How Can Fairness be Built into the AI Systems

In recent years, the emergence of artificial intelligence (AI) has revolutionized various aspects of our lives—from healthcare to transportation to finance and entertainment. AI systems can analyze vast amounts of data, identify patterns, and help make informed decisions without manual processes or human intervention.

While the advancements in AI have brought numerous benefits, they have also raised concerns about fairness in AI systems. A study by the Pew Research Center found that Americans are “more concerned than excited” by the increasing applications of AI in daily life. As such, building fairness into AI systems has become even more important.

In this blog, we’ll discuss why fairness in AI should be seriously considered and provide some recommendations on developing an explainable, equitable, and reliable ethical AI.

AI Bias and Its Repercussions

AI systems can be unfair in various ways. The most common type of algorithmic bias involves using data not representative of the population or training models with limited datasets that are heavily biased towards one group. 

This could lead to AI making decisions about opportunities and services based on inaccurate demographic information, leading to discrimination due to existing societal biases. Additionally, training data errors can propagate into the final model output. For example, an AI system trained on a dataset containing racial stereotypes might lead to inaccurate predictions and false positives for minority groups.

Ways to Ensure Fairness in AI Systems

Organizations must take proactive steps to ensure fairness in AI systems. Here are some practical measures you should consider: 

1. Utilize Diversity-Aware Data Sources: Organizations should use datasets representative of all target populations when training their AI models. This helps avoid bias or exclusion based on demographic data such as gender, race, or ethnicity. 
2. Implement Automated Fairness Checks: Organizations should implement automated fairness checks during development to identify and address potential bias. This will help ensure that AI systems perform accurately and fairly for all target populations. 
3. Require Explainability From AI Systems: Explainability is important in creating trust between users and AI systems as it provides greater transparency on how the system makes decisions. 

Organizations should require explainability tools or techniques such as natural language processing (NLP) models to generate explanations about why certain decisions were made, which can then be reviewed by experts or regulators to ensure fairness and accuracy. 

Develop Unbiased AI with VirtuousAI

VirtuousAI provides an Infrastructure as a Service (IaaS) where customers can develop ethical Artificial Intelligence that is equitable, reliable, and transparent—entirely free of charge! Our cloud-based platform provides a range of automated fairness checks and explainability tools to help create trust in AI systems. 

With VirtuousAI, organizations can be confident that their AI projects are fair, accurate, and trustworthy. Contact our team for more information.