In a world where technology interconnects us, we need AI assessment tools that are as inclusive as they are innovative. However, even the most recommended tools often reflect societal biases, inadvertently marginalizing those who don’t check the right boxes. Achieving global mental health equity means overcoming substantial challenges, especially in terms of accessibility and assessment accuracy. These challenges are further compounded by biases inherent in popular AI assessment tools. AI models can demonstrate accuracy differences as large as 25% among different ethnic groups. And popular tools like the GAD-7 lose about 15% of their sensitivity when used on non-Western populations, highlighting a critical gap in effectiveness. Thesis: Equitable AI assessment tools possess the transformative potential to redefine mental health care across the globe. However, for these tools to truly make an impact, they must be designed and implemented with a keen focus on improving access, promoting engagement, and enhancing effectiveness for all populations. This approach transcends mere technological advancements and reaches into the realm of ethics, ensuring that these innovations close the gaps in mental health care access and outcomes rather than widening them. Emphasizing equitable access and meaningful user engagement will underpin their ability to break down barriers and overcome biases, thus providing a just and effective solution for mental health care globally. Key Points to be Discussed: • Understanding AI Bias in Mental Health Assessments: Explore the sources and nature of biases in AI models, such as algorithmic bias originating from non-representative training datasets. Discuss real-world implications where biases have led to skewed mental health assessments and outcomes. • The Role of Data Inclusivity with the Equity Equation: Introduce the equity equation for digital mental health. This approach ensures that AI outcomes are accessible, effective, and fair, defined by the equation: Equity = (Access × Engagement × Effectiveness) / (Barriers + Bias). • Rethinking Standardized Mental Health Measures: Delve into the advantages and challenges that come with utilizing the standardized measures recommended by the International Consortium for Health Outcomes Measurement. • Examples of Known Biases in Progress and Outcome Monitoring Instruments: Review examples of known biases within the National Institute of Mental Health standardized measures chosen for the common data elements initiative. This spotlights where improvements can be made to enhance equity. • Potential Solutions and Innovations: Delve into solutions that have demonstrated the ability to address the challenges of mental health equity and explore why many health practitioners continue to favor traditional AI assessment tools that employ diagnostic measures developed by the World Health Organization, American Psychiatric Association, and Pfizer. The presentation seeks to spark a collaborative effort to transform common measures into a truly equitable process through AI assessment tools that emphasize equity = (access × engagement × effectiveness) / (barriers + bias). It is designed to engage congress delegates in a conversation about real-world applications and strategies that can be implemented globally. By addressing biases in AI assessment tools, we can pave the way for innovative, inclusive solutions that ensure equitable access to mental health support worldwide.