There have been few studies on cognitive bias for algorithm understanding in a human-computer cooperative situation. In the present study, we conducted an experiment with participants to investigate the cognitive process of higher level abstraction (algorithm understanding) performed in a human-computer collaboration task. The most recently used (MRU) algorithm, known to be one of the simplest adaptive algorithms, and probabilistic MRU algorithm were used to test the human capability to understand an algorithm. The experimental results showed that inductive reasoning in which participants observed the history of computer action, and they updated a statistical model while restricting their focus on a certain history with deterministic bias and Markov bias played key role to correctly understand the MRU algorithm. The results also showed that deductive reasoning was used to understand algorithms when participants rely on prior knowledge, and that there was a case in which the algorithm, even known to be the simplest one, was never understood.

We construct an elderly corpus with a control group (EC) comprising narratives of elderly people with Mild Cognitive Impairment (MCI), healthy elderly people, and younger people in order to develop a method to classify the elderly into healthy and MCI by analyzing the corpus. To do so, we carry out three tasks (picture description task: PDT, episode picture description task: EDT, and animation description task: ADT) to participants (n = 80) and their voices to the tasks are recorded and manually transcribed. 60 out of the participants are the elderly and classified into MCI and healthy control based on Mini Mental State Examination (MMSE). Then, language features such as Type Token Ratio and Idea Density are extracted by analyzing the elderly people’s data and machine learning models are built with the extracted features. In the experiments, our classification model using combined language features obtained from all tasks’ data achieved the highest performance (AUC = 0.85). The results indicate that it would be important to carry out multiple tasks to detect the elderly with MCI.

Social tagging systems is a system that shares online contents and gives a user arbitrary character string (i.e., tag). Many Web services such as Flickr, Twitter, Instagram, Facebook are adopting social tagging system. It is reported that the process of tag growth can be roughly approximated by the Yule–Simon process. On the other hand, the growth of individual tags has also been analyzed. If it follows Yule–Simon process, the probability distribution of deviation can be obtained analytically. However, it is not well studied how the growth of individual tags in the actual social tagging system deviates from the prediction value of the Yule–Simon process. In this paper, we analyze the growth of individual tags of the social tagging system focusing on deviation from Yule–Simon process. Moreover, we propose a model that modifies the Yule–Simon process and explore a more detailed mechanism of tag generation and selection.

In this study, we propose a new design of academic workshop to generate co-creation between researchers and citizens. Co-creation was defined as “people with diverse backgrounds conduct creative activities together to achieve common goals and/or to solve problems.” We designed a process with four steps to generate co-creation; ①learning about host area, ②presentation and dialogue, ③generate co-creation projects, and ④conduct creative activities. We established “Special Interest Group on Crowd Co-creation Intelligence” at The Japanese Society for Artificial Intelligence as a target of social experiment. A program of the workshop was designed to achieve one to three steps out of four steps. 56 people including researchers and citizens participated in the first workshop. As a result of the questionnaire, it was found that the aim of each programs were almost achieved. A total of six co-creation projects was generated. Built a good relationship between participants and generating co-creation ideas contributed to the generation of co-creation projects. In some cases, new cocreation projects were generated that combine citizen's ideas for solving regional problems and researchers' skills which could realize them. In this way, the effects co-creation between citizens and researchers through this workshop were seen.