AI&SOCIETY - 03/08/2020

The Risks of Artificial Intelligence Bias

A growing number of organizations and industries are relying on Artificial Intelligence systems in their businesses. Many of them are just using AI technology as an important factor of success for the future industry environment. Still, more than 70% of business operations today involve AI algorithms that make decisions and take actions at an incredibly fast pace.

Despite the increasing use of AI, the practice has shown some issues that are worrying IT professionals and challenge objectivity in decision making. Almost half of the specialists employed in the sector are concerned about the bias. Such an issue is undermining the trust of both employees and consumers in the credibility of machine learning.

Statistics of AI-Based Business Operations

DataRobot surveys note interesting numbers that indicate the involvement of AI in the following business.

It is most widely used in the operative departments that use cognitive systems in about 76% of their operations, followed by finance with 54% of AI-driven operations. The marketing sector is doing about 50% of its activities by using AI algorithms, while human resources are showing much lower numbers.

Yet, a large percentage of people in the executive and IT sector consider bias to be one of the leading threats for further AI implementation.

How Does AI Bias Appear?

Ironically, AI has the potential to manage tasks fairly and without any human bias. Still, artificial intelligence itself can also become the source of it. Due to the constant input of vast amounts of data into cognitive systems, we also insert a human subjectivity. Underlying data are often the cause of bias, as there is not enough expert content, which makes way for assumptions, stereotypes, and even spam.

For that reason, a justice system, for example in the US, should be free from racism. Instead, artificial intelligence wrongly marks members of the African American community as “risky” and causes more frequent sentences for African American people in shorter court proceedings.

The analytics of past events can lead to the creation of misguided patterns and prejudices that stop AI from judging righteously.

How to Reduce Bias in AI Algorithms?

First, it is essential to identify all forms of fairness. Is it an individual matter or it represents an equal treatment for people who are similar in some characteristics? If the second answer is correct, then particular emphasis should be placed on fairness in groups, as they are often the target of artificial bias. It is an exceptionally complicated task since we cannot apply one common rule to all groups, except in specific conditions.

While some feel that different fairness patterns should be set depending on the group, others want to maintain the same profile for everyone. It seems that the problem of fairness will never succeed in finding a universal model that would fit every individual or group entirely and without bias.

Explainability Techniques 

One technique was initially developed to label the next issue of AI systems — explainability. It can play a significant role in detecting biases in AI algorithms but also in finding a specific solution for their removal. The following technique would be able to analyze all the factors included in making a decision and determine if there was any influence of bias during the process.

Conclusion

Every business must take the bias problem very seriously. Most executives declare they will direct more resources and develop a strategy to mitigate the consequences of AI bias. It is possible by investing in white box systems or hiring specialized help, even third-party companies, solely for AI fairness issues. About 85% of executives believe that fixing this critical point would contribute to better use of AI in business.

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