Biblio

How to develop AI systems that can explain how they made decisions is one of the important and hot topics today. Inspired by the dual-process theory in psychology, this paper proposes a human-in-the-loop approach to develop System-2 AI that makes an inference logically and outputs interpretable explanation. Our proposed method first asks crowd workers to raise understandable features of objects of multiple classes and collect training data from the Internet to generate classifiers for the features. Logical decision rules with the set of generated classifiers can explain why each object is of a particular class. In our preliminary experiment, we applied our method to an image classification of Asian national flags and examined the effectiveness and issues of our method. In our future studies, we plan to combine the System-2 AI with System-1 AI (e.g., neural networks) to efficiently output decisions.

Nowadays, Volunteered Geographic Information (VGI) is commonly used in research and practical applications. However, the quality assurance of such a geographic data remains a problem. In this study we use machine learning and natural language processing to improve record retrieval by category (e.g. restaurant, museum, etc.) from Wikimapia Points of Interest data. We use textual information contained in VGI records to evaluate its ability to determine the category label. The performance of the trained classifier is evaluated on the complete dataset and then is compared with its performance on regional subsets. Preliminary analysis shows significant difference in the classifier performance across the regions. Such geographic differences will have a significant effect on data enrichment efforts such as labeling entities with missing categories.

This paper begins to describe a new kind of database, one that explores a diverse range of movement in the field of dance through capture of different bodies and different backgrounds - or what we are terming movement vernaculars. We re-purpose Ivan Illich's concept of 'vernacular work' [11] here to refer to those everyday forms of dance and organized movement that are informal, refractory (resistant to formal analysis), yet are socially reproduced and derived from a commons. The project investigates the notion of vernaculars in movement that is intentional and aesthetic through the development of a computational approach that highlights both similarities and differences, thereby revealing the specificities of each individual mover. This paper presents an example of how this movement database is used as a research tool, and how the fruits of that research can be added back to the database, thus adding a novel layer of annotation and further enriching the collection. Future researchers can then benefit from this layer, further refining and building upon these techniques. The creation of a robust, open source, movement lexicon repository will allow for observation, speculation, and contextualization - along with the provision of clean and complex data sets for new forms of creative expression.