Want to avoid bias in LLMs? Here are 4 strategies you need to implement.

Large language models (LLMs) do their part to make many AI applications work as accurately and reliable as possible. Yet, the concern of bias may still arise – which can be detrimental to LLMs and AI as a whole. We will be taking a look at these four must-do strategies to tackle bias now and going forward.

While not all models are immune to bias, reducing the risk of unreliability because of it will be paramount. The sooner the strategies we’ll discuss are implemented, the better. Let’s begin right now.

Understanding Bias in LLMs

Bias can be found across various forms such as gender, racial, and socio-economic biases among others. The source of this will likely be training data that was initially introduced to them, which may feature documents and other data sources with out of date information (which may contain historical inequalities or skewed representations). As such, it is important to make sure that such bias is rooted out of the data ensuring that AI models are accurate, up-to-date, and free of the bias that may otherwise marginalize users or groups of people.

Identifying Sources of Bias

Identifying the source of biased data will be the first important step. The data collection process can play a role in LLM bias since the datasets could be an overrepresentation of demographics and viewpoints that may be long outdated. At the same time, data preprocessing and model training techniques may each play a role in bias.

Bear in mind that data is not the only thing linked to bias. It can also include the models and how they are designed, trained, and deployed.

Impact of Bias on Model Performance

It’s clear that bias can play an adverse role in LLM performance. When the outcomes are unfair or discriminatory, that’s a major sign of bias being present inside the model. Furthermore, it will be able to perpetuate stereotypes or even negatively label the nuances of different cultures and languages.

Strategy 1: Diversifying Training Data

The initial strategy to prevent bias in large language models is to ensure that the training data is diverse and representative. Metrics for the coverage of sources, groups, and viewpoints should be defined. Would your model be better if it had been trained with more data collected from among women, for example? Of course.

Expanding Data Sources

Diversifying training data begins with the ability to expand the sources where data can be collected. To do this, make sure that the data is geographically and culturally diverse. This way, they can be included in the datasets that LLMs use for more accurate and fair outputs. Plus, it can create content that will be equitable and inclusive to users.

Active Inclusion of Underrepresented Groups

It won’t suffice just to broaden data sources. We must be proactive in incorporating data from historically underrepresented groups. This means partnering with organizations that are able to connect us with these communities and collect the necessary data.

Utilizing Advanced Data Augmentation Techniques

An alternative method to pursue is advanced data augmentation techniques. The aim of these methods is to improve training data diversity, which very much includes the use of synthetic data generation. This is a useful tool because it creates new data points within existing samples.

Strategy 2: Implementing Bias Detection and Correction Techniques

The unbiased training of artificial intelligence requires both a dataset free of bias and means of detecting bias that may inadvertently be introduced during the training process. Achieving both of these conditions demands much work from those who would create AI with equity in mind. Diverse datasets are a good start. But two techniques in particular—preprocessing and in-processing—represent the main methods by which today’s AI creators hope to achieve bias-free outcomes.

Pre-processing for Bias Mitigation

When it comes to preparing for training, overlooking data preprocessing is a common mistake. Bias in data can be detrimental to decision-making, especially in cases where a decision seriously impacts individuals. We can use data augmentation to create a more diverse dataset, bringing us closer to obtaining a representative sample of the population. We must also filter our dataset for explicit bias and any harmful content that could lead to poor decisions down the road.

In-model Adjustments

Mitigating bias is a multifaceted problem. Even if we ensure that the training data is sufficiently representative, and the model architectures are sound, and we eliminate human bias from the model training process, bias can still be introduced during inference. We are often left with a degree of freedom to make inferences that can be seen as a biasing choice.

Ensuring Fairness in Model Evaluation

Mitigating bias for the benefit of fairness will need to be addressed in LLM evaluations. This will include the development of evaluation metrics so they account for various demographic groups while spotting any potential biases that may appear in the model’s output.

Strategy 3: Continuous Monitoring and Evaluation

LLMs need to be monitored and evaluated regularly—for bias, among other things. One reason for this is that avoiding bias in LLMs is not just a one-and-done proposition. You can’t just throw a bunch of diverse data at a model and expect everything to be fine once you’re done. You need to keep checking, over time, to see if diverse data are still part of the model’s training and also if the model is still functioning as diversified as possible.

Establishing Evaluation Metrics

It will be important to have evaluation metrics that are clear and comprehensive when making certain that Large Language Models (LLMs) are fair. These metrics must be easy to understand and apply, especially when the data used to measure the models’ fairness is collected across various demographic groups and different hypothetical scenarios (a.k.a. “tests”).

Iterative Improvement Process

Monitoring and evaluation should feed into an iterative process of improvement. Findings from the evaluation phase should inform adjustments to the training data, model architecture, and training process to continually reduce bias.

This iterative approach ensures that efforts to combat bias are ongoing, adapting to new data and changing societal norms.

Utilizing User Feedback for Continuous Improvement

In addition to formal monitoring processes, incorporating user feedback into the bias mitigation strategy can provide valuable insights into the real-world impact of the LLM’s outputs. By soliciting feedback from a diverse group of users, developers can identify areas where bias may still exist and make targeted improvements to enhance the model’s fairness.

Engaging with users in this way not only improves the LLM’s performance but also fosters trust and transparency in the development process.

Strategy 4: Fostering Transparency and Accountability

Last but not least, transparency and accountability are key to building trust in AI systems and ensuring they are used responsibly. This involves clear documentation of the data sources, training processes, and methodologies used to mitigate bias.

Documentation and Openness

Comprehensive documentation provides insight into the decisions and processes behind the model’s development. This transparency is crucial for identifying potential sources of bias and understanding how they have been addressed.

Openness about the limitations and uncertainties of the model also fosters a culture of continuous improvement and responsible AI use.

Engaging with Stakeholders

Stakeholder engagement is critical in this strategy. It would also be best for you to get users, ethicists, and affected communities involved in this as well. Feedback will be essential to ensure that LLMs are free from as much bias as possible. While challenging, allowing more voices to make suggestions and conversations will be important to the ongoing LLM developments that need to be made.

Stakeholders want to see an LLM model that is trustworthy because of less bias. The same applies to the affected communities and other users.