How can you make your AI systems inclusive for all users?

To make AI systems inclusive for all users, it is crucial to understand the users and their needs. Conducting analysis can help identify different user groups, their goals, challenges, expectations, and contexts of use. It is also essential to consider the system's potential impact on users, such as their privacy, security, trust, and well-being. Designing for accessibility and diversity can help ensure that the system is usable by all users. Incorporating feedback from diverse users and stakeholders can help identify and address any issues or biases in the system. Evaluating and iterating the design based on feedback and data can help improve its performance and impact on users and stakeholders.

? Purchase or develop datasets that come with clearly defined datasheets (SYSTEM CARDS) stating where the data came from, and how it was gathered and what the intended purpose is (same with the algorithms and model cards). For examples read Google, Meta etc cards. Otherwise it's like buying a dirty email newsletter list. ? Place the AI in the BLOCKCHAIN (digital vault) eg every human has control over their health data: hospital can see their medical records but random insurance companies cannot. Scraping and collating big data out of the control of everyday humans has to stop. ? Make REGULATIONS that give the humans being ingested into the AI further control by insisting the cards are correct and audited and verified in the blockchain.

Diverse users expect to see diverse perspectives. For example, Midjourney received some criticism because the simple term 'attractive' originally triggered more examples of Caucasian faces. Users had to add the word 'diverse' to receive more non-white results. The models are often updated to address this bias that prevents users from seeing the diverse results they expected.

Always listen to your users. (feedbacks/responses) Understanding your users is a continuous process kind of loop. Make it easy for them to share their responses to the AI used, both while testing it and after it is in use. This continuous feedback update is very very important because it helps to make changes to the AI system when users need something different based on their experiences or based on the problems faced. It's something like making sure your car is running smoothly by paying attention to any strange sounds or watching your dashboard screen for any issues and fixing them right away.

Let's consider a case where a business function that would usually be performed by a person is now getting replaced with an AI system. Say an employee wants to get recommendations on up-skilling that could help him/her in career progression. Similar to a human HR expert who first tries to know the career aspirations of the employee, the AI system should be open to knowing the employee as an individual rather than working off, of a set of pre-defined rules based on superficial attributes. The advantage of an effective AI system is that it can be made flexible and spontaneous rather than fixed & judgmental. Understanding the users is key but using algorithms that make better and less biased decisions is a yardstick to measure these success.

Accessibility standards, like the Web Content Accessibility Guidelines (WCAG), play a crucial role in creating an inclusive digital world. WCAG ensures that web content is accessible to everyone, including individuals with disabilities like visual, auditory, or cognitive impairments. Abiding by these standards not only fulfills ethical and legal responsibilities but also expands a product's user base. Accessible design aids in reaching a broader audience, enhancing user experience and satisfaction.

I concur that prioritizing accessibility in system design is firmly rooted in meeting ethical and legal obligations. Furthermore, I wholeheartedly support the notion that systems intended for broad public use tend to garner greater acceptance. However, it's worth noting that numerous Machine Learning systems are highly specialized for specific industries. In such cases, crafting a system that accommodates diverse user groups can be a time-intensive endeavor, potentially resulting in excessive design and development efforts.

In my opinion this is the most underrated aspect of AI system design. Specific advice depends very much on the type of AI you are designing. There are many types. I'll focus on LLMs. LLM fairness and transparency is greatly influenced by the Training Dataset. Attention should be paid to whether the Training Dataset holds a truly representative view of the world, or the subject that the LLM seeks to learn. Having a diverse team of data scientists review and refine the Dataset is an easy and effective way to increase probability of fairness. If possible you should also fully describe the Dataset for open review and comment, and also do the same with the Loss Minimization algorithm of the neural network underlying the LLM.

For this aspect I'll focus on a less fashionable but broadly used and established type of AI: constraint-based optimizers. Your constraint functions encode very important assumptions about how the world works. You should have a diverse team of mathematicians, statisticians and subject experts review the selected independent variables *and their coefficients*. Cultural bias can hide in the coefficients and even small "errors" can have big, important impacts on the optimization.

Building culturally inclusive AI systems requires models that adapt to diverse communication styles. For instance, in India, where cultural diversity is rich, individuals use honorifics like "ji" or "sir/ma'am" to convey respect. A culturally aware AI model should understand and incorporate these nuances. Expressions of politeness, varying across regions, demand tailored responses. A user from India might expect a different level of formality, reflecting regional norms. By integrating India's diverse cultural nuances into training data, developers create AI systems that navigate the subtleties of language, fostering inclusive and respectful user interactions in this culturally rich context.

Engaging and empowering users is vital for AI inclusivity. However, beyond creating a welcoming experience, it's pivotal to foster an environment where users feel comfortable providing candid feedback. Users' insights, especially regarding diversity and inclusiveness, are invaluable. Their perspectives can pinpoint blind spots, ensuring the AI caters to a broad spectrum of needs. The development team should prioritize and respond earnestly to such feedback. Inclusivity is an iterative process, and listening to diverse voices ensures that AI systems are not just welcoming but also genuinely representative and equitable.

AI FOR ALL ! Unlocking the power of AI, we must allow for inclusivity by engaging and empowering the users. By inviting them to shape the system, customize their experiences, and sharing their thoughts. When users understand how it works and can make it their own, they feel included. We need make everyone part on this adventurous experience, making AI a helpful tool for all.

Post-launch vigilance is crucial for AI systems. While pre-launch evaluations might capture many issues, some nuances only become apparent when the system interacts with real-world users. Unnoticed problems may persist, and unforeseen challenges can emerge. Active monitoring and continuous feedback collection are essential. Even after rigorous testing, no system is perfect on its debut. Listening to user feedback is paramount, as it highlights both persistent oversights and new issues. Iterative refinement, guided by direct user experiences, ensures that the AI system remains inclusive, effective, and adaptable to evolving needs.

Engaging with diverse communities throughout the development lifecycle of AI systems is crucial to ensuring inclusivity. This involves actively seeking input, feedback, and collaboration from different user groups to understand their unique needs and preferences. Let's delve into a practical example to illustrate the impact of community involvement. Practical Example: Healthcare AI App for Diabetes Management Imagine a team of developers working on an AI-powered mobile application designed to assist individuals in managing diabetes. Recognizing the importance of community involvement, the development team decides to engage with a diverse range of users early in the process.