Let's talk about some real life examples. So, we talked about how building trust in AI organizations is a key highlight. If I'm a designer, what I want to do is I want to focus on the key aspects of understandability. Is this system understandable? Can somebody jump in? And maybe there's a learning curve, but even with the learning curve, have I done my due diligence in designing something that's understandable to the user? Privacy. We just talked about auditability, fairness.
And when we're talking about accountability, if I'm a developer, I need to work closely with designers to build robust AI systems that are going to allow the user to understand the quality and the accuracy of the data that they're giving. And as an organization, I should definitely start thinking about how I can allocate different resources to researching the consequences of the AI systems that I'm building. You know, like what is going to happen
if we implement this algorithm into our social media practice? How is this going to affect the end user? You know, these are a lot of big questions, and organizations should be allocating resources to really researching like what are the answers, what are the potential consequences to what we're doing. This is what it means to really be accountable for our actions. Fairness. So as a designer, I'm working on mitigating bias and addressing disparities
in AI outcomes through extensive strategy and research on user base. You know, as product designers, I know we have these like verticals, like some someone's a UI designer, someone's UX, someone's research. But at the end of the day, holistically, like as we're building and growing in our careers, one of the things that is super, super beneficial and helpful, you know, not only in our practice but in our impact on the world, is being able to to really use our knowledge and skills of strategy and of research to figuring out
how to mitigate bias, figuring out how to address disparities in AI outcomes, incorporating diverse demographics, for example, in AI training data, which basically will allow the system to run its outcomes, not on just one, like tunneled a batch of data, but having diverse data sets means that you're building outcomes for a diverse group of people. Because AI systems are really just
what we put into them, right? Like they're based off of our data. So are we taking data from everyone? Is our sample size diverse enough to feed into the data to make sure that we're being fair, we're being transparent with our systems. And then privacy as an organization. I aim to build robust user privacy, security and data infrastructure. I aim to allow users to know how their data is handled and obtained.