Moral responsibility is a major concern in automated decision-making, with applications ranging from self-driving cars to kidney exchanges. From the viewpoint of automated systems, the urgent questions are: (a) How can models of moral scenarios and blameworthiness be extracted and learnt automatically from data? (b) How can judgements be computed tractably, given the split-second decision points faced by the system? By building on deep tractable probabilistic learning, we propose a learning regime for inducing models of such scenarios automatically from data and reasoning tractably from them. We report on experiments that compare our system with human judgement in three illustrative domains: lung cancer staging, teamwork management, and trolley problems.
Our contributions here are related to the body of work surrounding MIT's Moral Machine website @cite_3 . For example, @cite_8 build on the theory of @cite_30 by developing a computational model of moral decision-making whose predictions they test against Moral Machine data. Their focus is on learning abstract moral principles via hierarchical Bayesian inference, and although our framework can be used to these ends, it is also flexible with respect to different contexts, and allows constraints on learnt models. @cite_13 develop a method of aggregating the preferences of all participants (again, a secondary feature of our system) in order to make a given decision. However, due to the large numbers of such preference orderings, tractability issues arise and so sampling must be used.
@cite_30 @cite_13 @cite_3 @cite_8
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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. We introduce a computational framework for understanding the structure and dynamics of moral learning, with a focus on how people learn to trade off the interests and welfare of different individuals in their social groups and the larger society. We posit a minimal set of cognitive capacities that together can solve this learning problem: (1) an abstract and recursive utility calculus to quantitatively represent welfare trade-offs; (2) hierarchical Bayesian inference to understand the actions and judgments of others; and (3) meta-values for learning by value alignment both externally to the values of others and internally to make moral theories consistent with one’s own attachments and feelings. Our model explains how children can build from sparse noisy observations of how a small set of individuals make moral decisions to a broad moral competence, able to support an infinite range of judgments and decisions that generalizes even to people they have never met and situations they have not been in or observed. It also provides insight into the causes and dynamics of moral change across time, including cases when moral change can be rapidly progressive, changing values significantly in just a few generations, and cases when it is likely to move more slowly. 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 societ al 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. As artificial intelligence moves ever closer to the goal of producing fully autonomous agents, the question of how to design and implement an artificial moral agent (AMA) becomes increasingly pressing. Robots possessing autonomous capacities to do things that are useful to humans will also have the capacity to do things that are harmful to humans and other sentient beings. Theoretical challenges to developing artificial moral agents result both from controversies among ethicists about moral theory itself, and from computational limits to the implementation of such theories. In this paper the ethical disputes are surveyed, the possibility of a ‘moral Turing Test’ is considered and the computational difficulties accompanying the different types of approach are assessed. Human-like performance, which is prone to include immoral actions, may not be acceptable in machines, but moral perfection may be computationally unattainable. The risks posed by autonomous machines ignorantly or deliberately harming people ... Abstract Big Data is a digital phenomenon that enables the collection and use of massive amounts of data derived from both man and machine. This data is characterized in terms of its volume, variety, velocity, veracity, variability, and its complexity. While Big Data allows firms to rapidly capture, analyze, and exploit information, it can also enable access to data that compromises an individual's privacy. And this can happen either deliberately or inadvertently. Either way, Big Data fosters a discussion of ethical issues relative to the sharing and usage of data. Ethical debates are typically articulated within the context of ethical theories. These theories help to frame our understanding of moral issues. Their use affords insight into the context and the logic of the moral arguments being presented, thereby providing us with a rational mechanism by which to better evaluate whether an intended action or actual outcome is morally right or wrong. Four ethical theories are briefly reviewed in this paper: Kantianism, Utilitarianism, Social Contract Theory, and Virtue Theory. Each theory is than examined to show how it might be employed to examine Big Data issues. The long-standing rationalist tradition in moral psychology emphasizes the role of reason in moral judgment. A more recent trend places increased emphasis on emotion. Although both reason and emotion are likely to play important roles in moral judgment, relatively little is known about their neural correlates, the nature of their interaction, and the factors that modulate their respective behavioral influences in the context of moral judgment. In two functional magnetic resonance imaging (fMRI) studies using moral dilemmas as probes, we apply the methods of cognitive neuroscience to the study of moral judgment. We argue that moral dilemmas vary systematically in the extent to which they engage emotional processing and that these variations in emotional engagement influence moral judgment. These results may shed light on some puzzling patterns in moral judgment observed by contemporary philosophers. