"Garbage In, Garbage Out": AI's Data Bias Problem

The term “garbage in, garbage out” rings especially true in the world of AI. Data biases are known to adversely affect models, leading to inaccurate, incorrect, or unfair outputs. This in turn erodes trust, amplifies existing biases, and ultimately limits real-world adoption. 

What are the Primary Types of AI Bias?

• Cognitive bias: Human judgement errors that can seep into and influence AI systems. 

• Confirmation bias: Favors information that supports or confirms preexisting beliefs, consciously or unconsciously.

• Exclusion bias: Arises when relevant populations or groups are left out of a dataset.

• Historical bias: Reflects outdated societal norms or inequalities embedded in historical data.

• Implicit bias: Subconscious stereotypes or prejudices affecting data collection or quality.

• Measurement bias: Inaccuracies introduced by flawed methods of collecting or labeling data.

• Reporting bias: Introduced when certain outcomes or events are more likely to be reported or emphasized than others.

• Selection bias: Results from training data that is not representative of the broader population or intended use case.

• Deployment bias: Occurs when an AI model is applied in real-world settings that differ from the conditions it was trained or validated in, leading to poor performance or unintended consequences.

Real-World Examples of AI Bias

Even the most well-designed models can fail when built on flawed foundations. Here are three recent cases demonstrating the effects of AI bias:

Voice Assistants and Accent Discrimination

AI-powered voice assistants consistently underperform for speakers with diverse accents and dialects – particularly those of women and people of color. A study investigating speech recognition systems from several major tech companies found an average word error rate of 0.35 for black speakers compared to 0.19 for white speakers, exposing a gap in basic functionality. 

Gender Bias in Liver Disease Screening Tools

Four AI models designed to detect liver disease appeared over 70% accurate when assessing patients. But upon recreating the study, researchers found the models missed 44% of cases among women compared to just 23% among men. The two models with the highest overall accuracy also had the widest gender gap, revealing how surface-level performance can mask deeper bias.

Deepfake Detection and Error Disparities

Even deepfake detection technology designed to safeguard against digital manipulation shows disparities in performance. Research conducted by University of Buffalo researchers demonstrated that deepfake detection algorithms had higher error rates when analyzing videos of darker-skinned individuals compared to lighter-skinned ones (up to a 10.7% difference). This discrepancy raises concerns about the technology’s reliability and its potential to disproportionately misidentify or overlook harmful content targeting minority communities.

The Aris Solution: Mitigating Biases with High-Quality, Representative Datasets

AI is, at its core, a reflection of the data it is trained on. The massive datasets used to train models reflect the biases, inequalities, and imperfections present in human society. If companies fail to train models on fresh, representative datasets, these biases will persist as AI systems continue to be implemented in our everyday lives.

This is where Aris comes in. Aris provides AI companies with human-generated, high-quality, multimodal datasets that support the mitigation of algorithmic bias. Aris’ ecosystem leverages a suite of mobile apps powered by a global network of users to collect a wide range of demographically and geographically representative data. By empowering AI companies to source balanced, representative, and high-quality data, Aris ensures models can reflect the complexity of the world they aim to serve.

Ensuring fairness and equity in AI models is a necessity for building trustworthy AI. At Aris, we’ve helped leading AI companies improve their models by supplying diverse and representative datasets that reduce bias. Explore our case studies to see how Aris is setting a new standard for AI accountability and responsibility.