We analyzed the widely popular UTKFace dataset to implement a Convolutional Neural Network (CNN) algorithm, used to predict gender given an image of the human. Over the course of the experiment, we observed various statistics of the dataset, to give a strong representation of what could drive the results our model returned. By further investigating the current policy as it comes to data collection and privacy, we hope to suggest practices that will lead humanity to a safer, more equal future. Through the analysis of images from popular datasets, optimization of machine learning models, and the changing information industry standards, we aim to find and assess a difference in accuracies of gender-prediction models when it comes to race.

Abstract The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one's identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter's anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment.

The world of technology is changing. It used to be one where users could post on social media, order clothes online, and play mobile games without a care in the world. As time progressed, companies eventually realized that they could collect small pieces of information from a user’s technological habits, and use that to form a profile of the user of some sort. Such a profile could be used for marketing, grouping, or even just to sell-off. I conducted a study of mobile device application developers, specifically relating to iOS third-party applications. This is more of a hidden loophole in the “security” of Apple’s iOS. This is important to the field of data privacy because many users use third-party applications without the knowledge of what data is being taken and when it is being taken. I used a packet-monitoring service to listen in on third-party applications and wrote a script to filter out relevant pieces of data that were being sent back to an application’s server. The findings of this study concluded that third-party iOS applications have unmonitored power to access pieces of information like contacts, location, and emails.