Best Data Science Insight

Food production and their supply chains are the main cause of water pollution, especially in emerging and developed countries. Understanding the connection between our diet and water pollution is extremely challenging because it requires combining knowledge about food production, supply chains, and transportation. As more and more people seek to live sustainably, there is a need to inform consumers about the planetary impacts of their choices. We propose to construct a knowledge graph and application to create a water footprint calculator for our dietary choices that computes the water footprint for each ingredient of a meal proposed by the user and suggest alternatives to reduce the water footprint (e.g., replacing beef by pork in a cheesesteak meal will save the planet 1,600 gallons of water).

Common sense knowledge bases are used widely in research, spanning many areas in artificial intelligence, including natural language understanding, computer vision, and planning. However, these resources may contain human biases, which will ultimately be embedded in the resulting AI solution and potentially have negative societal impacts. The extent to which these biases exist is unclear. In this project, you will define several well-motivated biases (location, gender, ethnicity) and measure the extent to which they are represented in ConceptNet.