Addressing AI Bias: Real-World Challenges and How to Solve Them

Artificial Intelligence (AI) has become a cornerstone technology in everything from healthcare and finance to tech and criminal justice. Yet, it’s not a perfect solution.

The more we lean on AI to help with decision-making, the more we need to address the elephant in the room: AI bias.

AI bias isn’t a technical glitch—it’s a real-world problem that can perpetuate inequality and lead to unfair outcomes. And it’s not something we can just brush under the rug. AI bias is something we need to consider with everything from software development to simple search queries.

Below, we’ll explore the challenges of AI bias and what we can do to create fairer, more ethical AI systems that truly serve all users.

What is AI bias?

AI bias occurs when an artificial intelligence system produces systematically prejudiced results due to erroneous assumptions in the machine learning algorithms. This AI bias affects the fairness and accuracy of AI-driven decisions and answers.

Ultimately, machine learning bias mirrors (and perpetuates) human biases because AI systems learn from data that often contains these biases (as well as prompts).

Examples of AI bias

AI systems, while powerful and increasingly prevalent, are not immune to biases that mirror and often magnify human prejudices. The following examples shed light on how AI bias can emerge across different sectors and applications:

Hiring algorithms

One well-known example is AI systems used in hiring processes. In 2018, Amazon had to scrap its AI recruiting tool after it found that the tool favored male candidates. The AI tool was trained on resumes submitted over ten years—a period when the tech industry was predominantly male.

Consequently, the AI system learned to prefer resumes that resembled past successful (mostly male) candidates, perpetuating gender bias in hiring.

Criminal justice systems

AI bias has also been observed in the criminal justice system. For instance, investigators found the COMPAS algorithm (used to predict the likelihood of reoffending) to have racial bias. They found that the system was more likely to falsely flag Black defendants as future criminals while more frequently incorrectly labeling white defendants as low-risk.

Causes of AI bias

Finding a solution often involves digging deep into the roots to find the cause. With AI algorithm bias, there’s not a single component to blame—there’s typically three:

1. Training data: One of the primary causes of AI bias is the data used to train these systems. AI systems learn from historical data, which often contains existing biases. For example, if an AI model is trained on data that reflects a biased society (such as fewer women in senior positions), the model is likely to perpetuate those biases and possibly experience AI hallucinations.

2. Algorithm design: Bias can creep in through the choice of features that the AI algorithms use to make decisions or by weighting different variables. For example, if an algorithm is designed to prioritize certain attributes that correlate with bias (like zip codes in loan approval processes that might correlate with race), it can inadvertently perpetuate discrimination.

3. Human oversight: While AI systems can process large amounts of data quickly, they lack the ability to understand the broader context or ethical implications of their decisions. A human in the loop can provide the necessary judgment to catch and correct biases that AI might miss. However, if the human oversight is biased (even if it’s just unconscious bias), the problem still remains.

Why you should care about AI bias

Artificial intelligence bias isn’t just a technical issue—it has real-world consequences that can significantly impact individuals and society. Here’s why you should care as you leverage artificial intelligence to grow your business:

• Fairness: Biased data in AI systems can perpetuate existing inequalities and unfairly impact marginalized communities.

• Credibility: Bias in AI erodes public trust and hinders the adoption of beneficial AI technologies.

• Legal responsibilities: Using biased AI systems can lead to legal penalties and damage your company’s reputation.

• Business impact: Biased AI can result in poor decisions that negatively affect business outcomes and customer satisfaction.

• Social responsibility: There’s a moral obligation to build AI technologies that benefit society and promote fairness.

5 strategies to mitigate AI bias

Just because AI isn’t perfect yet doesn’t mean we shouldn’t use it. Instead, we need to learn how to mitigate AI bias and make these systems more fair and ethical for all.

And that’s the responsibility of software developers and end users.

Here’s what you can do to minimize bias:

1. Diverse data collection

AI systems are better equipped to make fair and accurate decisions when your training data includes a wide range of scenarios and demographic groups. Use diverse data sets to help your AI models not favor one group over another.

Regularly update your datasets to reflect changes in society and avoid outdated biases.