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Does sharing moral values encourage people to connect and form communities? The importance of moral homophily (love of same) has been recognized by social scientists, but the types of moral similarities that drive this phenomenon are still unknown. Using both large-scale, observational social-media analyses and behavioral lab experiments, the authors investigated which types of moral similarities influence tie formations. Analysis of a corpus of over 700,000 tweets revealed that the distance between 2 people in a social-network can be predicted based on differences in the moral purity content-but not other moral content-of their messages. The authors replicated this finding by experimentally manipulating perceived moral difference (Study 2) and similarity (Study 3) in the lab and demonstrating that purity differences play a significant role in social distancing. These results indicate that social network processes reflect moral selection, and both online and offline differences in moral purity concerns are particularly predictive of social distance. This research is an attempt to study morality indirectly using an observational big-data study complemented with 2 confirmatory behavioral experiments carried out using traditional social-psychology methodology. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . The actions of a rational agent reveal information about its mental states. These inferred mental states, particularly the agent’s intentions, play an important role in the evaluation of moral permissibility. While previous computational models have shown that beliefs and desires can be inferred from behavior under the assumption of rational action they have critically lacked a third mental state, intentions. In this work, we develop a novel formalism for intentions and show how they can be inferred as counterfactual contrasts over influence diagrams. This model is used to quantitatively explain judgments about intention and moral permissibility in classic and novel trolley problems. The old Asian legend about the blind men and the elephant comes to mind when looking at how different authors of scientific papers describe a piece of related prior work. It turns out that different citations to the same paper often focus on different aspects of that paper and that neither provides a full description of its full set of contributions. In this article, we will describe our investigation of this phenomenon. We studied citation summaries in the context of research papers in the biomedical domain. A citation summary is the set of citing sentences for a given article and can be used as a surrogate for the actual article in a variety of scenarios. It contains information that was deemed by peers to be important. Our study shows that citation summaries overlap to some extent with the abstracts of the papers and that they also differ from them in that they focus on different aspects of these papers than do the abstracts. In addition to this, co-cited articles (which are pairs of articles cited by another article) tend to be similar. We show results based on a lexical similarity metric called cohesion to justify our claims. © 2008 Wiley Periodicals, Inc. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] Recent work shows an important asymmetry in lay intuitions about moral dilemmas. Most people think it is permissible to divert a train so that it will kill one innocent person instead of five, but most people think that it is not permissible to push a stranger in front of a train to save five innocents. We argue that recent emotion-based explanations of this asymmetry have neglected the contribution that rules make to reasoning about moral dilemmas. In two experiments, we find that participants show a parallel asymmetry about versions of the dilemmas that have minimized emotional force. In a third experiment, we find that people distinguish between whether an action violates a moral rule and whether it is, all things considered, wrong. We propose that judgments of whether an action is wrong, all things considered, implicate a complex set of psychological processes, including representations of rules, emotional responses, and assessments of costs and benefits. