Probabilistic judgment aggregation is concerned with aggregating judgments about probabilities of logically related issues. It takes as input imprecise probabilistic judgments over the issues given by a group of agents and defines rules of aggregating the individual judgments into a collective opinion representative for the group. The process of aggregation can be subject to constraints, i.e., aggregation rules can be required to satisfy certain properties. We explore how probabilistic independence constraints can be represented and incorporated into the aggregation process.

Abstract Smart devices that operate in a shared environment with people need to be aligned with their values and requirements. We study the problem of multiple stakeholders informing the same device on what the right thing to do is. Specifically, we focus on how to reach a middle ground among the stakeholders inevitably incoherent judgments on what the rules of conduct for the device should be. We formally define a notion of middle ground and discuss the main properties of this notion. Then, we identify three sufficient conditions on the class of Horn expressions for which middle grounds are guaranteed to exist. We provide a polynomial time algorithm that computes middle grounds, under these conditions. We also show that if any of the three conditions is removed then middle grounds for the resulting (larger) class may not exist. Finally, we implement our algorithm and perform experiments using data from the Moral Machine Experiment. We present conflicting rules for different countries and how the algorithm finds the middle ground in this case.

Abstract Egalitarian considerations play a central role in many areas of social choice theory. Applications of egalitarian principles range from ensuring everyone gets an equal share of a cake when deciding how to divide it, to guaranteeing balance with respect to gender or ethnicity in committee elections. Yet, the egalitarian approach has received little attention in judgment aggregation—a powerful framework for aggregating logically interconnected issues. We make the first steps towards filling that gap. We introduce axioms capturing two classical interpretations of egalitarianism in judgment aggregation and situate these within the context of existing axioms in the pertinent framework of belief merging. We then explore the relationship between these axioms and several notions of strategyproofness from social choice theory at large. Finally, a novel egalitarian judgment aggregation rule stems from our analysis; we present complexity results concerning both outcome determination and strategic manipulation for that rule.

An autonomous system is constructed by a manufacturer, operates in a society subject to norms and laws, and interacts with end users. All of these actors are stakeholders affected by the behavior of the autonomous system. We address the challenge of how the ethical views of such stakeholders can be integrated in the behavior of an autonomous system. We propose an ethical recommendation component called Jiminy which uses techniques from normative systems and formal argumentation to reach moral agreements among stakeholders. A Jiminy represents the ethical views of each stakeholder by using normative systems, and has three ways of resolving moral dilemmas that involve the opinions of the stakeholders. First, the Jiminy considers how the arguments of the stakeholders relate to one another, which may already resolve the dilemma. Secondly, the Jiminy combines the normative systems of the stakeholders such that the combined expertise of the stakeholders may resolve the dilemma. Thirdly, and only if these two other methods have failed, the Jiminy uses context-sensitive rules to decide which of the stakeholders take preference over the others. At the abstract level, these three methods are characterized by adding arguments, adding attacks between arguments, and revising attacks between arguments. We show how a Jiminy can be used not only for ethical reasoning and collaborative decision-making, but also to provide explanations about ethical behavior.

The ability to make videos for different aspect ratios (known as video retargeting) contributes to optimal viewing experience on different video platforms. In this paper, we present an idiom-based tool for retargeting videos, from the most common 16:9 aspect ratio into narrower aspect ratios. In contrast to earlier retargeting approaches, which distort the video and are completely automated, our tool enables cropping and panning with user input and oversight. Users can select and order idioms from six cinematic idioms to control video retargeting, and the tool applies selected idioms in order and generates the retargeting results. We performed a pilot study for the feasibility of the tool, and conducted quantitative analysis to inform further work on crafting intelligent cropping and panning tools. In addition, we interviewed an experience video editor on how retargeting is done manually and the quality of the output of the tool.

Abstract The spread of information in a social network has received renewed interest as social media becomes an increasingly popular channel of communication. We are interested in the phenomenon of social diffusion of a piece of information in the presence of a contradicting information in the network. Specifically we explore the use of formal methods for verification in studying this phenomena. Using Monte Carlo simulation and the probabilistic model checker (PRISM) we are able to represent social networks and confirm an earlier conjecture that disseminating new information rapidly is resistant to the presence of contradicting information.

