What are the most effective ways to fight AI bias? This question continues to loom large amid ongoing reports of bias creeping into business applications of AI. You’ve probably already heard a handful of horror stories about GenAI hallucinations, prompt hacking, and chatbot jailbreaking—but let’s dive into the finer details.

For instance, a new study from the University of Washington indicates that large language models (LLMs) used for recruitment are hampered by racial and gender bias. And research from Rutgers-Newark reports that AI used more to diagnose and treat patients may contain biases and blind spots that could hinder healthcare outcomes for black and Latino patients.

That’s the bad news. The good news is that organizations are discovering more effective ways to fight AI bias—and it all comes down to data.

AI bias can damage your business

Eliminating bias is one of the key tenets of practicing safe AI. To serve your business well, AI needs to be responsible. And for AI to be responsible, you’ll need to minimize its bias. AI bias potentially marginalizes and penalizes large segments of the global population—which can harm its users and damage your business’s reputation.

For instance, biased hiring algorithms can generate negative publicity, leading to a loss of trust from both customers and the public. This unfortunate fact has been routinely validated, like when a high-tech firm’s AI hiring tool was found to discriminate against female candidates.

AI bias can also create unacceptable legal and regulatory risks for your business. resulting lawsuits and regulatory penalties. Or it could alienate your customers with subpar customer service. In fact, a survey of more than 350 technologists found that more than half of the companies affected by AI bias reported losing revenue (62%) and customers (61%).

AI bias can hurt your business in more subtle ways, too. Bias in AI systems can prevent your business from reaching a diverse customer base. If an AI system is trained on data that overrepresents certain demographic groups while neglecting others, it may fail to accurately predict the needs of underrepresented populations. This leads to missed opportunities for growth in untapped markets.

But when your AI systems mitigate bias effectively, your business serves the needs of all your customers, which makes your business more inclusive. And inclusive AI delivers measurable value by solving for many such issues.

Focus on data to fight AI bias

So, how does AI bias happen, and why is it so difficult to stop? The answers are complicated, but they almost all have to do with data. As in, biased data.

Bias can creep in at multiple stages, from how data is collected to how it is processed and labeled, often reflecting systemic inequities in society. Once entrenched, these biases can propagate across AI systems, perpetuating stereotypes or further disenfranchising minority demographics.

Data used for training can be biased

AI systems learn from the data they are trained on. If that data is biased or unrepresentative, the AI will replicate and potentially amplify those biases. For instance, facial recognition systems trained predominantly on images of white males have been shown to perform poorly on women and people of color.

Similarly, hiring algorithms trained on historical data from male-dominated industries may favor male candidates over female ones.

Human bias can affect data labeling

In supervised machine learning, humans often label the data used to train models. Even well-intentioned individuals may introduce unconscious biases into this process. For example, search engines have been found to perpetuate stereotypes by associating certain terms with specific demographics.

Biased data is difficult to stop

Since AI relies on historical data that often reflects societal inequalities, it’s challenging to remove bias completely without fundamentally altering the data itself.

This is especially true when the biases are subtle or deeply ingrained in the data. For example, removing all biased elements from large datasets can be extremely complex and labor-intensive.

De-biasing datasets is exceptionally difficult because it requires identifying and correcting all forms of bias without losing valuable information or introducing new biases. And some biases are so subtle that they can go unnoticed until after the model has been deployed.

But this doesn’t mean it’s impossible for companies to fight bias in data. On the contrary, knowing where bias is most likely to happen helps you explore more targeted approaches to fighting it.

You need a multi-pronged approach to fight AI bias

We suggest that you take a multi-pronged approach to fighting AI bias. Addressing AI bias requires more than one-size-fits-all solutions; it requires a blend of technical rigor and strategic oversight. Data, the lifeblood of AI systems, must be scrutinized, cleaned, and enhanced to avoid perpetuating inequities or blind spots.

A diverse team of certified data annotators is key

Sourcing data annotation talent through a diverse network of certified and vetted domain experts can help ensure that the training data your models consume is inclusive and representative. This helps mitigate biases that could arise from narrow or homogeneous datasets.

The key to success is quality over quantity. Data labeled by certified domain experts, rather than ordinary people, tends to capture more complex and context-specific information.

This versatility is especially important for LLMs, which need to understand subtle linguistic cues and domain-specific jargon. Expert annotators are better equipped to handle these complexities, helping to ensure that the labeled data reflects real-world scenarios more accurately.

While quantity-focused talent networks can provide volume, they often lack the depth of understanding required for complex tasks. Certified domain experts bring specialized knowledge that helps ensure higher accuracy and consistency in labeling. This is particularly important in fields like healthcare, law, or finance, where small errors in labeling can lead to significant consequences.

It’s important to put in place strong vetting mechanisms to make sure that you’re relying on people who possess the expertise they claim to know, just as you would vet job candidates in the hiring process.

Consider the value of synthetic data

You can generate synthetic data to include underrepresented groups, which helps address the limitations of traditional datasets. This approach is particularly useful for ensuring that AI models perform well across different demographic groups.

By generating artificial data that mimics the statistical properties of real-world data, synthetic data allows for the creation of diverse and controlled datasets that adequately represent marginalized groups. This can help reduce biases that arise from imbalanced datasets, such as those that overrepresent certain demographics while underrepresenting others.

For example, synthetic data can be used to augment datasets in healthcare where certain patient groups may be underrepresented, leading to more equitable AI models.

Keep humans in the loop

You need to keep people in the loop after you annotate data, too. That’s because AI models, as we’ve noted, make mistakes. In fact, you should plan on them making mistakes.

Even after the initial training phase, human oversight remains essential to monitor outputs, correct errors, and adapt the model to changing real-world conditions. Human reviewers can identify areas where the model may still produce incorrect or biased responses and provide feedback for further fine-tuning.

For example, consider a company that uses an LLM to assist with the initial screening of job applicants by analyzing resumes and ranking candidates based on qualifications. After deployment, HR managers might notice that the AI system disproportionately ranks male candidates higher than female candidates for technical roles, even when qualifications are similar.

In this example, HR professionals might manually review the AI’s rankings to identify patterns of gender bias. They might compare the AI’s recommendations with their own assessments of candidate qualifications. Then the human reviewers would flag instances where qualified female candidates were ranked lower than they should have been.

They would also give feedback to the model, adjusting the weights assigned to certain features (e.g., removing unnecessary emphasis on irrelevant keywords more commonly associated with male candidates).

You could then use the flagged cases to retrain the model so that the model learns from these corrections and reduces its reliance on biased patterns in future rankings.

A frontier AI data foundry platform can help

A frontier AI data foundry platform can play an important role in mitigating AI bias by providing high-quality, diverse, and inclusive datasets that help prevent biased outcomes in AI models. The Centific approach focuses on creating inclusive AI systems that incorporate a variety of inputs from different demographic, cultural, and linguistic backgrounds.

With Centific, AI models are trained on data that reflects the complexity of the real world, reducing the risk of perpetuating stereotypes or excluding certain groups.

Learn how you can fight AI bias with a frontier AI data foundry platform.