Talk in English - US at CascadiaPHP 2024
Track Name:
Crater Lake
Short URL: https://joind.in/talk/7bfcb
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Aspects of development work like accessibility are often treated by product teams as legal obligations, boxes to be checked off the list. As we widen the scope to the broader organization, we can see this mindset being applied to other areas, like Diversity, Equity and Inclusion, or dealing with remote workers, or incorporating emerging technologies like AI. At best, each area is seen as one person’s job to take care of, with a limited scope of impact, and everyone else can wash their hands of it.
But making things better, easier and more equitable is the reason most people get started developing software - why wouldn’t we extend these goals and their benefits as broadly as possible? We’ll talk about concepts of universal design you can apply across workstreams. By applying these, you’ll see increased internal engagement and morale. Externally, you’ll grow your potential audience, which can have a meaningful positive impact on your bottom line - everyone wins!
Comments
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So well done. This was so informative!
Loved the topic, the information, the pacing, all of it. Great talk!
Great insights and presentation style. Love the notion of “Holistic tech”, it feels good to have a label to attach to it and a solid definition.
The AI section was perhaps disproportionate, but I'm always happy to hear prominent voices warning that AI is not the cure-all that marketing makes it out to be.
Great talk, I think more practical tools and methods to improve accessibility would be good to add.
Fantastic talk, Kaitlyn is clearly one of those people we can all be glad is on our side. I particularly liked the discussion about non-obvious accessibility issues such as "wall of text". The AI section went a bit long and seemed a little tangential at times, but on the other hand there were some solid suggestions for how to use AI effectively in an equity-minded context.
Great talk! I especially liked the AI part. There were easy to understand points and analogies on why to always check AI's work, and why it can never be the complete solution.