We're building tools to measure, monitor, and mitigate bias in AI-generated content. Our mission is to make AI more transparent, fair, and accountable.
Our research and tools address these critical issues in AI systems today
AI systems often perpetuate and amplify gender stereotypes in language processing, image generation, and decision-making algorithms.
Many AI models show significant disparities in accuracy and representation across different racial and ethnic groups.
Without proper safeguards, AI systems can generate or fail to detect harmful, toxic, or misleading content.
Many AI systems operate as 'black boxes,' making it difficult to understand how they reach decisions or identify potential biases.
We're developing cutting-edge tools to make AI more responsible, transparent, and beneficial for everyone.
A comprehensive metric that evaluates AI-generated content for bias, fairness, and ethical considerations. Get actionable insights to improve your AI systems.
A fine-tuned Mistral-7B LLM that serves as a cognitive guide and mentor rather than providing direct solutions. Designed to promote critical thinking.
We provide specialized AI services to help organizations implement responsible AI practices.
Combining governance advisory with rigorous audits, this service helps organizations implement and maintain ethical AI systems that comply with regulatory and societal standards.
Learn moreDeploying advanced tools like RAIL Score calculator to detect and correct biases in AI systems, ensuring fairness and enhancing the credibility of automated decisions.
Learn moreUtilizing AI to automate complex business processes while ensuring ethical decision-making, enhancing efficiency without sacrificing transparency or fairness.
Learn moreOffering a suite of Responsible AI services including AI-powered search, personalized user experiences, ethical recommendation systems, and AI chatbots, each designed to enhance user engagement while adhering to the highest standards of fairness and privacy.
Learn moreOur work is informed by leading research in the field of responsible AI. Explore these resources to learn more about fairness, transparency, and ethical AI development.
Liu et al., arXiv 2023
Comprehensive survey of fairness challenges and mitigation strategies in LLMs, including gender and racial bias.
Bolukbasi et al., NeurIPS 2016
Seminal work identifying gender stereotypes in word embeddings and proposing methods to debias them
Mehrabi et al., Nature Machine Intelligence 2024
Analysis of bias propagation in multimodal AI systems combining text, image, and audio data.
Bender et al., FAccT 2021
Risks of large language models perpetuating social biases (retained for its foundational impact).
Srivastava et al., arXiv 2024
Explores transparency frameworks for AI systems, emphasizing accountability and user trust.
Zou et al., arXiv 2023
Techniques to identify and reduce harmful content generated by advanced AI models.
Perez et al., arXiv 2022
Innovative approach using AI to test AI systems for harmful outputs, revealing vulnerabilities that manual testing might miss.
xAI, 2025
Official guidelines from xAI on building safe, transparent, and equitable AI systems.