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Algorithms are increasingly used to make important decisions about human lives, ranging from social benefit assignment to predicting risk of criminal recidivism. The sensitivity of these decisions has raised important questions about ensuring fairness in algorithmic decision making. Recent work has focused on addressing two aspects of fairness: distributional fairness, concerning the fairness of the outcomes of decision making, and procedural fairness, concerning the fairness of the decision-making process. Assessment of procedural fairness relies on human judgments, for example, about the fairness of using certain features in the algorithm making the decision. Yet to our knowledge, almost no prior work examines people’s perceptions of fairness in algorithmic decision making. We propose a framework for understanding why people perceive certain features as fair to be used in algorithms. We define a framework of eight underlying properties of features, such as relevance, volitionality and reliability, and hypothesize that people use these properties to form a heuristic for making moral judgments about the fairness of feature use in decision-making algorithms. We evaluate our framework through a series of scenario-based surveys with 576 people. We find that, based on a person’s assessment of the eight underlying properties in our exemplar scenario, we can predict if they will perceive a feature as fair to be used with at least 87 accuracy. Use of machine learning techniques for educational proposes (or educational data mining) is an emerging field aimed at developing methods of exploring data from computational educational settings and discovering meaningful patterns. The stored data (virtual courses, e-learning log file, demographic and academic data of students, admissions registration info, and so on) can be useful for machine learning algorithms. In this article, we cite the most current articles that use machine learning techniques for educational proposes and we present a case study for predicting students' marks. Students' key demographic characteristics and their marks in a small number of written assignments can constitute the training set for a regression method in order to predict the student's performance. Finally, a prototype version of software support tool for tutors has been constructed. Machine learning is a means to derive artificial intelligence by discovering patterns in existing data. Here, we show that applying machine learning to ordinary human language results in human-like semantic biases. We replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web. Our results indicate that text corpora contain recoverable and accurate imprints of our historic biases, whether morally neutral as toward insects or flowers, problematic as toward race or gender, or even simply veridical, reflecting the status quo distribution of gender with respect to careers or first names. Our methods hold promise for identifying and addressing sources of bias in culture, including technology. Historically, papers have been physically bound to the journal in which they were published; but in the digital age papers are available individually, no longer tied to their respective journals. Hence, papers now can be read and cited based on their own merits, independently of the journal's physical availability, reputation, or impact factor (IF). We compare the strength of the relationship between journals' IFs and the actual citations received by their respective papers from 1902 to 2009. Throughout most of the 20th century, papers' citation rates were increasingly linked to their respective journals' IFs. However, since 1990, the advent of the digital age, the relation between IFs and paper citations has been weakening. This began first in physics, a field that was quick to make the transition into the electronic domain. Furthermore, since 1990 the overall proportion of highly cited papers coming from highly cited journals has been decreasing and, of these highly cited papers, the proportion not coming from highly cited journals has been increasing. Should this pattern continue, it might bring an end to the use of the IF as a way to evaluate the quality of journals, papers, and researchers. © 2012 Wiley Periodicals, Inc. This article explores the potential impact of individual consumer decisions to transmit or withhold word-of-mouth information on the flow of information in a market. Information flow is examined in networks composed of graphs and nodes, where graphs represent channels that foster information flow and nodes represent sentient decision makers who can potentially impede or foster information flow. We assert that actors in embedded markets first judge the moral hazards imposed by the information they consider for transmission and the social context of transmission, and then moderate their decisions to transmit the information in light of these judgements. We analyze this process using a modified version of Marshall Sahlins's social exchange theory, examine the plausibility of this analysis using two laboratory experiments, and explore the consequences of this behavior at the aggregate level using a computer simulation. Our view highlights the importance of the interplay of individuals and their social context for understanding word-of-mouth processes. We present a study which investigated how and why users of Mozilla Firefox use multiple tabs and windows during web browsing. The detailed web browsing usage of 21 participants was logged over a period of 13 to 21 days each, and was supplemented by qualitative data from diary entries and interviews. Through an examination of several measures of their tab usage, we show that our participants had a strong preference for the use of tabs rather than multiple windows. We report the reasons they cited for using tabs, and the advantages over multiple windows. We identify several common tab usage patterns which browsers could explicitly support. Finally, we look at how tab usage affects web page revisitation. Most of our participants switched tabs more often than they used the back button, making tab switching the second most important navigation mechanism in the browser, after link clicking.