This is an experience report describing a pilot AI Ethics course for undergraduate computer science majors. In addition to teaching students about different ethical approaches and using them to analyze ethical issues, the course covered how ethics has been incorporated into the implementation of explicit ethical agents, and required students to implement an explicit ethical agent for a simple application. This report describes the course objectives and design, the topics covered, and a qualitative evaluation with suggestions for future offerings of the courses.

A new challenge in natural language processing, argumentation mining is the automatic identification of an argument's premises, conclusion, and argumentation scheme, and relationships between arguments. Argumentation mining could provide critical context for information extraction and question answering and support new forms of summarization and citation indexing. However, argumentation mining, in this sense, has not yet been addressed in BioNLP. This theoretical paper contributes towards annotation of argumentation in biomedical/biological corpora. We show that argumentative zone models and models of discourse coherence do not represent the same aspects of discourse as a model of evidence-based argumentation. We explore the challenges of annotation of argumentation in full-text biomedical articles and describe the steps we have taken towards annotating a biomedical corpus for argumentation mining research.

The TBI-Doc prototype demonstrates the feasibility of automatically producing draft case reports for a new brain imaging technology, High Definition Fiber Tracking (HDFT). Here we describe the ontology for the HDFT domain, the system architecture and our goals for future research and development.

With some sense-making software, investigators can use causal networks to visualize possible stories explaining the evidence. Despite the different domains, there are interesting correspondences between that type of application and a proposed intelligent learning environment (ILE) in which science students could visualize and debate causal scenarios accounting for clinical findings. The proposed ILE will extend the design of the GenIE Assistant, a system to generate first-draft genetic counseling letters. This paper compares the underlying computational models of sense-making software and the GenIE Assistant. Then it discusses refinements of the Assistant's causal argumentation schemes to support debate in the ILE. The refinements are at a level of abstraction that seem applicable to computational models for sense-making and evidential reasoning in law.

We present the results of an in-depth qualitative analysis of argumentation in two genetic counseling patient letters. In addition to argumentation techniques designed for medical experts, we found other types of causal argumentation designed for lay readers, reflecting the educational and supportive counseling functions of these letters. Analysis was facilitated by use of a coding scheme for representing causal probabilistic biomedical content of the letters as Bayesian networks. We define the argument techniques used in the letters in terms of Bayesian network, semantic network, argumentation theory, and user model concepts rather than in terms of genetics concepts. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 71–93, 2007.

This paper presents a corpus study that explores the extent to which captions contribute to recognizing the intended message of an information graphic. It then presents an implemented graphic interpretation system that takes into account a variety of communicative signals, and an evaluation study showing that evidence obtained from shallow processing of the graphic's caption has a significant impact on the system's success. This work is part of a larger project whose goal is to provide sight-impaired users with effective access to information graphics.

This paper presents our implemented computational model for interpreting and generating indirect answers to Yes-No questions. Its main features are 1) a discourse-plan-based approach to implicature, 2) a reversible architecture for generation and interpretation, 3) a hybrid reasoning model that employs both plan inference and logical inference, and 4) use of stimulus conditions to model a speaker's motivation for providing appropriate, unrequested information. The model handles a wider range of types of indirect answers than previous computational models and has several significant advantages.

Americanae nace como un proyecto conjunto que surge dentro de la Red Europea de Información y Documentación sobre América Latina (REDIAL), y que ha afrontado la Biblioteca de la Agencia Española de Cooperación Internacional para el Desarrollo (AECID). Esta nueva biblioteca virtual hace más accesibles los libros digitales de tema americanista a los investigadores y usuarios interesados de cualquier parte del mundo.

In the right situation, a speaker can use an unqualified indefinite description without being misunderstood. This use of language, normal state implicature, is a kind of conversational implicature, i.e. a non-truth-functional context-dependent inference based upon language users' awareness of principles of cooperative conversation. I present a convention for identifying normal state implicatures which is based upon mutual beliefs of the speaker and hearer about certain properties of the speaker's plan. A key property is the precondition that an entity playing a role in the plan must be in a normal state with respect to the plan.