Human intelligence scores are dropping across the US for the first time, research suggests, and experts warn technology could be to blame. IQ exams were first introduced in 1905, and throughout the century, there was a 30-point increase based on scores in logic, vocabulary, spatial reasoning and visual and mathematical problem-solving skills. The average IQ score in the US currently stands at 98 but varies by a gap of about eight points between states, with New Hampshire ranking first with an average IQ of 103.2. More research needs to be conducted to determine why IQ scores are falling across the country, but one expert speculated that a drop in reading and an increase in media entertainment, like YouTube, is at fault. There are also concerns that phones degrade our memory and recall because there is less need to store information with Google at our finger tips.

Including human analysis has the potential to positively affect the robustness of Deep Neural Networks and is relatively unexplored in the Adversarial Machine Learning literature. Neural network visual explanation maps have been shown to be prone to adversarial attacks. Further research is needed in order to select robust visualizations of explanations for the image analyst to evaluate a given model. These factors greatly impact Human-In-The-Loop (HITL) evaluation tools due to their reliance on adversarial images, including explanation maps and measurements of robustness. We believe models of human visual attention may improve interpretability and robustness of human-machine imagery analysis systems. Our challenge remains, how can HITL evaluation be robust in this adversarial landscape?