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.
The issue, therefore, is to identify and address such biased sources before they create a problem. It is about making sure that the training data, for AI models, is as diverse and inclusive as the real world, and does not contain prejudice.
The Ripple Effects of AI Bias in Business
Algorithmic bias can lead to discriminatory outcomes in hiring, lending, and other critical business processes
AI bias isn’t confined to theoretical discussions or academic debates. It has a real and measurable impact on the bottom line of businesses, leaving a trail of financial losses, legal battles, and tarnished reputations.
- Reputational Damage: Consider the cautionary tale of Microsoft’s AI chatbot, Tay. Within hours of its release in 2016, Tay, trained on Twitter conversations, learned to spew racist and sexist remarks, forcing Microsoft to quickly shut it down. The incident not only showcased the dangers of unchecked machine learning bias but also dealt a significant blow to Microsoft’s reputation, raising concerns about its commitment to ethical AI development. (Know more)
- Financial Losses: The consequences of this algorithmic bias are not only social but also have financial implications that are just as severe. Another high-profile scandal involving Goldman Sachs surfaced in 2019 when it was revealed that its Apple Card was programmed to provide significantly lower credit limits to women than their male counterparts with similar credit scores and incomes. This led to outrage, demands for an inquiry, and possible legal proceedings showcasing the fact that bias in AI software has severe financial repercussions. (Know More)
- Legal Troubles: Legal troubles are another equally grave problem. Facebook was accused of discrimination in housing through its ad-targeting platform, which was claimed to demographically exclude individuals of color, women, and persons with disabilities, among others. This case shows how companies expose themselves to legal risks when their AI systems reproduce bias. (Know More)
- Eroded Customer Trust: Algorithmic bias also has significant social impacts: it may lead to loss of customer confidence, which is a crucial component in any company. A Forbes Advisor survey shows that 76% of consumers are concerned with misinformation from artificial intelligence (AI) tools such as Google Bard, ChatGPT, and Bing Chat. This lack of trust translates to a loss in sales, customers and clients switching to other better firms, and erosion of brand image. (Know More)
A Multi-Pronged Approach to Tackle AI Bias
Mitigating AI bias requires a holistic approach, addressing both technical and organizational factors:
- Data Diversity: Make sure training data is as diverse as possible to meet real-world applications. This includes using sources to gather information and making sure everyone who needs to be represented has been included.
- Algorithmic Transparency: Introduce AI systems that are understandable so that users can see how decisions are being made. This helps to ensure that biases are detected and eradicated where necessary.
- Bias Testing and Auditing: Biases should be detected in AI systems periodically through some automated methods, and they should be checked by reviewers as well. It is advisable to engage several stakeholders in this process as this will provide the needed diversity of opinion.
- Ethical Frameworks: Implement best practices and standards that shield your organization from core ethical risks associated with AI. Enhance the culture of accountability and responsibility.
- Human-in-the-Loop: Always have human supervision during the development of an AI system, from its design to deployment. AI can perform tasks independently, but when it comes to correcting algorithmic bias, it is the judgment of a human that is needed. This is called having a human in the loop of AI training.
Conclusion
Creating ethical AI isn’t a one-and-done deal; it’s a constant balancing act that requires our unwavering attention. We must first acknowledge the uncomfortable truth: bias is deeply ingrained in our society and can easily infiltrate the very AI technology we create.
This means we need to build diverse teams, bringing together people with different backgrounds, experiences, and perspectives.
Shining a light on AI’s decision-making process is equally important. We need to understand why AI makes the choices it does. Transparency builds trust, ensures accountability, and makes it easier to spot and correct potential biases.
But technology alone can’t solve this problem. We need strong ethical frameworks, a shared sense of responsibility, and clear rules for AI development. After all, the people behind the technology, the environment in which it’s created, and the values it embodies will ultimately determine whether AI helps or hinders humanity.
Don’t let bias hold back the potential of AI. Embrace the power of Bionic AI to unlock a future where innovation and ethics go hand in hand. Book a demo now!