Importance of Inclusive Data Collection

I recently saw a picture of an “#InclusiveAI”, team, but all the members were white. While it's great to see companies striving for inclusivity, it's important to remember that diversity & inclusion goes beyond just gender and includes race, ethnicity, age, ability, culture, and backgrounds. Having a diverse team when building #AI systems is crucial for several reasons. As someone who possesses multiple identities that are usually excluded when building these types of innovations, I care even more so. (ofc you shouldn’t only care when affected!). 🌻Why is true #InclusiveAI important? Firstly, it helps to uncover problems and make data connections that might be missed by a homogenous group. A truly representative team brings a range of skills, experience, and expertise to the table, which can drive superior AI by bringing diverse thought to projects. This can maximize a project’s chance of success. Secondly, diversity in AI development is important in combating against AI bias. AI learns only what people show it, so if the data used to train AI systems is skewed or biased, the resulting AI will also be biased. This can have major consequences. For example, if a #generativeAI model is fed photos of mostly white/light-skinned people to learn what a face looks like, then brown/dark-skinned faces will be difficult to generate—if generated at all. 💡A lack of diversity in AI development could increase discriminatory issues within AI technology. The lack of diversity in race and ethnicity, gender identity, and sexual orientation not only risks creating an uneven distribution of power in the workforce but also reinforces existing inequalities generated by AI systems. This reduces the scope of individuals and organizations for whom these systems work and contributes to unjust outcomes. In conclusion, it's imperative for diverse peoples to be part of inclusive AI teams. Building AI without ttue representation, without insistent diversity can result in flawed systems that perpetuate extreme biases on all fronts. By striving for true inclusion in AI development, we can ensure that future technology benefits all people and not just a homogenous group. 💭 Keen to know your thoughts on this topic, please share in the comments below. #theBOLDjourney #AITools #AI #marketing

Data is NOT just about numbers. But why does data matter? Data has always been a tool for change. Using data isn't about: • Ignoring diverse voices in data careers • Keeping data inaccessible and hard to use • Allowing bias in data and AI • Overlooking the importance of ethical practices • Failing to challenge unfair narratives • Neglecting the impact of data on communities • Avoiding accountability and truth • Letting data be used without responsibility Using data is really about: • Expanding representation in data careers • Making data accessible and actionable • Challenging bias in data and AI • Upholding ethical, inclusive data practices • Empowering people to use data for change • Ensuring fairness and accuracy • Elevating diverse voices • Building a future of opportunity and equity Want to make a difference with data? Empower more people to take part in the narrative. → You will drive meaningful change. → You will ensure data works for everyone. → You will create a more just world. Let's use data for truth and inclusion. And build a better future for all.

Despite over 7,000 recognized languages worldwide, the internet is dominated by just a few. Among the top 38 languages used online, not a single one is African, with nearly 50% of content in English. All other languages appear on less than 0.1% of websites [1]. This stark underrepresentation means current Large Language Models are incapable of truly capturing the rich culture and diversity of our world. But data equity issues go beyond languages—they affect all aspects of data and technology. Underrepresented communities face inequities due to systemic biases in how data is collected, analyzed, and used—not just a lack of data. In sectors like healthcare, finance, education, and social services, AI systems can produce biased outcomes that disproportionately impact these communities, perpetuating existing inequalities. These problems stem from historical biases, unfair algorithms, and inequitable data governance, leading to systems that fail to serve everyone's needs. 🌐 Why Data Equity Matters Now 1/ Prevent Amplifying Inequalities: Without deliberate action, AI can perpetuate and worsen existing biases in critical sectors like healthcare, finance, and education. 2/ Maintain Public Trust: Perceived bias or unfairness in technology erodes trust, hindering adoption and progress. 3/ Unlock Innovation and Growth: Inclusive data leads to more effective, innovative solutions that serve all segments of society. 🤝 Data Equity Is a Collective Responsibility 1/ Private Sector: Integrate data equity into operations. Invest in diverse and representative datasets, mitigate algorithmic bias, promote transparency, and ensure equitable benefit-sharing. 2/ Academia and Experts: Build knowledge and tools. Develop measurement frameworks, promote equitable attribution, provide education, and conduct ethical impact assessments. 3/ Government: Establish enabling environments. Promote open-code policies, harmonize standards, fund training, and recognize Indigenous data sovereignty. 4/ Civil Society: Advocate for data justice. Raise public awareness, monitor data practices, empower communities, and promote ethical frameworks. 5/ Communities: Take ownership of data. Promote data sovereignty, participate in assessments, negotiate benefit-sharing, and build governance capacity. 📚 Learn More About Data Equity - Data Equity: Foundational Concepts in Generative AI: https://lnkd.in/gFsBeEvZ - Advancing Data Equity: An Action-Oriented Framework: https://lnkd.in/gWibWGi8 Kudos to the work of the Global Future Council on the Future of Data Equity. [1]: Languages used on the Internet: https://lnkd.in/gQbG_iQr

From inclusive data collection to rethinking eligibility criteria and expanding outreach, we’re working to make clinical research and care delivery more equitable, including for LGBTQIA+ communities. We know that inclusive research starts with inclusive data. That is why we’ve participated in more than a dozen panels and roundtables over the past six months to highlight the importance of collecting sexual orientation and gender identity (SOGI) data — a crucial step toward closing care gaps. Hear from Keith Dawson, Senior Director, Global Health Equity and Population Science, and Meg McKenzie, Director, Patient Inclusion & Health Equity, about why LGBTQIA+ inclusion and optional self-reported SOGI data collection are essential to shaping a healthier future.