients to monitor and address any potential biases in the algorithms they use. By prioritizing diversity, transparency, and accountability, we set an example for the industry in developing equitable algorithms.
Algorithms are incredibly powerful tools, but they are only as good as the data that they are fed. Biased algorithms can have severe consequences, from perpetuating discrimination to denying opportunities to certain groups of people. It is essential to consider the factors that contribute to bias in algorithms and take steps to ensure that they are unbiased. With the steps mentioned above, we can make certain that algorithms remain unbiased and fair for everyone.
