Pareto Optimality and Strategy Proofness in Group Argument Evaluation (Extended Version)

An inconsistent knowledge base can be abstracted as a set of arguments and a defeat relation among them. There can be more than one consistent way to evaluate such an argumentation graph. Collective argument evaluation is the problem of aggregating the opinions of multiple agents on how a given set of arguments should be evaluated. It is crucial not only to ensure that the outcome is logically consistent, but also satisfies measures of social optimality and immunity to strategic manipulation. This is because agents have their individual preferences about what the outcome ought to be. In the current paper, we analyze three previously introduced argument-based aggregation operators with respect to Pareto optimality and strategy proofness under different general classes of agent preferences. We highlight fundamental trade-offs between strategic manipulability and social optimality on one hand, and classical logical criteria on the other. Our results motivate further investigation into the relationship between social choice and argumentation theory. The results are also relevant for choosing an appropriate aggregation operator given the criteria that are considered more important, as well as the nature of agents' preferences

A Voting-Based System for Ethical Decision Making

We present a general approach to automating ethical decisions, drawing on machine learning and computational social choice. In a nutshell, we propose to learn a model of societal preferences, and, when faced with a specific ethical dilemma at runtime, efficiently aggregate those preferences to identify a desirable choice. We provide a concrete algorithm that instantiates our approach; some of its crucial steps are informed by a new theory of swap-dominance efficient voting rules. Finally, we implement and evaluate a system for ethical decision making in the autonomous vehicle domain, using preference data collected from 1.3 million people through the Moral Machine website.Comment: 25 pages; paper has been reorganized, related work and discussion sections have been expande

Experimental Assessment of Aggregation Principles in Argumentation-Enabled Collective Intelligence

On the Web, there is always a need to aggregate opinions from the crowd (as in posts, social networks, forums, etc.). Different mechanisms have been implemented to capture these opinions such as Like in Facebook, Favorite in Twitter, thumbs-up/-down, flagging, and so on. However, in more contested domains (e.g., Wikipedia, political discussion, and climate change discussion), these mechanisms are not sufficient, since they only deal with each issue independently without considering the relationships between different claims. We can view a set of conflicting arguments as a graph in which the nodes represent arguments and the arcs between these nodes represent the defeat relation. A group of people can then collectively evaluate such graphs. To do this, the group must use a rule to aggregate their individual opinions about the entire argument graph. Here we present the first experimental evaluation of different principles commonly employed by aggregation rules presented in the literature. We use randomized controlled experiments to investigate which principles people consider better at aggregating opinions under different conditions. Our analysis reveals a number of factors, not captured by traditional formal models, that play an important role in determining the efficacy of aggregation. These results help bring formal models of argumentation closer to real-world application

Interval methods for judgment aggregation in argumentation

Given a set of conflicting arguments, there can exist multiple plausible opinions about which arguments should be accepted, rejected, or deemed undecided. Recent work explored some operators for deciding how multiple such judgments should be aggregated. Here, we generalize this line of study by introducing a family of operators called interval aggregation methods, which contain existing operators as instances. While these methods fail to output a complete labelling in general, we show that it is possible to transform a given aggregation method into one that does always yield collectively rational labellings. This employs the down-admissible and up-complete constructions of Caminada and Pigozzi. For interval methods, collective rationality is attained at the expense of a strong Independence postulate, but we show that an interesting weakening of the Independence postulate is retained

A Computational Model of Commonsense Moral Decision Making

We introduce a new computational model of moral decision making, drawing on a recent theory of commonsense moral learning via social dynamics. Our model describes moral dilemmas as a utility function that computes trade-offs in values over abstract moral dimensions, which provide interpretable parameter values when implemented in machine-led ethical decision-making. Moreover, characterizing the social structures of individuals and groups as a hierarchical Bayesian model, we show that a useful description of an individual's moral values - as well as a group's shared values - can be inferred from a limited amount of observed data. Finally, we apply and evaluate our approach to data from the Moral Machine, a web application that collects human judgments on moral dilemmas involving autonomous vehicles

Experimental assessment of aggregation principles in argumentation-enabled collective intelligence

On the Web, there is always a need to aggregate opinions from the crowd (as in posts, social networks, forums, etc.). Different mechanisms have been implemented to capture these opinions such as Like in Facebook, Favorite in Twitter, thumbsup/ down, flagging, and so on. However, in more contested domains (e.g. Wikipedia, political discussion, and climate change discussion) these mechanisms are not sufficient since they only deal with each issue independently without considering the relationships between different claims.We can view a set of conflicting arguments as a graph in which the nodes represent arguments and the arcs between these nodes represent the defeat relation. A group of people can then collectively evaluate such graphs. To do this, the group must use a rule to aggregate their individual opinions about the entire argument graph. Here, we present the first experimental evaluation of different principles commonly employed by aggregation rules presented in the literature. We use randomized controlled experiments to investigate which principles people consider better at aggregating opinions under different conditions. Our analysis reveals a number of factors, not captured by traditional formal models, that play an important role in determining the efficacy of aggregation. These results help bring formal models of argumentation closer to real-world application

Addressing accountability in highly autonomous virtual assistants

Building from a survey specifically developed to address the rising concerns of highly autonomous virtual assistants; this paper presents a multi-level taxonomy of accountability levels specifically adapted to virtual assistants in the context of Human-Human-Interaction (HHI). Based on research findings, the authors recommend the integration of the variable of accountability as capital in the development of future applications around highly automated systems. This element inserts a sense of balance in terms of integrity between users and developers enhancing trust in the interactive process. Ongoing work is being dedicated to further understand to which extent different contexts affect accountability in virtual assistants

High levels of anti-tuberculin (IgG) antibodies correlate with the blocking of T-cell proliferation in individuals with high exposure to Mycobacterium tuberculosis

SummaryObjectivesTo determine the effect of anti-tuberculin antibodies in the T-cell proliferation in response to tuberculin and Candida antigens in individuals with different levels of tuberculosis (TB) risk.MethodsSixteen high-risk TB individuals, 30 with an intermediate TB risk (group A), and 45 with a low TB risk (group B), as well as 49 control individuals, were studied. Tuberculin skin test (TST) results were analyzed and serum levels of antibodies (IgG and IgM) against purified protein derivative (PPD) were measured by ELISA. Tuberculin and Candida antigens were used to stimulate T-cell proliferation in the presence of human AB serum or autologous serum.ResultsHigh levels of anti-tuberculin IgG antibodies were found to be significantly associated with the blocking of T-cell proliferation responses in cultures stimulated with tuberculin but not with Candida antigens in the presence of autologous serum. This phenomenon was particularly frequent in high-risk individuals with high levels of anti-tuberculin IgG antibodies in the autologous serum when compared to the other risk groups, which exhibited lower levels of anti-tuberculin antibodies.ConclusionsAlthough cellular immunity plays a central role in the protection against TB, humoral immunity is critical in the control of Mycobacterium tuberculosis infection in high-risk individuals with latent TB infection