AI UX—Create Diverse Education Tools

The ”one size fits all approach” is rarely satisfactory and in the case of AI systems, a cookie-cutter design heavily compromises how people understand, perceive, relate to, and embrace these systems. This episode looks at how you can create diverse education tools with your AI devices.

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Additional Resources:

Loi, D., 2018, Intelligent, Affective Systems: People’s Perspective & Implications, Yogyakarta, Jakarta, Malang, Indonesia.

Loi, D., Raffa, G. & A Arslan Esme, 2017, Design for Affective Intelligence, 7th Affective Computing and Intelligent Interaction conference, San Antonio, TX

Bostrom, N., & Yudkowsky, E. 2014. The Ethics of Artificial Intelligence in The Cambridge Handbook of Artificial Intelligence. Cambridge University Press

Sophia Chen. AI Research Is in Desperate Need of an Ethical Watchdog. Retrieved 14 October, 2017

PwC. 2017. Global Artificial Intelligence Study: Sizing the Prize.

Gershgorn, D. 2017. The Age of AI Surveillance is Here. Quartz

This is AI:UX a mini-series focused on 10 guidelines that were created to assist all those that are involved in the design and development of AI-based systems. I'm Daria Loi, and today I will talk about guideline number eight. Create multiple and diverse education tools. 

The "one size fits all" approach is never satisfactory, particularly in the case of AI systems. A cookie-cutter design would heavily compromise how people understand, perceive, relate to, and embrace the systems. During my research and interviews, people asked for a clear idea of what a system is, what it does, where data goes, and who has access to it, and why. So my recommendation is to empower people so they can make informed choices on how to incorporate AI systems in their lives. 

This requires the appreciation that people have diverse baseline understanding on these matters and requires the development of diverse ways to educate them. This education should identify what AI is, what it does, and what to expect from it, how to choose what is best for them, their surroundings, and communities. These education tools must avoid cryptic, technology-centric, and confusing lingo to be accessible for the general population. Education tools should include diversified content, capable of addressing diverse needs and contexts. 

Content should be articulated in diverse ways because we all learn differently, and our learning abilities shift depending on what context we are in. For example, give your users the opportunity to learn about the system hands-on through pre-launch trials, demonstration, and interactive demos. 

Finally, content should reach people through diversified channels, be it digital or non-digital, interactive or static. People need to understand AI, not be confused or let down by it. This is why I suggest you create multiple and diverse education tools. In the case of complex system powered by AI where trust is a massive sticking point, this guideline is fundamental. 

Thanks for watching. Don't forget to like this video and subscribe. I will see you next week on Tuesday for more AI:UX. 

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