2. Bias testing

Regular bias testing involves evaluating AI systems against known benchmarks to detect disparities in outcomes across different demographic groups. These tests can highlight areas where the AI system might unfairly favor or discriminate against certain groups.

AI software testing can include fairness metrics and adversarial testing to find and address biases. Developers can use results from these tests to make necessary tweaks and adjustments.

3. Human oversight

While AI can process vast amounts of data quickly, it lacks the nuanced understanding that humans bring. Human reviewers can catch biases that AI might miss and provide context that AI systems lack.

This oversight can involve regular audits, reviews of AI decisions, and incorporating feedback from diverse stakeholders to guarantee the AI system aligns with ethical standards.

4. Algorithmic fairness techniques

Implementing algorithmic fairness techniques can significantly reduce AI bias. For example, counterfactual fairness adjusts the algorithm to guarantee decisions will remain the same even if the sensitive attributes (like race, gender, or economic status) are different.

Other techniques include:

• Re-weighting data to balance representation: This technique involves adjusting the weights of data points to fairly represent underrepresented groups in the training process. This helps the AI model to learn from a more balanced dataset and reduces bias towards any particular group​.

• Using fairness constraints in optimization processes: Fairness constraints are added to the optimization algorithms used to train AI models to help outcomes meet specific fairness criteria. This approach aims to produce models that make equitable decisions across different demographic groups.

• Using differential privacy: Differential privacy techniques protect individual data while maintaining overall dataset utility. Adding noise to the data or using other privacy-preserving methods helps the AI model learn from the data without compromising individuals’ privacy.

5. Transparency and accountability

This involves making the AI decision-making process clear and understandable to users. Provide detailed documentation about how AI models are trained, the data used, and the decision logic—this helps stakeholders understand and trust the AI system.

Examples of combatting AI bias

Some organizations are already doing their part to battle AI bias, but it will continue to be an uphill fight as large language models (LLMs) consume more data.

Here are a few examples of combatting AI bias—these examples highlight the proactive steps taken by various organizations to combat AI bias. See which practices you can adopt to help build more equitable and trustworthy AI systems.

• IBM’s AI Fairness 360 toolkit: IBM developed the AI Fairness 360 (AIF360) toolkit—an open-source library that includes metrics to check for biases in datasets and machine learning models. It also provides algorithms to mitigate these biases. This toolkit supports developers in building fairer AI systems by offering practical tools to identify and reduce bias throughout the AI development lifecycle.

• Microsoft’s Fairlearn: Microsoft has developed Fairlearn, an open-source toolkit for assessing and improving the fairness of AI models. Fairlearn provides fairness metrics and mitigation algorithms to help developers understand and mitigate bias in their models.

• Partnership on AI’s fairness, transparency, and accountability initiative: The Partnership on AI—a consortium of leading technology companies, academic institutions, and NGOs—has launched initiatives focused on fairness, transparency, and accountability in AI. It conducts research, publishes guidelines, and promotes collaboration to address AI bias and promote ethical AI development.

• MIT Media Lab’s Algorithmic Justice League: The Algorithmic Justice League advocates for AI accountability. They conduct research, raise awareness about AI bias, and collaborate with policymakers and industry leaders to develop standards and practices that promote fairness in AI systems.

Accelerate your AI projects with DigitalOcean GPU Droplets

Unlock the power of NVIDIA H100 GPUs for your AI and machine learning projects. DigitalOcean GPU Droplets offer on-demand access to high-performance computing resources, enabling developers, startups, and innovators to train models, process large datasets, and scale AI projects without complexity or large upfront investments

Key features:

• Powered by NVIDIA H100 GPUs with 640 Tensor Cores and 128 Ray Tracing Cores

• Flexible configurations from single-GPU to 8-GPU setups

• Pre-installed Python and Deep Learning software packages

• High-performance local boot and scratch disks included

Sign up today and unlock the possibilities of GPU Droplets. For custom solutions, larger GPU allocations, or reserved instances, contact our sales team to learn how DigitalOcean can power your most demanding AI/ML workloads.