The spread and exchange of information among people has been formally studied in the social sciences since the 1950s. The ways in which we do communicate has changed multiple times since. In particular, since the digitalization of communication methods, the speed of information exchange, the connectivity among people and with artificial agents, the reachability of information contents have all changed. Moreover, we now have an almost permanent record of what knowledge has been communicated, when, by whom. This novel status of information exchange has rapidly become a new field of studies on its own. The understanding of networked communication has thus become ubiquitous in multi-agent systems in AI, in epistemic-social logics and of course in social network analysis. Works that address this challenge are also split across these fields. This special issue is devoted to the theme of the ECAI workshop ‘Netreason: Reasoning About Social Networks’ (organized online on September...

An institution, be it a body of government, commercial enterprise, or a service, cannot interact directly with a person. Instead, a model is created to represent us. We argue the existence of a new high-fidelity type of person model which we call a digital voodoo doll. We conceptualize it and compare its features with existing models of persons. Digital voodoo dolls are distinguished by existing completely beyond the influence and control of the person they represent. We discuss the ethical issues that such a lack of accountability creates and argue how these concerns can be mitigated.

Recent advances in artificial intelligence (AI) have led to an increased focus on automating media production. One relevant application area for AI is using speech recognition to create subtitles and closed captions for videos. The AI methods based on machine learning are still not sufficiently reliable in terms of producing perfect or acceptable subtitles. To compensate for this unreliability, AI can be used to build tools that support, rather than replace, human efforts and to create semi-automated workflows. In this paper, we present a prototype for including automated speech recognition for subtitling in an existing production-grade video editing tool. We devised an experiment with 25 participants and tested the efficiency and effectiveness of this tool compared to a fully manual process. The results show that there is a significant increase in both effectiveness and efficiency for novices in subtitling. Furthermore, the participants found the augmented process to be more demanding. We identify some usability issues and design choices that pertain to making augmented subtitling easier.

We provide a comprehensive analysis of the computational complexity of the outcome determination problem for the most important aggregation rules proposed in the literature on logic-based judgment aggregation. Judgment aggregation is a powerful and flexible framework for studying problems of collective decision making that has attracted interest in a range of disciplines, including Legal Theory, Philosophy, Economics, Political Science, and Artificial Intelligence. The problem of computing the outcome for a given list of individual judgments to be aggregated into a single collective judgment is the most fundamental algorithmic challenge arising in this context. Our analysis applies to several different variants of the basic framework of judgment aggregation that have been discussed in the literature, as well as to a new framework that encompasses all existing such frameworks in terms of expressive power and representational succinctness.

An autonomous system is constructed by a manufacturer, operates in a society subject to norms and laws, and is interacting with end-users. We address the challenge of how the moral values and views of all stakeholders can be integrated and reflected in the moral behavior of the autonomous system. We propose an artificial moral agent architecture that uses techniques from normative systems and formal argumentation to reach moral agreements among stakeholders. We show how our architecture can be used not only for ethical practical reasoning and collaborative decision-making, but also for the explanation of such moral behavior.

Judgment aggregation (JA) studies how to aggregate truth valuations on logically related issues. Computing the outcome of aggregation procedures is notoriously computationally hard, which is the likely reason that no implementation of them exists as of yet. However, even hard problems sometimes need to be solved. The worst-case computational complexity of answer set programming (ASP) matches that of most problems in judgment aggregation. We take advantage of this and propose a natural and modular encoding of various judgment aggregation procedures and related problems in JA into ASP. With these encodings, we achieve two results: (1) paving the way towards constructing a wide range of new benchmark instances (from JA) for answer set solving algorithms; and (2) providing an automated tool for researchers in the area of judgment aggregation.

Artificial intelligence (AI) systems are becoming part of our lives and societies. The more decisions such systems make for us, the more we need to ensure that the decisions they make have a positive individual and societal ethical impact. How can we estimate how good a system is at making ethical decisions? Benchmarking is used to evaluate how good a machine or a process performs with respect to industry bests. In this paper we argue that (some) ethical dilemmas can be used as benchmarks for estimating the ethical performance of an autonomous system. We advocate that an open source repository of such dilemmas should be maintained. We present a prototype of such a repository available at https://imdb. uib.no/dilemmaz/articles/all1.

