How can you make sure your AI system is inclusive?

Data reflects the society created by humans. So, with certainty the bias and skewness will exist. Garbage IN results in Garbage OUT. If one wants to build models from the data and take decisions that could potentially affect life of users eventually, we must ensure the data feeding in to the model is clean. Few steps- look for missing data sources, question how the results would alter if missing data is available, re-check the weightage assigned to every variable objectively. If the weightage for a missing data point is high (eg consumers of a product includes all genders but data for design is available only from males' testing),then develop behavioural models to do simulations & generate the data needed for the missing scenarios.

Data is like the ingredients in a recipe. If you use poor-quality ingredients, the end dish won't be as good. Similarly, if your data is biased or incomplete, your AI model can produce unfair or inaccurate results. It's not just about gathering tons of data, but also about ensuring that it's a balanced and accurate representation of the world. Always double-check your data sources and use tools to root out biases before they affect your outcomes.

Ensuring inclusive AI involves a multi-step process, beginning with data collection and analysis and extending to the deployment and evaluation stages. To guarantee inclusive AI, it is essential to adopt a human-centered and participatory approach during the design and development phase, involving diverse stakeholders like users, experts, and communities. Ethical principles such as transparency, accountability, privacy, and security should guide the AI system's development and align with stakeholder needs. Additionally, accessibility and usability for individuals with various abilities, preferences, and backgrounds should be a priority throughout the AI system's design.

Deploy and Evaluate stage is a lot like throwing a dinner party. You've done the prep work, but now you need to see how your guests (users) react to the meal (AI system). Monitoring isn't just about making sure nothing's broken; it's also about noticing who's not enjoying the party and why. Just like you'd ask for feedback on your cooking, you should actively solicit input from users, especially those who are often overlooked, to make your AI system more inclusive.

Steps for Awareness 1. Develop Educational Materials: Create user-friendly guides and materials about your AI system. For a language translation app, provide tutorials on language input variations and how the AI handles them. 2. Host Workshops and Webinars: Conduct workshops and webinars on responsible AI usage. Use case studies to illustrate potential benefits and risks. 3. Offer Learning Opportunities: Establish online courses or training programs. A health AI company could offer courses on understanding AI-driven diagnostics for healthcare professionals. 4. Support Skill Development: Provide resources and mentorship programs. In a finance AI context, support aspiring analysts from diverse backgrounds in learning AI-driven analytics.

This is one of the biggest challenges. A lot of people in teams suffer from group think and therefore if they have a bunch of data scientists who have the same "motivations" then the AI will fall on its sword. That's one challenge to get over. The other is to ensure you have non data scientists or developers who are critical thinkers, philosophers, art historians etc. These types of people will bring other dimensions into the conversation that tranditional data people won't see. The other area is people of colour. This will bring the fairness and equality factor to the AI with diverse experiences in life. Biases that would otherwise be overlooked. If these aren't considered then the AI will end up in the graveyard with so many others.

Inclusive AI goes beyond code, it's about embracing human values when we build responsible AI products. It starts with forming diverse teams that bring rich perspectives to the table during the development process. During dataset training, model development, and testing phases, meticulously go over for unconscious biases that might inadvertently creep into AI algorithms. Apply thorough A/B and stress testing to validate AI responses across various scenarios. Moreover, continuous monitoring and testing play a pivotal role to catch unforeseen hiccups. By following these practices, we can ensure the AI systems that we build are not only robust but also ethically sound and inherently more inclusive.

Ensuring inclusivity in an AI system involves a multi-faceted approach. Firstly, diverse and representative training data is essential to minimize biases. Secondly, continuous monitoring and testing of the system for potential bias and discriminatory outputs is crucial. Collaborating with experts from diverse fields and seeking user feedback can help refine the AI's responses and make it more inclusive. Additionally, providing transparency in the decision-making process and allowing customization by users can further promote inclusivity.

?? This is one of the most important question before building any AI system. In case you already run an AI system, the most critical step in creating inclusive AI is to recognize where and how bias infects the system. 1/ To identify biases (recognizing exclusion in AI within): dataset bias, associations bias, automation bias, interaction bias, confirmation bias. 2/ Insights for Inclusive AI: 1. Redefine bias as a spectrum 2. Enlist customers to correct bias 3. Cultivate diversity with privacy and consent 4. Balance intelligence with discovery 5. Build inclusive AI teams There’s no magic formula for all scenarios, nor should there be. When attempting to build AI that really helps and understands human, we need perspective.