The Golden Rule as a Heuristic to Measure the Fairness of Texts Using Machine Learning

In this paper we present a natural language programming framework to consider how the fairness of acts can be measured. For the purposes of the paper, a fair act is defined as one that one would be accepting of if it were done to oneself. The approach is based on an implementation of the golden rule (GR) in the digital domain. Despite the GRs prevalence as an axiom throughout history, no transfer of this moral philosophy into computational systems exists. In this paper we consider how to algorithmically operationalise this rule so that it may be used to measure sentences such as: the boy harmed the girl, and categorise them as fair or unfair. A review and reply to criticisms of the GR is made. A suggestion of how the technology may be implemented to avoid unfair biases in word embeddings is made - given that individuals would typically not wish to be on the receiving end of an unfair act, such as racism, irrespective of whether the corpus being used deems such discrimination as praiseworthy

Individuals' insight into intrapersonal externalities

An intrapersonal externality exists when an individual's decisions affect the outcomes of her future decisions. It can result in decreasing or increasing average returns to the rate of consumption, as occurs in addiction or exercise. Experimentation using the Harvard Game, which models intrapersonal externalities, has found differences in decision making between drug users and control subjects, leading to the argument that these externalities influence the course of illicit drug use. Nevertheless, it is unclear how participants who behave optimally conceptualise the problem. We report two experiments using a simplified Harvard Game, which tested the differences in contingency knowledge between participants who chose optimally and participants who did not. Those who demonstrated optimal performance exhibited both a pattern of correct responses and systematic errors to questions about the payoff schedules. The pattern suggested that they learned explicit knowledge of the change in reinforcement on a trail-by-trial basis. They did not have, or need, a full knowledge of the historical interaction leading to each payoff. We also found no evidence of choice differences between participants who were given a guaranteed payment and participants who were paid contingent on their performance, but those given a guaranteed payment were able to report more contingency knowledge as the experiment progressed, suggesting that they explored more rather than settling into a routine. Experiment 2 showed that using a fixed inter-trial interval did not change the results

What your Facebook Profile Picture Reveals about your Personality

People spend considerable effort managing the impressions they give others. Social psychologists have shown that people manage these impressions differently depending upon their personality. Facebook and other social media provide a new forum for this fundamental process; hence, understanding people's behaviour on social media could provide interesting insights on their personality. In this paper we investigate automatic personality recognition from Facebook profile pictures. We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals. For example, extroverts and agreeable individuals tend to have warm colored pictures and to exhibit many faces in their portraits, mirroring their inclination to socialize; while neurotic ones have a prevalence of pictures of indoor places. Then, we propose a classification approach to automatically recognize personality traits from these visual features. Finally, we compare the performance of our classification approach to the one obtained by human raters and we show that computer-based classifications are significantly more accurate than averaged human-based classifications for Extraversion and Neuroticism

Intrapersonal externalities: when decisions help or hinder your future self

Intrapersonal Externalities are outcomes from a decision which affect the payoffs from future decisions. Positive intrapersonal externalities represent self-investment in one's future self, such as learning a musical instrument or exercising. Negative intrapersonal externalities represent a disinvestment in one's future self, such as eating sugary food or smoking a cigarette. An individual who succumbs to the lure of an immediate payoff in lieu of future potential earnings is considered to be choosing myopically, and it is hypothesised that addictions are one such situation. This failure of the decision-making system to optimise its choices contrasts with delay discounting, in which an individual is assumed to choose rationally but to discount the future, and impulsive disinhibition, in which an individual is assumed to be unable to control their actions even while they state a preference for an action they do not take. This thesis starts by measuring the relationship between delay discounting and impulsive disinhibition with a range of addictive behaviours in a large online study, and finds that while they are consistent predictors there is further variance to explain. It then examines the validity of the intrapersonal externalities model, and finds that it adds independent variance beyond delay discounting and impulsivity in predicting smoking behaviour; there is no evidence that performance in intrapersonal externalities tasks is related to trait impulsivity. It also uses intrapersonal externalities in the laboratory to study advice seeking and taking in an impulsive decision-making context, which would be incompatible with delay discounting or impulsive disinhibition theory. This thesis concludes that the intrapersonal externalities model has been shown to be a viable third model to understand addictive decisions, and suggests that it could be extended to study social addictions