Ethics by Design concerns the methods, algorithms and tools needed to endow autonomous agents with the capability to reason about the ethical aspects of their decisions, and the methods, tools and formalisms to guarantee that an agent's behavior remains within given moral bounds. In this context some questions arise: How and to what extent can agents understand the social reality in which they operate, and the other intelligences (AI, animals and humans) with which they co-exist? What are the ethical concerns in the emerging new forms of society, and how do we ensure the human dimension is upheld in interactions and decisions by autonomous agents?. But overall, the central question is: "Can we, and should we, build ethically-aware agents?" This paper presents initial conclusions from the thematic day of the same name held at PRIMA2017, on October 2017.

With recent advances in artificial intelligence and the rapidly increasing importance of autonomous intelligent systems in society, it is becoming clear that artificial agents will have to be designed to comply with complex ethical standards. As we work to develop moral machines, we also push the boundaries of existing legal categories. The most pressing question is what kind of ethical decision-making our machines are actually able to engage in. Both in law and in ethics, the concept of agency forms a basis for further legal and ethical categorisations, pertaining to decision-making ability. Hence, without a cross-disciplinary understanding of what we mean by ethical agency in machines, the question of responsibility and liability cannot be clearly addressed. Here we make first steps towards a comprehensive definition, by suggesting ways to distinguish between implicit and explicit forms of ethical agency.

One of the better studied properties for operators in judgment aggregation is independence, which essentially dictates that the collective judgment on one issue should not depend on the individual judgments given on some other issue(s) in the same agenda. Independence, although considered a desirable property, is too strong, because together with mild additional conditions it implies dictatorship. We propose here a weakening of independence, named agenda separability: a judgment aggregation rule satisfies it if, whenever the agenda is composed of several independent sub-agendas, the resulting collective judgment sets can be computed separately for each sub-agenda and then put together. We show that this property is discriminant, in the sense that among judgment aggregation rules so far studied in the literature, some satisfy it and some do not. We briefly discuss the implications of agenda separability on the computation of judgment aggregation rules.

Judgment aggregation problems form a class of collective decision-making problems represented in an abstract way, subsuming some well known problems such as voting. A collective decision can be reached in many ways, but a direct one-step aggregation of individual decisions is arguably most studied. Another way to reach collective decisions is by iterative consensus building – allowing each decision-maker to change their individual decision in response to the choices of the other agents until a consensus is reached. Iterative consensus building has so far only been studied for voting problems. Here we propose an iterative judgment aggregation algorithm, based on movements in an undirected graph, and we study for which instances it terminates with a consensus. We also compare the computational complexity of our itterative procedure with that of related judgment aggregation operators.

Crowdsourcing, together with its related approaches, has become very popular in recent years. All crowdsourcing processes involve the participation of a digital crowd, a large number of people that access a single Internet platform or shared service. In this paper we explore the possibility of applying formal methods, typically used for the verification of software and hardware systems, in analysing the behavior of a digital crowd. More precisely, we provide a formal description language for specifying digital crowds. We represent digital crowds in which the agents do not directly communicate with each other. We further show how this specification can provide the basis for sophisticated formal methods, in particular formal verification.

Several recent articles have studied judgment aggregation rules under the point of view of the normative properties they satisfy. However, a further criterion to choose between rules is their computational complexity. Here we review a few rules already proposed and studied in the literature, and identify the complexity of computing the outcome.

One of the better studied properties for operators in judgment aggregation is independence, which essentially dictates that the collective judgment on one issue should not depend on the individual judgments given on some other issue(s) in the same agenda. Independence is a desirable property for various reasons, but unfortunately it is too strong, as, together with mild additional conditions, it implies dictatorship. We propose here a weakening of independence, named agenda separability and show that this property is discriminant, i.e., some judgment aggregation rules satisfy it, others do not.

Many voting rules are based on some minimization principle. Likewise, in the field of logic-based knowledge representation and reasoning, many belief change or inconsistency handling operators also make use of minimization. Surprisingly, minimization has not played a major role in the field of judgment aggregation, in spite of its proximity to voting theory and logic-based knowledge representation and reasoning. Here we make a step in this direction and study six judgment aggregation rules; two of them, based on distances, have been previously defined; the other four are new, and all inspired both by voting theory and knowledge representation and reasoning. We study the inclusion relationships between these rules and address some of their social choice theoretic properties.