  Humans increasingly interact with Artificial intelligence(AI) systems. AI
systems are optimized for objectives such as minimum computation or minimum
error rate in recognizing and interpreting inputs from humans. In contrast,
inputs created by humans are often treated as a given. We investigate how
inputs of humans can be altered to reduce misinterpretation by the AI system
and to improve efficiency of input generation for the human while altered
inputs should remain as similar as possible to the original inputs. These
objectives result in trade-offs that are analyzed for a deep learning system
classifying handwritten digits. To create examples that serve as demonstrations
for humans to improve, we develop a model based on a conditional convolutional
autoencoder (CCAE). Our quantitative and qualitative evaluation shows that in
many occasions the generated proposals lead to lower error rates, require less
effort to create and differ only modestly from the original samples.
