Ways to Reduce Bias in AI

Four strategies advised by technology and marketing experts to reduce bias in AI

Artificial intelligence has a role in many decisions, including who gets a job interview, who qualifies for particular credit products, and many more. The article also points out that while using AI in predictions and decision-making can lessen subjectivity in humans, it can also include biases that lead to incorrect and/or discriminatory predictions and outputs for specific segments of the population. While marketing companies and other stakeholders rely on AI to help them target the best consumers for a company's products and services, as well as potential employees, they must also take measures to remove any unintentional bias from the AI algorithms. This is not only the morally right thing to do, but it can also prevent their marketing messages from reaching the right prospective consumers. The following four strategies are advised by technology and marketing experts to eradicate or at least reduce bias in AI.

Take a look at the AI Training Data

According to Zaiman, the data scientists make sure that the data conveys to the end users a complete picture of the variety. To avoid any inconsistencies, the data team thoroughly plans all the situations and the causes of action. We must carefully consider each person's history and experience in order to reduce bias. As customers use our approach, they would provide us with feedback and explain how the model would work in practice.

Check and double-check AI's judgement

According to Christian Wettre, while it was relatively simple to examine manual lead scoring designs in the past for scoring components that might be construed as discriminatory, this may be harder to do with AI models, which require more specialised knowledge to comprehend.

A best practise, according to Wettre, is to allow AI to be prescriptive but always transparent, allowing business users to assess the AI's implementation so that it can always be verified by the business.

Get direct feedback from your clients

Baruch Labunski, the creator of Rank Secure, said, "We take a lot of steps to guard against bias in our AI algorithms. Before relying on the experiences of your clients, you must take into account the limitations of your data. We accomplish that by occasionally speaking with clients in person to gather a sample of their individual experiences with AI. By doing so, we individually email or call them to inquire about their experience. To comprehend what the consumer is going through with AI, we go through the process with our vendor. After experiencing it firsthand, we may identify problems that require fixing. That is how prejudice is discovered."

Use ongoing oversight to prevent bias in AI

According to Nicolas Gaude, co-founder, and chief technical officer of Prevision.io, the company uses a five-part framework for making moral decisions in data and machine learning initiatives. "We have organized it according to the five distinct phases of a data project: initiation, planning, monitoring, execution, and closing. We can guarantee that our AI is always free from prejudice by doing this."

Even if there are safeguards in place at every stage to assist prevent bias, it is crucial to examine and monitor the outcomes to make sure that unintended bias didn't infiltrate earlier stages.

According to Gaude, the monitoring phase entails obtaining consent from data consumers, exchanging data and results with others, and being open and transparent with data disclosers. Our closure step entails documentation, continuous implementation, evaluations, and iterations of systematic information ethics issues, as well as taking into account how data is being disposed of, destroyed, and/or retrained.