FAIRLY’s mission is to support the broad use of fair and responsible AI by helping organizations accelerate safer and better models-to-market.
GOVERNANCE | RISK | COMPLIANCE...with efficiency, speed, and accuracy
About Fairly AI
The concept of Fairly AI Inc. (henceforth “FAIRLY”) started in 2015 as an interdisciplinary research project involving philosophy, cognitive science and computer science. After extensive product concept and design iterations, FAIRLY was formally incorporated in April 2020, and is a global operation with headquarters in Kitchener-Waterloo, Ontario, Canada.
FAIRLY is a trusted provider of AI governance, risk and compliance technology. Built to help businesses accelerate responsible AI models to market, FAIRLY’s API-driven productivity and risk management tools feature automated and transparent reporting, bias detection, and explainable model risk assessment, thus streamlining AI development, validation and audit processes across the three lines of defense for financial services at scale. For SMEs and startups outside of financial services, our Model Risk Management as a Service is the quality assurance partner you need to help guide you through the governance, risk and compliance processes for safer and faster models-to-market.
Our patent-pending technology for today and tomorrow
The speed of AI innovation is far outpacing governance, risk and compliance management. The AI model risk management market is an emerging category and FAIRLY has been a trailblazer in this space: There are unintentional risks associated with the use of AI models which FAIRLY is passionate about minimizing through the development of the FAIRLY solution. These risks can be identified in the AI model governance structure and through repeatable identification can be minimized during model development. Such risks that can be minimized are: unidentified human bias embedded into the design of the AI technology; human logic errors; ethically questionable model predictions due to insufficient testing and oversight; reputational and financial harm; failure to realize value from expensive AI projects due to under-performing and poorly understood AI models; being left behind the competition and risk that the models developed don’t yield an acceptable ROI, which could reduce future AI model funding. All such risks are critical to internal auditors who are going to be required to report on AI governance in the future.