Augmenting Public Safety Through AI and Machine Learning

In this demo day, we took a close look at the tremendous potential AI offers for making communities safer, by helping to reduce, prevent, and respond to crimes. When it comes to public safety, it is often critical to act quickly. AI technologies can supplement the work of people, taking on monotonous and time-consuming tasks that would be impossible for humans to do effectively. Natural language processing can read and analyze public communications and news reports to detect potential problem areas and get-ahead of violence. Of course, this work must be done responsibly and ethically.

Sharing her perspective on the impact that AI can have in keeping people safe was an expert in the field, ElsaMarie D’Silva, the Founder & CEO of the Red Dot Foundation. The Red Dot Foundation’s award-winning platform Safecity crowdsources personal experiences of sexual violence and abuse in public spaces. ElsaMarie is listed as one of BBC Hindi’s 100 Women, and her work has been recognized by numerous UN organizations and the SDG Action Festival.

To go a little deeper into the application of AI for public safety, we shared Omdena projects that took innovative approaches to make communities safer.

Case Study 1: Preventing sexual harassment through a safe-path finder algorithm

“UN Women states that 1 in 3 women face some kind of sexual assault at least once in their lifetime.”

With the first case study, the Omdena team drew upon Safecity’s crowdsourced data about sexual harassment in public spaces and leveraged open-source data to build heatmaps and calculate safe routes through major cities in India. Part of the solution is a sexual harassment category classifier with 93 percent accuracy and several models that predict places with a high risk of sexual harassment incidents to suggest safe routes.

Case Study 2: Understanding gang violence patterns and actors through Twitter analysis

Our team worked in partnership with Voice 4 Impact, an award-winning NGO whose solution to violence in our communities addresses the questions people worldwide are asking: “How do we keep missing the signs?”

The Omdena team made use of Natural Language Processing techniques  —  AI techniques that analyze text to understand what is being communicated. Machine learning algorithms were used to understand gang language and AI models were built to detect violent messages on Twitter, without profiling. The aim is to predict and ultimately prevent, gang violence.

Case Study 3: Analyzing Domestic Violence through Natural Language Processing (NLP)

Finally, we presented Omdena’s work to uncover domestic violence in India hidden due to COVID lockdowns. This work is part of a project with the award-winning Red Dot Foundation and Omdena’s collaborative platform to build solutions to better understand domestic violence and online harassment patterns during COVID-19. The project used natural language processing techniques with social media, government reports, and other text content to create a dataset with which Safecity could mobilize local efforts to protect and support domestic violence victims.