Clarissa Rodrigues

How to Lie with AI: Understanding Bias, Ethics, and the Hidden Risks in Machine Learning

Description: Do you understand AI well enough to spot a lie? I will dive into the fascinating world of artificial intelligence and machine learning, focusing on how bias can infiltrate AI models. Attendees will explore real-world examples of biased AI decisions, learn how machine learning models can "lie," and discuss the ethical challenges this poses. Through case studies like COMPAS and image classification failures, we’ll uncover how biased data can lead to problematic outputs. By the end of the session, attendees will gain practical strategies to identify, prevent, and mitigate AI bias in their own work. Takeaways: Understanding AI and Bias through real-life case studies Ethical Concerns and Practical solutions Future of looking ahead

Most Expensive Design Mistakes (Ever) and how to avoid them

Description: QAs work to make sure goods and services are designed, Description: developed, and made to meet or exceed consumers’ expectations and requirements. But, isn’t UX doing the same? Having the users in mind is what connects these areas to build better products. A fantastic User Experience is what makes the difference when customers choose between one product or another. Considering that, it only makes sense that QAs and UXrs join forces and help build great experiences from their own perspectives. Why not have a system that considers the best of both worlds? As you’d know, the saying that “good design is obvious” is a tale as old as time. As Steve Krug once said, “Your goal should be for each page or screen to be self-evident, so that just by looking at it the average user can say “I get it.” - Steve Krug. Takeaways: How to create effective visual hierarchies How to don’t reinvent the wheel How to nor we allow personal feelings to take over the process and how ask the right questions