38.6% of AI facts contain bias. 90% of training data comes from Europe/North America. Discover how democratized data collection creates fairer AI
AI is everywhere. It’s solving problems, creating efficiencies, and transforming industries. But the problem is that it’s often built on biased data. When the data used to train AI models is incomplete or one-sided, the results are too. This means AI can unintentionally make decisions that unfairly impact certain groups of people.
The true cost of biased AI is the potential perpetuation of inequality and the harm it causes to communities. Understanding the impact of biased training data is crucial for building AI that serves everyone equally fairly.
Let’s take a closer look at why this matters and how we can tackle the hidden costs of biased AI.
AI models are only as good as the data they’re trained on, and much of that data is flawed. Research from USC shows that up to 38.6% of AI facts can be biased, depending on the dataset.
“We studied different groups from categories like religion, gender, race, and profession to see if the data was favoring or disfavoring them, and found out that, yes, indeed, there are severe cases of prejudice and biases.” — Ninareh Mehrabi ( A Ph.D. candidate at USC-ISI who worked on the project)
When the data used to train AI is not diverse and fair, the AI becomes biased, leading to inaccurate results and unfair outcomes. For example, it could misdiagnose patients when used in healthcare. In recruitment, it could overlook qualified candidates..
To build effective AI, we must ensure the data represents everyone, not just a narrow group. AI trained on diverse data produces better and fairer outcomes.
AI bias is already shaping the decisions that affect people’s lives. In 2019, a health care algorithm used to determine which patients need additional attention was found to favor white patients over black patients despite black patients being sicker with more chronic conditions.
The algorithm used healthcare costs as a metric to determine who needed extra care. However, since black patients spend less on healthcare due to access issues, the system wrongly assumed they were healthier.
Rather than being just small glitches, these are life-changing issues. And the cost is real: wasted opportunities, unequal treatment, and entrenched inequality.
“If you build those biases into the algorithms and don’t deal with it, you’re going to make those biases more pervasive and more systematic, and people won’t even know where they are coming from.” — Ashish Jha, Director of the Harvard Global Health Institute.
Ignoring bias in AI makes it worse over time. To fix this, we need to train AI with data that is fair and represents everyone. Without it, AI will continue to reinforce unfair systems.
Today, AI is often built on data from just a few regions, mostly the U.S. and Western Europe. Recent data shows that over 90% of the data used to train AI comes from Europe and North America, with less than 4% from Africa. This is a problem because the world is diverse and filled with different cultures, experiences, and viewpoints that AI currently misses out on.
“The next Albert Einstein, the next Martin Luther King, is sitting out there in the world somewhere right now. We don't know where they are.” — J.D. Seraphine (Founder & CEO at Raiinmaker)
This is a powerful reminder that the future of AI needs to include perspectives from all corners of the globe. When AI models are trained with data from only a small group, they miss out on the richness of humanity and the wide range of experiences that shape our world.
Biased AI costs businesses. Misleading healthcare predictions, unfair hiring practices, and inaccurate legal decisions all stem from poor data. But the biggest problem is inefficiency.
When AI models are trained with bad data, companies waste time and money fixing issues that should have been caught earlier. The market moves fast. If you’re not working with the right data, you’re falling behind.
Good AI starts with good data. The solution needs to be efficient, cost-effective, and fair.
The solution to biased AI goes beyond just acknowledging the problem. We must adopt systematic approaches that democratize data collection and validation.
Decentralized, human-powered platforms offer one promising approach, providing broader representation in AI training data while creating economic opportunities for contributors.
Effective AI requires diverse inputs from a global population. The most promising approaches to this challenge incorporate several key elements:
Platforms like Raiinmaker are already implementing these principles by enabling smartphone-based contributions to AI training through tagging images, validating data, and providing feedback on model outputs.
For decentralized AI training to succeed, reputation systems must become more sophisticated and transparent. Effective reputation frameworks include:
These systems allow platforms to balance open participation with quality control, ensuring that democratized data collection doesn't sacrifice accuracy.
Developing more representative AI requires ongoing innovation in several areas:
Continued development of lightweight validation tools that can run on low-cost devices will be essential for truly global participation in AI training.
Sustainable systems must fairly compensate contributors while remaining economically viable for AI developers, particularly those with limited resources.
Methods for identifying and addressing cultural blind spots must evolve to detect subtle forms of bias that may not be immediately apparent to outsiders.
Building understanding of AI bias should become part of educational curricula, creating a more informed global population that can participate meaningfully in AI development.
By creating frameworks that diversify contributions, ensure transparency, and measure impact, we can develop AI that truly reflects humanity's full spectrum. The best AI systems will emerge when everyone has a voice in their creation.
Raiinmaker embodies this approach through a decentralized, human-powered platform designed to give everyone a seat at the table. Instead of relying on narrow data sources, we’re tapping into a global network of users to provide real-world, diverse input to train AI models.
Download the Raiinmaker app today and see how we’re building a fairer, more inclusive AI for everyone.