AI Bias: Why Algorithmic Bias can hurt your business? - Bionic
This Blog was Originally Published at: AI Bias: Why Algorithmic Bias can hurt your business? — Bionic A decade ago, two individuals, Brisha Borden and Vernon Prater, found themselves entangled with the law. While Borden, an 18-year-old Black woman, was arrested for riding an unlocked bike, Prater, a 41-year-old white man with a criminal history, was caught shoplifting $86,000 worth of tools. Yet, when assessed by a supposedly objective AI algorithm in the federal jail, Borden was deemed high-risk, while Prater was labeled low-risk. Two years later, Borden remained crime-free, while Prater was back behind bars. This stark disparity exposed a chilling truth: the algorithm’s risk assessments were racially biased, favoring white individuals over Black individuals, despite claims of objectivity. This is just one of the many AI bias examples, the tendency of AI systems to produce systematically unfair outcomes due to inherent flaws in their design or the data they are trained on. Things haven’t changed much since then. Even when explicit features like race or gender are omitted, AI algorithms can still perpetuate discrimination by drawing correlations from data points like schools or neighborhoods. This often comes with historical human biases embedded in the data they are trained on. AI is good at describing the world as it is today with all of its biases, but it does not know how the world should be.” — Joanne Chen To fully realize the potential of AI in the interest of business while minimizing its potential for negative effects, it is crucial to recognize its potential drawbacks, take measures to address its negative effects and understand its roots. In this article, we will take a closer look at the bear traps of AI and algorithmic bias, understand its types, and discuss the negative impacts it can have on your company. We will also teach you how to develop fair AI systems that contribute to the general welfare of society. Indeed, the future of AI should not be defined by the perpetuation of algorithmic bias but by striving for the greater good and fairness for everyone. What is AI Bias? AI biases occur when artificial intelligence systems produce results that are systematically prejudiced due to flawed data, algorithm design, or even unintentional human influence. For instance, COMPAS is an AI technology employed by US courts to assess the risk of a defendant committing further crimes. Like any other risk-assessment tool, COMPAS was used and was condemned for being racially prejudiced, as it more often labeled black defendants as high risk than white ones with similar criminal records. This not only maintained and even deepened racism in the criminal justice system but also drew questions as to the correctness and objectivity of AI processing. Understanding the Roots of Algorithmic Bias Machine learning bias is often inherent and not brought in as a flaw; it simply mirrors our societal prejudices that are fed into it. These biases may not always be bad for the human mind because they can help someone make quick decisions in a certain situation. On the other hand, when such biases are included or incorporated into AI systems, the results may be disastrous. Think of AI as a sponge that absorbs the data it is trained on; if the data contains prejudice that exists within society, the AI will gradually incorporate those prejudices. The incomplete training data also makes the AI come up with AI hallucinations, which basically are AI systems generating weird or inaccurate results due to incomplete training data. The data that machine learning bias flourishes in is either historical data, which captures past injustices, or current data with skewed data distribution that fails to include marginalized groups. This can happen if one uses the Grounding AI approach based on biased training data or the design of algorithms themselves. Algorithmic bias can arise from the choices made by developers, the assumptions they make, and the data they choose to use.