71 research outputs found
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
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