Building and Using Responsible AI Technology by Implementing World-Class Audit Techniques.
Our ethical AI-powered technology is designed to detect discriminatory biases (race, ethnic, gender, age, disability, religion) within an institution’s data ecosystems. We assist in the development of how data becomes inclusive across the data lifecycle and provide recommendations to prevent bias. We listen, observe, analyse, and understand how your organization builds trust to create an inclusive society.
Watch our Founding CEO speak on a global panel by the World Health Organization as she discusses CornerstoneAI’s ethical technology systems.
She addresses the importance of inclusive AI (accountable, fair, and transparent) and why we detect biases such as ethnicity, gender, age or disability within an institution’s data ecosystems.
This affects an institution’s ability to conduct equitable business in various countries; how companies attract and retain talented employees; their brand loyalty; and the impact they have on rebuilding our economy post-COVID19.
We built an AI-powered debiasing platform that synergize three machine learning techniques and also includes custom ethical and regulation implications for your organization.
Our team consist of researchers from Oxford University and McGill University, and other ethical AI industry thought-leaders in business, politics and entrepreneurship.
Our AI discriminatory debiasing automation platform can audit data with a couple of hours to a week depending on the size of your data. We also conclude our findings in a detailed report.
Our AI-powered audit seeks to create impact across an AI-first world that is aligns to various United Nations Sustainable Development Goals.
organizations believe that ethical issues have resulted from the use of AI systems over the last 2-3 years
of consumers said they would place higher trust in a company whose AI interactions were seen as ethical.
of consumers said they would share positive experiences with friends and family.
of Canadians believe that the lack of diversity among people working in the field of AI could lead to biases in the technology being developed.
Take the first step towards eliminating discriminatory bias in your data today.