Artificial intelligence (AI) has proven to be an effective tool in revolutionizing various industries such as healthcare, finance, and transportation. This technology has enabled organisations to automate various tasks and make important decisions with greater efficiency and accuracy. However, there is growing concern that AI systems are not as impartial as they should be, and that AI algorithms can exhibit bias.

AI bias occurs when AI systems are not neutral, and their programs exhibit bias towards specific groups of people, data, or features. This bias can lead to unfair outcomes, discrimination, and perpetuate societal inequalities. For instance, an AI recruitment tool could be biased towards male candidates, leading to an irregular number of female hires.

AI bias can be introduced to a system in several ways. The following are some of the most common ways;
– Intentional Bias – Some developers introduce bias into AI systems intentionally, with the aim of gaining an unfair advantage or discriminating against specific groups.
– Historical Bias – AI models learn from data available, such data may be inherently biased because of historical factors e.g., data on the workforce composition could be biased towards certain groups since artificial intelligence tends to replicate the existing data in the system, thus generating similar results over time.
– Technical Bias – This type of bias is a result of algorithm design, testing or operational errors. For instance, a poor risk evaluation tool may lead to biassed decision-making.
– Representational Bias – This is related to misrepresentation in terms of how an AI system classifies or categorizes particular groups. For example, a tool for understanding emotions may not represent the full range of human expression, thus failing to match emotions of some groups of individuals.

Examples of AI Bias

AI bias may appear subtle, but it can result in far-reaching implications for individuals, organizations and even entire communities.

* AI Voice Recognition Bias: AI voice recognition tools such as Siri, Amazon’s Alexa and Google Assistant have been shown to exhibit gender bias. Research has shown that these tools tend to have difficulties recognizing female voices and are more responsive to male voices.

* Criminal Justice Bias: AI tools are being used in the criminal justice system, but sadly, these tools have been shown to be biased against black Americans. Research revealed that a popular tool known as COMPAS over-predicted recidivism among African American defendants (45.5%) than Caucasians (24.9%).

* Biases in Job Postings: In 2018, Amazon found themselves in trouble after their AI recruitment tool was biased against women. The tool used past hiring data to evaluate job candidates and the tool taught itself to favor male candidates. This happened because the vast majority of historic hires were male engineers, hence biasing the tool in gender favoritism.
* Money and Credit Card Approval: AI tools may make biased decisions when approving loans or identifying high-risk individuals. For instance, in 2019, Goldman Sachs implemented an AI tool to determine credit loan eligibility. The tool was biased against women, ultimately becoming the subject of an investigation.

The dangers of AI Bias

Apart from contributing to societal inequality, AI bias also leads to;
– Inaccurate Decisions: Biases in an AI system can lead to decisions that are not based on impartial, objective data. As a result, the system unjustifiably impacts individuals or groups.
– Loss of public trust: Showing biases in AI systems can lead to distrust amongst consumers and ultimately lead to a decline in the use of this technology.
– Negative impacts on the credibility of businesses: When AI bias is discovered, it can lead to a loss of reputation for companies, reducing their market value.

It is essential to note that data and AI developers are responsible for improving the accuracy of AI systems.

How to prevent AI Bias

To reduce the likelihood of artificial intelligence bias, developers must:
– Audit the software and data for bias before deploying AI models.
– Define a clear objective for each AI program. This is essential to prevent developers from aligning an AI system to values or attitudes that don’t align with the original purpose of the system.
– Use a diverse set of data when training the AI algorithm. This ensures that the AI system captures the full range of societal diversity represented in society today.
– Provide transparent explanations for how AI systems are making decisions, and make sure to document implicit concepts or assumptions. This makes it easier for regulators or auditors to pinpoint areas that are likely to be biased.
– Evaluate the AI systems regularly and remove those that demonstrate significant levels of discrimination or bias.

Conclusion

AI technology has incredible potential, but developers must ensure that they are not bias, and their use contributes to societal equality rather than perpetuating it. As AI is continuously being integrated into different sectors, it’s essential to remain vigilant of bias and work to identify and rectify it. Every organisation must make it part of its responsibility to ensure that they are promoting fairness and equality when dealing with AI systems. Ultimately, the high-level responsibility for preventing bias in AI systems falls to developers and businesses immersed in AI, and history has taught us that any difference from this can bring about significant issues.