As the capabilities of artificial intelligence (AI) continue to grow, more businesses across varied sectors seek to access the possibilities AI can offer. With AI’s reach expanding further than ever, it is critical for businesses and software professionals to be aware of the largest problem facing AI today: bias. Algorithm bias reflects partiality in people and systems and can lead to serious real-world consequences of perpetuating discrimination against already marginalized groups. It can also create a major liability for organizations, resulting in social and economic costs. Addressing AI bias requires large and varied data sets, thorough testing, and strong collaboration between developers and teams.
Causes of Data Bias
Bias in AI algorithms begins with the data. It involves systematic and unfair favoritism or discrimination against groups or individuals. Often, AI is biased because the sampling data it utilizes to train reflects historical discrimination. For example, an algorithm meant to select potential hires from a pool of candidates might train on documentation of previous selection decisions. If past hiring managers demonstrated a bias against female applicants, that bias would be revealed in the data, and the resulting algorithm would show the same tendency.
Another contributing factor in bias stems from incomplete, unrepresentative data. For instance, training a facial recognition algorithm on pictures of only white men will lead to its inability to recognize women and people of color. To avoid bias, sampling data sets must be large and varied enough to reflect the real world. Sets too small or drawn from a single source are more likely to be biased. Labels are also an important part of sampling data. If some data has incorrect or missing labels, the algorithm is more likely to make mistakes. Finally, context is essential for sampling data. If data lacks necessary context—for example, readings of a health metric that don’t account for age—this can contribute to the AI developing a misleading view of the world, thereby producing inaccurate results.
Importance of Fair, Accurate, and Trustworthy AI
Biased AI can have severe negative impacts on the lives of marginalized populations, further entrenching existing social inequities. For instance, if an algorithm that determines trustworthiness for loans is biased against non-white borrowers, it can perpetuate the economic disadvantages already facing people of color in the United States. Companies that use biased AI will face negative organizational ramifications. Algorithm bias in customer interactions can have economic and legal consequences for a company. If customers can prove they were discriminated against, they have grounds to pursue legal action against the business. The result could be damaging to the company’s bottom line and reputation. Companies as large as Facebook, which was sued in 2019 for using ad-personalization algorithms that targeted users based on gender, race, and religion, are dealing with these issues.
While not every case of AI bias will result in a lawsuit, other costs are involved. When AI models are discovered to be biased, it is critical that systems undergo retraining, which is expensive, time-consuming, and detrimental to productivity. To avoid or mitigate this risk, leadership benefits from prioritizing the management and minimizing bias.
Enterprises can avoid the dire consequences of AI bias by prioritizing good practices around sampling data. It is important to ensure data sets are large and varied, and then draw from multiple sources in the event that one is biased. In the previously mentioned case of training facial recognition algorithms, a solution would be to expand the data set of faces to include people from around the world.
Often, synthetic data is used to train AI. For example, an algorithm designed to identify financial fraud might train on dummy transactions. Synthetic data provides a solution for situations when not enough real-world data is available. It can also help avoid the issue of bias in historical data. Using synthetic data, however, presents its own potential challenges. It is crucial to ensure that synthetic data is sufficiently varied and does not reflect the partialities of the person who created it. For this reason, it is important that enterprise standards govern synthetic data.
Program directors and team members have specific roles to play in addressing AI bias. The program director’s role is to provide the team with a diverse collection of data. The implementation team is responsible for cleaning, processing, categorizing, and labeling the data, while including necessary context.
Use Algorithm Testing to Address Bias
Algorithm testing is a critical process that provides an opportunity to identify and nip AI bias in the bud before it can have harmful consequences. There are four main stages of algorithm testing:
Unit testing is the initial testing stage, performed by the developer.
Integration testing tests the model in conjunction with other enterprise components.
Performance testing uses high-volume data sets to simulate real-world scenarios.
Fairness testing explicitly tests for bias, ensuring the algorithm does not favor or disfavor certain groups. Business users usually conduct it. To be most effective, fairness testing needs to be creative and address edge scenarios. Fairness testing is growing in popularity and implementation among enterprises seeking to address bias.
The Path to a More Unbiased AI
Biased AI can cause serious negative repercussions within and outside an organization. Fortunately, businesses are taking the issue seriously and changing how they source data and test algorithms. The involvement of cross-functional, interdisciplinary, and diverse stakeholders is essential for the design and implementation of an effective bias management program. To develop fair, accurate, and trustworthy AI, it is critical to ensure that sampling data for AI algorithms is multi-sourced, varied, and unbiased. Multi-step testing with high-volume data sets also plays a crucial role. Businesses prioritizing unbiased, responsible AI will benefit their organization and the larger communities in which they operate.
About the Author
Kulbir Sandhu is a digital transformation leader with expertise in artificial intelligence, machine learning, robotics process automation, and cloud migration. He has implemented AI/ML solutions for financial clients, helping organizations identify money laundering activities, anomalies on accounts, and fraudulent transactions. Mr. Sandhu has a bachelor’s degree in electronics and communication from Punjab Technical University. For more information, contact firstname.lastname@example.org.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.