How To Avoid AI Data Bias?

We like to think of machines as being completely impartial, but the reality is that they’re typically as biased as the people programming them. The same biases that hamper human decision-making can also affect artificial intelligence and machine learning models. 

Although AI data bias starts in the training stage, it may not be recognized until the model is in production. Sometimes, bias results from deliberate actions, as may be the case with social media platform X’s algorithmic bias. More frequently, however, data may be unintentionally skewed by flawed or incomplete training data or biases baked into the language itself. 

This blog discusses four strategies to help identify and mitigate AI data bias at every stage of AI development and operation.

What is AI data bias?

AI bias occurs when model training data reflects human biases against particular groups and distorts outputs in potentially harmful ways. Machine learning data bias often originates during data collection and model training, for instance, from narrow datasets containing only information about white men.

Using biased training data leads to algorithmic bias that favors the majority group while discriminating against other underrepresented groups in the training dataset. For example, an AI-powered medical diagnostics solution trained primarily with data collected from white patients will be less accurate when detecting diseases in patients of other races. 

4 Strategies to mitigate AI data bias

The following strategies can help companies avoid and mitigate bias so they can be sure they’re using AI ethically.

1. Data selection

The best and earliest method for avoiding bias is to train models on high-quality data that accurately represent the real-world scenarios they’ll face in production. Brands may employ several techniques for improving the quality of AI training data, including:

• Exploratory data analysis (EDA) - Using visualizations to analyze training data and identify potential bias before ingestion by the model. 
• Rebalancing - Adding or removing data points to fix imbalanced datasets.
• Noise reduction - Identifying and eliminating low-relevance or irrelevant data points that could skew model results.

2. Bias detection tools

Bias detection tools like Granica Screen scan large language model (LLM) outputs for statements that indicate the presence of bias. Screen covers various forms of bias comprehensively by using a granular taxonomy to increase detection accuracy over alternative solutions. 

3. Human-in-the-loop (HITL) 

HITL involves having a human evaluate a machine learning model’s decisions to ensure they’re accurate, ethical, and free of bias. It can help catch issues that automated tools miss and prevent them from having any real-world consequences, which is critical for AI applications used in fields like healthcare, housing, and recruiting. 

4. Real-world monitoring and testing

Bias detection tools and HITL should also be included as parts of an ongoing monitoring and testing strategy to catch any hint of bias in model outputs and prevent drift. Such a program is critical because AI models continue learning from inputs long after the training stage ends. It’s also important to test models regularly using known benchmarks to validate responses to potential bias triggers. 

AI & ML data bias mitigation with Granica Screen

Granica is an AI privacy and safety solution that helps organizations develop and use AI ethically. 

The Granica Screen “Safe Room for AI” detects sensitive, biased, and toxic content in tabular and natural language processing (NLP) data during training, fine-tuning, inference, and retrieval augmented generation (RAG).

Granica Signal is a model-aware data selection and refinement solution that helps reduce noise in AI & ML datasets. It automatically detects and corrects imbalances in datasets to help curate well-distributed, representative data samples, resulting in fair, unbiased AI outcomes.