We are thrilled to announce the launch of the Islamic Bias Benchmark (IBB), an ambitious new initiative designed to evaluate and improve AI models' knowledge of Islam. The IBB aims to provide a comprehensive suite of benchmarks to assess and address biases within AI systems and also test their knowledge, including Large Language Models (LLMs) and Image Generation Models.
This initiative seeks to offer constructive insights to help identify areas for improvement, fostering more accurate and balanced representations of Islamic perspectives across AI models. Our vision is to establish a standardized, industry-wide benchmark that will guide the development of models better informed about Islam. While this is a bold and far-reaching goal, we are confident in our ability to make it a reality.
The IBB will be open-source, inviting collaboration from respected institutions, scholars, and experts to ensure the benchmarks are comprehensive and impactful. By addressing and mitigating biases in AI, we hope to contribute meaningfully to the broader discourse around fairness in AI technology, ensuring that Islamic perspectives are represented with the nuance and respect they deserve.
AI models, like human learners, can be evaluated based on their performance in specific tasks or subjects. These evaluations can be divided into two categories, each with its own advantages and limitations:
Evaluations requiring manual review: These assessments involve human effort to evaluate the model’s responses. While they allow for more nuanced understanding—especially in complex or open-ended tasks—they can be time-consuming, subjective, and difficult to scale.
Evaluations that can be automatically checked for correctness: In these evaluations, responses can be verified automatically without human intervention, making them more efficient and scalable. However, they may miss the subtleties and depth required for more complex tasks. A common example of this is multiple-choice questions.
Each method has its strengths and shortcomings. Given our goal of building a widely adopted, easy-to-use benchmark, we believe the second approach—automated evaluations—best suits our needs. Multiple-choice questions are especially effective in this context because they are fast, easy to evaluate, and straightforward. While we may explore better approaches in the future, this decision aligns with current best practices in AI benchmarking.
As we embark on this journey, we warmly welcome your ideas, suggestions, and feedback. Together, we believe we can make a lasting contribution to this critical space. This is just the beginning, but with your support, we are confident that we can bring this vision to life.