An answer set is a plain set of literals which has no further structure that would explain why certain literals are part of it and why others are not. We show how argumentation theory can help to explain why a literal is or is not contained in a given answer set by defining two justification methods, both of which make use of the correspondence between answer sets of a logic program and stable extensions of the Assumption-Based Argumentation (ABA) framework constructed from the same logic program. Attack Trees justify a literal in argumentation-theoretic terms, i.e. using arguments and attacks between them, whereas ABA-Based Answer Set Justifications express the same justification structure in logic programming terms, that is using literals and their relationships. Interestingly, an ABA-Based Answer Set Justification corresponds to an admissible fragment of the answer set in question, and an Attack Tree corresponds to an admissible fragment of the stable extension corresponding to this answer set.

We explore the idea that authoring a piece of text is an act of maximizing one's expected utility. To make this idea concrete, we consider the societally important decisions of the Supreme Court of the United States. Extensive past work in quantitative political science provides a framework for empirically modeling the decisions of justices and how they relate to text. We incorporate into such a model texts authored by amici curiae ("friends of the court" separate from the litigants) who seek to weigh in on the decision, then explicitly model their goals in a random utility model. We demonstrate the benefits of this approach in improved vote prediction and the ability to perform counterfactual analysis.

Research in neurally-based machine (i.e. computational) systems is expanding. “Reverse-engineered” models of brain-like structures are viable candidates for developing increasing complexification (via generatively encoded “intelligence”) that could instantiate some form of consciousness – albeit not identical to human consciousness. This essay posits how such trajectories could lead to the iterative development of “machine sentience” and addresses issues of what “machine consciousness” might mean for: 1) the ways that humans regard such machine entities as “beings” and/or “persons”, and 2) philosophical, ethical and socio-legal positions which might need to be adapted to guide and govern human treatment of, and interactions with such entities. Herein, I argue that neuroethics contributes crucial insights and viable tools to any meaningful approach to this topic (in synergy with extant discourse in “robo-ethics”). As the fields of neuro- and cognitive science, and computational engineering become increasingly convergent, so too must the philosophical and ethical approaches that can – and should – be employed to direct what convergent science may create. The speed and breadth of such technological development are such that neuroethical address and engagement of these issues and questions must be equivalently paced and iterative, so as to retain preparatory value.

In today's age of big data, websites are collecting an increasingly wide variety of information about their users. The texts of websites' privacy policies, which serve as legal agreements between service providers and users, are often long and difficult to understand. Automated analysis of those texts has the potential to help users better understand the implications of agreeing to such policies. In this work, we present a technique that combines machine learning and crowdsourcing to semi-automatically extract key aspects of website privacy policies that is scalable, fast, and cost-effective.

Website and mobile application privacy policies are intended to describe the system’s data practices. However, they are often written in non-standard formats and contain ambiguities that make it difficult for users to read and comprehend these documents. We propose a crowdsourcing approach to extract data practices from privacy policies to provide more concise and useable privacy notices to users and support the analysis of stated data practices. To that end, we designed a hierarchical task workflow for crowdsourcing the extraction of data practices from privacy policies. We discuss our workflow design and report preliminary results.

In this talk, I will describe efforts being undertaken in a collaboration between human rights advocates and Social media and mobile phones with good cameras and computer scientists at Carnegie Mellon University to Internet access are dramatically changing the nature of develop tools, methods and algorithms that will make it human rights documentation, reporting and advocacy. Key to this process, and like YouTube, Live Leak, Vimeo, and Facebook every apropos of this session, is the development of mechanisms week. In Syria, more than 650,000 videos have been to enable "the crowd" (i.e., those individuals around the uploaded to social media sites since the conflict started world who care about human rights and have relevant three years ago. This trove of interest dies down or moves on to new issues or places. In presenting this relevant in the long-term, what is irrelevant to the project, I hope to get feedback from other participants in situation or repetitive, and what is patently false or the workshop on how to achieve this goal, particularly by misleading.

The use of artificial intelligence (AI) in life sciences, or "Life Tech", has increased at a rapid pace. According to World Intellectual Property Organization (WIPO), there has been "a shift from theoretical research to the use of AI technologies in commercial products and services," as reflected in the change in ratio of scientific papers to patent applications over the past decade.1 Indeed, while research into AI began in earnest in the 1950s, more than 1.6 million scientific papers have been published on AI, with more than half of identified AI inventions in the last six years alone.2,3 A review article in Nature Medicine reported last year that despite few peer-reviewed publications on use of machine learning technologies in medical devices, FDA approvals of AI as medical devices have been accelerating.4 Many of these FDA approvals relate to image analysis for diagnostic purposes, such as QuantX, the first AI platform to evaluate breast abnormalities; Aidoc, which detects acute intracranial hemorrhages in head CT scans, assisting radiologists to prioritize patient injuries; and IDx-DR, which analyzes retinal images to detect diabetic retinopathy.

Policy making is an extremely complex process occurring in changing environments and affecting the three pillars of sustainable development: society, economy and the environment. Each political decision in fact implies some form of social reactions, it affects economic and financial aspects and has substantial environmental impacts. Improving decision making in this context could have a huge beneficial impact on all these aspects. There are a number of Artificial Intelligence techniques that could play an important role in improving the policy making process such as decision support and optimization techniques, game theory, data and opinion mining and agent-based simulation. We outline here some potential use of AI technology as it emerged by the European Union (EU) EU FP7 project ePolicy: Engineering the Policy Making Life-Cycle, and we identify some potential research challenges.

To better understand the interplay of censoring and sparsity we develop finite sample properties of nonparametric Cox proportional hazard's model. Due to high impact of sequencing data, carrying genetic information of each individual, we work with over-parametrized problem and propose general class of group penalties suitable for sparse structured variable selection and estimation. Novel non-asymptotic sandwich bounds for the partial likelihood are developed. We establish how they extend notion of local asymptotic normality (LAN) of Le Cam's. Such non-asymptotic LAN principles are further extended to high dimensional spaces where $p \gg n$. Finite sample prediction properties of penalized estimator in non-parametric Cox proportional hazards model, under suitable censoring conditions, agree with those of penalized estimator in linear models.

We present a novel, scalable and Bayesian approach to modelling the occurrence of pairs of symbols (i,j) drawn from a large vocabulary. Observed pairs are assumed to be generated by a simple popularity based selection process followed by censoring using a preference function. By basing inference on the well-founded principle of variational bounding, and using new site-independent bounds, we show how a scalable inference procedure can be obtained for large data sets. State of the art results are presented on real-world movie viewing data.