As AI systems become more integrated into our daily lives, ensuring they are fair and unbiased is crucial. However, identifying and addressing biases in these complex systems can be challenging. In this article, we propose a novel approach that combines both technical and non-technical solutions to help practitioners identify and mitigate biases in AI.
The Problem
Existing approaches to fairness in AI often focus solely on technical solutions, such as algorithmic checks and balances. While these tools are important, they overlook the human element of bias and how it can be addressed. Additionally, operationalizing fairness can be difficult, leading to a lack of clarity and consistency in implementing fairness practices throughout projects.
The Proposed Approach
To tackle these challenges, we propose an integrated approach that combines technical and non-technical solutions within a visual analytics system. This system, called FairCompass, guides practitioners through a human-centred design process that emphasizes understanding and reasoning around fairness. The framework consists of three stages: Exploration, Guidance, and Informed Analysis.
Exploration: In this stage, users explore their dataset to gain an overall understanding of its fairness characteristics. They use visual analytics tools to identify trends and patterns, allowing them to formulate hypotheses about potential biases.
Guidance: During the Guidance stage, users receive feedback on their findings and gain insight into how to address any identified biases. This stage also involves using fairness compasses, which are visual analogies that help practitioners understand and reason around fairness concepts. These compasses provide a structured framework for exploring and addressing biases.
Informed Analysis: In the final stage, users analyze their findings using the FairCompass toolset. This involves using the Subgroup Exploration Tab to investigate subgroups within the dataset and identify potential biases. The Fairness Compass Tab provides a visual representation of the fairness characteristics of the dataset, allowing users to evaluate and improve its fairness.
Benefits
Our proposed approach offers several benefits over existing solutions. Firstly, it addresses the problem of overemphasizing technical solutions by integrating non-technical approaches into the design process. Secondly, it provides a human-centred design process that emphasizes understanding and reasoning around fairness. Finally, it offers a structured framework for addressing biases in AI systems.
Conclusion
In conclusion, ensuring fairness and mitigating biases in AI is a complex challenge that requires a multifaceted approach. By combining both technical and non-technical solutions within a visual analytics system, we can streamline the bias auditing process and create more inclusive AI systems. Our proposed approach, FairCompass, offers a human-centred design process that emphasizes understanding and reasoning around fairness, making it easier for practitioners to identify and address biases in their AI systems.
