What is Algorithm/Artificial Intelligence Bias?
Machine learning bias, also known as algorithm bias or AI (artificial intelligence) bias, refers to the tendency of algorithms to reflect human biases. It is a phenomenon that arises when an algorithm delivers systematically biased results due to erroneous assumptions of the machine learning process.
In today’s world of increasing levels of diversity and representation, this has proven to be much more problematic because the algorithms could be reinforcing biases. An algorithm could be trained in various ways. For instance, a facial recognition algorithm could be trained to recognise a young person more easily than an older person, as this data type has been used heavily during training. The main issue is that these biases are often unintentional and are not easily detectable until they have been programmed into the software.
Real-Life Instances of AI Bias
In real life, there could be multiple AI bias cases that we may encounter daily. To illustrate the issue at hand, in the United States, women make up about 27% of the CEOs. However, a recent study has shown that only 11% of the individuals who appeared in a Google picture search for the term CEO were women. A few months later, independent research was conducted at Carnegie Mellon University. This research revealed that Google’s online advertising system displayed high-paying positions to male individuals more often than to women. In response, Google pointed out that advertisers can be specific about which individuals they want their advertisements catered to while launching the ad. One such specification is the gender specification that the advertisers can set.
The researchers also suggested that this bias could be caused by the behaviour of the users, which in turn caused Google’s algorithm to get the connotation that men are more suited to high-paying jobs than women. For example, suppose most users who click on the advertisements for high-paying jobs are male. In that case, Google’s algorithm will learn to show those advertisements only to male individuals.
How Does AI Bias Work
The data used in training is a key element in determining the AI model and its predictions. The way these data are gathered or collected can heavily influence the AI model and possibly integrate bias into the system, including society’s bias into the model. First and foremost, we need to know the model training process to understand better how bias can be introduced into AI. For example, user-generated data may lead to a biased feedback loop. Research conducted on search engines found that the searches that contained the term “arrest” came up more often when African-American-identifying names were searched than for White-identifying names. The researchers speculated that the algorithm showed these results more frequently because the users may have clicked on various versions more often for different searches.
AI and Algorithm Bias Affecting Businesses
AI can drastically change how businesses operate and simplify their day-to-day operations. Every business is taking advantage of this new phenomenon in this modern world of AI technology. AI technology could contribute close to $15.7 trillion to the global economy, according to a PwC report published recently. While businesses across the globe are recognising the power of AI, they need to acknowledge the presence of bias in the systems as the consequences are costly. Experts suggest that there is a lot of brand value associated with the use of AI technology and many times, the implementation of AI fell short, and as a result, the brand faced a negative impact. Additionally, corporations are tilting more towards generative AI because of the credibility that AI has garnered in recent times.
But in doing so, a lot of the businesses have faced backlash in the form of lawsuits and liabilities as many of the generative AI that are being used have racism and sexism embedded in them. In 2015, Google faced a huge backlash because of AI technology when it launched its Photos app. The photo recognition technology of this app tagged a Black couple as gorillas. The company drew further criticism when it decided to address the problem by removing tags related to primates rather than developing a technology to better distinguish between different species. AI or algorithm bias can also throw off businesses’ supply chain processes by mispredicting and falsely forecasting customer demand for targeted audiences. These incidents, although unrelated, are caused by the same error in the system, which is AI bias or algorithm bias of the model.
So, experts say that businesses have to be aware of potential biases and should carry knowledge on how to mitigate these biases because the more biases are reduced, the more accurate the model is. More accuracy indicates better business outcomes.
