Navigating a digital maze: Trust, bias & privacy

Have you ever wondered why your news feed looks completely different from the one of the people you disagree with? The recommendation algorithms try to predict your next "like" based on what people with similar tastes are into. However, those individuals with "similar tastes to yours" also tend to share the same political opinions as you. Echo chambers, polarization, misinformation, bias, etc. How do we measure these phenomena? We delve into recommendation algorithms to explain their mechanisms and understand if they are responsible for the flood of pretty cat memes you see every day.

Most of the systems we use daily collect our data, from our phones to our Navigo cards. Our activities are tracked and analyzed to improve the performance of the metro or to target better commercial products. Would this be possible "privately"? There is a wide range of options, though not all of them are wise. Looking at some historical failures, we can grasp how powerful computers and big data have changed the privacy game. We will examine together some of the new challenges and possible solutions. As a warm-up: can you discover my name?

How do we decide whether to trust someone or not? This daily, seemingly straightforward decision has a great impact on our lives. Throughout time, societies have established a complex and solid (even if noisy and imperfect) reputation network; it has become beneficial to be trustworthy. It is why we hypothesized that evolution dictates how our brains compute the optimal solution to decide whom to trust. But that’s not an easy task. The brain uses a fast and nearly accurate computational method. I will tell you what we know about how your brain processes trust.