Abstract: As creators and users of artificial intelligence (AI), we have a duty to guide the development and application of AI in ways that fit our social values, in particular, to increase accountability, fairness and public trust. AI systems require context and connections to have more responsible outcomes and make decisions similar to the way humans do.
AI today is effective for specific, well-defined tasks but struggles with ambiguity which can lead to subpar or even disastrous results. Humans deal with ambiguities by using context to figure out what’s important in a situation and then also extend that learning to understanding new situations. In this talk, Amy Hodler will cover how artificial intelligence (AI) can be more situationally appropriate and “learn” in a way that leverages adjacency to understand and refine outputs, using peripheral information and connections.
Graph technologies are a state-of-the-art, purpose-built method for adding and leveraging context from data and are increasingly integrated with machine learning and artificial intelligence solutions in order to add contextual information. For any machine learning or AI application, data quality – and not just quantity – is critical. Graphs also serve as a source of truth for AI-related data and components for greater reliability. Amy will discuss how graphs can add essential context to guide more responsible AI that is more robust, reliable, and trustworthy.
Bio: Amy E. Hodler is a network science devotee and AI and graph analytics program manager at Neo4j. She promotes the use of graph analytics to reveal structures within real-world networks and predict dynamic behavior. Amy helps teams apply novel approaches to generate new opportunities at companies such as EDS, Microsoft, Hewlett-Packard (HP), Hitachi IoT, and Cray. Amy has a love for science and art with a fascination for complexity studies and graph theory. She tweets as @amyhodler.