Chatbot

About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.

Mobile Phone-Based Chatbot for Family Planning and Contraceptive Information

Abstract: This study is about a mobile phone-based Chatbot specifically designed to provide information about family planning and contraceptives Chatbot is essentially a text-messaging service that follows a decision-tree structure to provide feedback to users The Chatbot was built using a text-messaging platform developed by Trext and can be accessed in the United States by sending ‘BCS’ as a text message to phone number +1-313-228-3034 The contents of Chatbot are derived from the Balanced Counseling Strategy (BCS) prepared by The Population Council UTAUT model of technology adoption was employed to assess the attitudinal and behavioral factors that determine the intention to use Chatbot The study included 49 participants, age 18 and above, married or in a relationship Regression analysis show positive attitude as the main predictor of behavioral intention to use Chatbot to acquire family planning related information Consequently, positive attitude was determined by effort expectancy and performance expectancy to use the Chatbot The study has implications to design mobile phone-based texting services to help mothers, husbands and community health providers to learn about family planning in a private, interactive and enjoyable manner To the best of our knowledge, this is the first study to systematically evaluate the effectiveness of a mobile phone-based Chatbot for family planning counseling The study is a proof-of-concept with limited number of participants within USA However, the study offers implications to scale-up existing family planning interventions both domestically and internationally

Rating AI systems for bias to promote trustable applications

Abstract: New decision-support systems are being built using AI services that draw insights from a large corpus of data and incorporate those insights in human-in-the-loop decision environments. They promise to transform businesses, such as health care, with better, affordable, and timely decisions. However, it will be unreasonable to expect people to trust AI systems out of the box if they have been shown to exhibit discrimination across a variety of data usages: unstructured text, structured data, or images. Thus, AI systems come with certain risks, such as failing to recognize people or objects, introducing errors in their output, and leading to unintended harm. In response, we propose ratings as a way to communicate bias risk and methods to rate AI services for bias in a black-box fashion without accessing services training data. Our method is designed not only to work on single services, but also the composition of services, which is how complex AI applications are built. Thus, the proposed method can be used to rate a composite application, like a chatbot, for the severity of its bias by rating its constituent services and then composing the rating, rather than rating the whole system.

Chatbots for the Information Acquisition at Universities – A Student’s View on the Application Area

Abstract: Chatbots are currently widely used in many different application areas. Especially for topics relevant at the workplace, e.g., customer support or information acquisition, they represent a new type of natural language-based human-computer interface. Nonetheless, chatbots in university settings have received only limited attention, e.g., providing organizational support about studies or for courses and examinations. This branch of research is just emerging in the scientific community. Therefore, we conducted a questionnaire-based survey among 166 students of various disciplines and educational levels at a German university. By doing so, we wanted to survey (1) the requirements implementing a chatbot as well as (2) relevant topics and corresponding questions that chatbots should address. In addition, our findings indicate that chatbots are suitable for the university context and that many students are willing to use chatbots.

Chatbot system and method with enhanced user communication

Abstract: A chatbot system and method with enhanced user communication. A termination mark signifies that a chatbot input message sentence is complete. The chatbot system responds to the input message before a plurality of sentences are entered along with the input message sentence.