Science fiction icon Arthur C. Clarke famously wrote that “any sufficiently advanced technology is indistinguishable from magic.” Certainly, the developments we’ve seen in artificial intelligence (AI) over the past year or so can seem pretty magical.
But it’s essential that we all remember: AI is not magic. AI is data.
We live in an algorithm-driven world, full of promises about efficiency and innovation. But any data- and algorithm-driven scenario carries the hidden risk of bias. The term algorithmic bias, also known as machine learning or AI bias, refers to the ways systems can unintentionally perpetuate societal prejudices such as those based on race, gender, or economic status. If we want to deliver on the promise of an AI heaven rather than hell, we need to understand and fight algorithmic bias in all its forms.
Understanding the Impact of Algorithmic Bias (AI Bias)
What forms might those be, you ask?
Imagine a world where loan applications are denied, job applications are filtered out, and critical healthcare decisions are made based on biased algorithms. This isn’t science fiction – those are all very real consequences of unchecked algorithmic bias. For example, a 2019 study revealed significant racial bias in healthcare algorithms, leading to disparities in treatment recommendations based on patients’ ethnicities.
AI systems are increasingly used in decision-making processes across industries, from hiring and loan approvals to criminal justice and social services. When bias is baked into these systems, it can have a ripple effect, limiting opportunities for marginalized communities, reinforcing harmful stereotypes, and deepening social divides.
For example, a biased AI-powered resume screener might automatically reject applications from candidates with ethnic-sounding names or from certain neighborhoods, unfairly limiting their chances of getting a job interview. In the healthcare sector, biased algorithms can lead to misdiagnoses or unequal access to treatment for certain patient groups. These are just a few examples of how algorithmic bias can have serious consequences for people’s lives and livelihoods. We need to prioritize ethical AI development to ensure that this powerful technology is used for good, not to perpetuate existing injustices.
The Origins of Algorithmic Bias (AI Bias)
Algorithmic bias is a complex issue with numerous contributing factors:
- Biased data: AI learns from the data it’s fed. If that data reflects historical biases (for example, images predominantly featuring white faces, or resumes primarily from affluent areas), the AI will learn those biases too. This can be a problem in many areas – for example, facial recognition software trained on a dataset that mostly features faces of one ethnicity may struggle to accurately recognize faces from other
ethnicities. Similarly, an AI-powered loan approval system trained on data from a time period when housing discrimination was common may be more likely to deny loans to applicants from certain neighborhoods.
- Human bias: Even well-intentioned developers can unconsciously build their own biases into an AI system’s design. Our own life experiences, cultural background, and even seemingly innocuous assumptions can influence how we design and train AI
systems. For example, a team of developers who are all from similar socioeconomic backgrounds might not consider how their own financial experiences might bias a loan approval AI system.
- The “black box” problem: Some AI systems are so complex that it’s difficult to understand why they make certain decisions, making bias even harder to spot. These complex algorithms can be like opaque boxes – we feed them data, and they output a decision, but the internal workings of the system can be a mystery. This lack of transparency makes it challenging to identify and address potential biases within the system.
The Importance of Data Diversity
Tackling this problem requires a multi-pronged approach, starting with data diversity. Data diversity is paramount for training AI algorithms to excel in a wide range of real-world situations. It is not merely about the quantity of data but the quality and diversity that play a pivotal role in the success of AI models. Diverse datasets enable AI algorithms to be more versatile and robust, allowing them to perform effectively across different demographics and settings.
It’s essential to use datasets that represent the full range of people an AI system will interact with. This can be challenging, as historically underrepresented groups may not have been well-documented in data collection efforts. However, there are several strategies that can help:
Targeted data collection
Focus on gathering data from underrepresented groups to ensure the AI system is exposed to a wider range of experiences. This could involve partnering with community organizations or conducting outreach programs to collect data from specific demographics.
Data augmentation techniques
Techniques like image flipping, rotation, and color space manipulation can be used to artificially expand datasets and introduce variations that reflect real-world diversity. For example, in facial recognition technology, data augmentation can help ensure the AI system can accurately recognize faces from a wider range of ethnicities, skin tones, and facial features.
Synthetic data generation
In some cases, it may be possible to create synthetic data that accurately represents underrepresented groups. This can be a powerful tool for addressing data gaps, but it’s important to ensure the synthetic data is realistic and unbiased. Techniques like generative adversarial networks (GANs) can be used to create synthetic data, but careful attention needs to be paid to avoid inheriting biases from the training data used to create the GAN model.
More Strategies to Combat Algorithmic Bias
Improving data diversity when training AI models is essential, but it’s far from the only step we can take.
Inclusion in development
Diverse teams building AI systems are better equipped to identify and address potential biases during development. This means not just including people from different racial or ethnic backgrounds, but also incorporating a range of perspectives from gender, socioeconomic status, sexual orientation, and other factors. A team with a variety of viewpoints is more likely to spot potential biases and develop more inclusive AI solutions.
Explainable AI (XAI)
If we can understand the logic behind an AI’s decisions, we have a better chance of finding and correcting biases. XAI techniques can help make AI systems more transparent, allowing developers and auditors to see how the system arrives at its conclusions. This can be crucial for identifying and mitigating bias, as well as ensuring fairness and accountability in AI decision-making.
Governance and regulation
Clear ethical guidelines and regulations are needed to ensure AI development prioritizes fairness and accountability. This could include regular bias audits for high-stakes systems, as well as mandatory reporting of bias incidents. Regulatory bodies also have a role to play in developing standards for data collection, use, and storage that promote fairness and non-discrimination in AI development.
The Ongoing Challenge of Ethical AI
Building unbiased AI is an ongoing challenge, but it’s a fight we can’t afford to lose. The potential benefits of AI are enormous, with the potential to revolutionize medicine, education, scientific discovery, and more. But if those benefits are only enjoyed by a privileged few, the social and economic consequences could be dire. Imagine a world where AI automates existing inequalities, creating a stratified society where opportunities and resources are concentrated in the hands of those who have already benefited the most.
By focusing on diversity, transparency, and ethical standards, we can start using AI as a force for positive change, not a tool that perpetuates old problems under a shiny new surface. An example can be seen in the financial industry, where diverse teams working on AI-powered credit scoring models can help identify and rectify biases that may affect loan approvals for marginalized communities.
This means not just mitigating bias in the algorithms themselves, but also fostering a culture of awareness and accountability within the tech industry. Incorporating diverse voices and data in the AI development process can contribute to the creation of more ethical and unbiased AI systems that benefit society as a whole.
Read more AI articles by Chris Moschovitis:
AI Governance: A Practical Guide for Business Leaders
I am certified in Cybersecurity (CSX, CISM), Enterprise IT Governance (CGEIT), Data Privacy Solutions Engineering (CDPSE), and as a Certified Information Privacy Professional (CIPP/US). I am also an active member of organizations including ISACA, IAPP, and ISSA. In 2018, my book Cybersecurity Program Development for Business: The Essential Planning Guide was published by Wiley to critical acclaim. My second book, Privacy, Regulations, and Cybersecurity: The Essential Planning Guide, received an equally positive reception upon its release by Wiley in 2021. Additionally, I co-authored History of the Internet: 1843 to the Present and contributed to the Encyclopedia of Computers and Computer History as well as the Encyclopedia of New Media.
