4 Approaches to Building Fair and Unbiased AI Systems
In an age where artificial intelligence is transforming industries, the quest for fairness and impartiality in AI systems has never been more critical. Insights from a CEO and a Chief AI Officer provide invaluable perspectives on this pressing issue. The discussion opens with strategies to diversify data sources for fairness and concludes with methods to fine-tune models to avoid bias, encompassing a total of four expert insights. Discover how these leaders navigate the complexities of ethical AI development.
- Diversify Data Sources for Fairness
- Implement Guardrails for Ethical AI
- Ensure Data Quality and Transparency
- Fine-Tune Models to Avoid Bias
Diversify Data Sources for Fairness
When building AI systems, approaching fairness and bias requires a structured methodology that includes data collection, model training, and continuous evaluation. My approach starts with diversifying the data sources to ensure that the dataset is representative of various demographics and backgrounds. This helps mitigate any inherent biases present in the training data.
One notable project involved developing a predictive model for hiring decisions. During the data collection phase, we realized that the historical hiring data reflected biases, particularly favoring candidates from specific demographic backgrounds. To address this, we implemented techniques such as data augmentation to balance the representation of underrepresented groups and ensured that the features used in the model were relevant and unbiased.
Additionally, we employed fairness metrics to evaluate the model's performance across different demographic groups. For instance, we used techniques like disparate-impact analysis to identify any significant discrepancies in the model's predictions. Based on our findings, we iteratively adjusted the model and its underlying algorithms to improve fairness, ensuring that the hiring recommendations did not favor one group over another.
Ultimately, this project taught us the importance of transparency and accountability in AI development. By actively addressing potential biases and continuously evaluating our models, we created a more equitable system that not only improved our hiring process but also aligned with our organization's values of diversity and inclusion. This experience reinforced the need for ongoing vigilance in AI ethics and the importance of building systems that prioritize fairness from the ground up.
Implement Guardrails for Ethical AI
Large language models, like those used in AI applications, learn from massive amounts of unstructured data. This helps them imitate human language, adopt different viewpoints, and grasp subtle meanings. But one big challenge that comes with this method is AI bias.
AI bias happens when models produce results that reflect inaccurate or harmful stereotypes. For example, when early users of Stable Diffusion entered the prompt "Native American," it often generated images of people in traditional headdresses, even though not all Native Americans wear them. This reflects the biases in the data the model was trained on. AI won't question this on its own, which is why language model developers have to step in to make sure the results are fairer and more balanced.
A key solution is feeding models with diverse datasets and continuously checking for ethicality and neutrality. LM developers also adjust the system prompts directly, guiding AI to provide more diverse results when race or gender isn't specified in a query. But this approach isn't foolproof—sometimes it leads to historically inaccurate or contextually off results. It's more like painting over a problem without fixing the foundation.
Fortunately, we aren't limited to what AI creators give us. At Miquido, we use guardrails and evaluators to build AI systems that generate ethical, fair, and appropriate responses. When developing AI solutions, such as chatbots, guardrails ensure that no matter what a user asks, the AI won't return harmful or inappropriate answers.
Our custom framework, AI Kickstarter, allows us to include guardrails in all GenAI projects, especially where user interaction is involved. We take a two-step approach to implementing these guardrails.
Before the AI processes any user query, we run input filtering. This step analyzes the content of the query and removes anything harmful or manipulative. This way, users can't prompt the model to produce discriminatory or misleading content.
We also apply output filtering, reviewing the AI's responses to ensure they meet ethical, legal, and neutrality standards. If a response doesn't pass these checks, it's either rejected or corrected before being sent out.
By using this approach, we help companies protect their AI systems from generating biased or unethical results, ensuring they maintain a positive and inclusive user experience.
Ensure Data Quality and Transparency
Building AI systems that are fair and unbiased starts with a proactive approach at every stage of the development process. At PolymerHQ, we focus heavily on data quality and transparency. We know that biased outcomes are often the result of biased data inputs, so the first step is making sure that the data we use to train our models is representative and diverse. This includes actively seeking out data sources that reflect a wide range of scenarios, behaviors, and contexts, especially when dealing with sensitive issues like insider threats or data-loss prevention.
One example where we had to address potential biases came during the development of our system for detecting insider threats. Insider risk can vary greatly depending on the type of organization, job roles, and even regional practices. Early on, we noticed that our model was flagging a higher percentage of alerts for certain user groups, even though there was no concrete evidence of higher risk from those groups. This raised a red flag for us about potential bias in the data we were using.
To address this, we conducted a comprehensive audit of the dataset and the features our model was relying on. We discovered that certain behaviors common in specific departments or roles were being overrepresented in our training data, which skewed the results. For example, individuals in customer-support roles were frequently accessing sensitive information as part of their job, but this access was not inherently risky. We had to refine our feature selection to account for the context of data access, rather than just the frequency or volume.
By adjusting the model and retraining it with a more balanced and context-aware dataset, we significantly reduced false positives and ensured that the system was assessing risk fairly across all user groups. The lesson here is that building fair AI systems requires continuous monitoring, auditing, and adjustment. Biases can emerge in unexpected ways, so it's essential to remain vigilant and committed to fairness throughout the lifecycle of the AI system.
Fine-Tune Models to Avoid Bias
First, this is all about ongoing prompt engineering and fine-tuning the LLM you work with. We have an AI agent system called IONI that was initially developed as a customer-support AI chatbot, so we spent months fine-tuning our models (GPT-3.5 and later GPT-4, GPT-4o) to avoid bias in its answers.
Initially, our chatbot could provide absolutely inappropriate information about the related products or services, or provide overly general information taken from the web. However, our AI team has worked on this point day by day, trying to find the golden mean between the general answers and situations when the model can't answer at all due to the overly narrow views.
Second, this is all about the data you work with. We have already spent thousands of hours trying to ensure correct data distribution in our vector databases. We use mostly ChromaDB. It is crucially important how your data is decentralized into the embeddings and vectors. The better you set up this, the better your model (product) works.