How are you doing? : emotions and personality in Facebook

User generated content on social media sites is a rich source of information about latent variables of their users. Proper mining of this content provides a shortcut to emotion and personality detection of users without filling out questionnaires. This in turn increases the application potential of personalized services that rely on the knowledge of such latent variables. In this paper we contribute to this emerging domain by studying the relation between emotions expressed in approximately 1 million Facebook (FB) status updates and the users' age, gender and personality. Additionally, we investigate the relations between emotion expression and the time when the status updates were posted. In particular, we find that female users are more emotional in their status posts than male users. In addition, we find a relation between age and sharing of emotions. Older FB users share their feelings more often than young users. In terms of seasons, people post about emotions less frequently in summer. On the other hand, December is a time when people are more likely to share their positive feelings with their friends. We also examine the relation between users' personality and their posts. We find that users who have an open personality express their emotions more frequently, while neurotic users are more reserved to share their feelings

Intrapersonal externalities: when decisions help or hinder your future self

Intrapersonal Externalities are outcomes from a decision which affect the payoffs from future decisions. Positive intrapersonal externalities represent self-investment in one's future self, such as learning a musical instrument or exercising. Negative intrapersonal externalities represent a disinvestment in one's future self, such as eating sugary food or smoking a cigarette. An individual who succumbs to the lure of an immediate payoff in lieu of future potential earnings is considered to be choosing myopically, and it is hypothesised that addictions are one such situation. This failure of the decision-making system to optimise its choices contrasts with delay discounting, in which an individual is assumed to choose rationally but to discount the future, and impulsive disinhibition, in which an individual is assumed to be unable to control their actions even while they state a preference for an action they do not take. This thesis starts by measuring the relationship between delay discounting and impulsive disinhibition with a range of addictive behaviours in a large online study, and finds that while they are consistent predictors there is further variance to explain. It then examines the validity of the intrapersonal externalities model, and finds that it adds independent variance beyond delay discounting and impulsivity in predicting smoking behaviour; there is no evidence that performance in intrapersonal externalities tasks is related to trait impulsivity. It also uses intrapersonal externalities in the laboratory to study advice seeking and taking in an impulsive decision-making context, which would be incompatible with delay discounting or impulsive disinhibition theory. This thesis concludes that the intrapersonal externalities model has been shown to be a viable third model to understand addictive decisions, and suggests that it could be extended to study social addictions

Musical preferences predict personality: evidence from active listening and Facebook likes

Research over the past decade has shown that various personality traits are communicated through musical preferences. One limitation of that research is external validity, as most studies have assessed individual differences in musical preferences using self-reports of music-genre preferences. Are personality traits communicated through behavioral manifestations of musical preferences? We addressed this question in two large-scale online studies with demographically diverse populations. Study 1 (N = 22,252) shows that reactions to unfamiliar musical excerpts predicted individual differences in personality—most notably, openness and extraversion—above and beyond demographic characteristics. Moreover, these personality traits were differentially associated with particular music-preference dimensions. The results from Study 2 (N = 21,929) replicated and extended these findings by showing that an active measure of naturally occurring behavior, Facebook Likes for musical artists, also predicted individual differences in personality. In general, our findings establish the robustness and external validity of the links between musical preferences and personality

Latent human traits in the language of social media: An open-vocabulary approach

Over the past century, personality theory and research has successfully identified core sets of characteristics that consistently describe and explain fundamental differences in the way people think, feel and behave. Such characteristics were derived through theory, dictionary analyses, and survey research using explicit self-reports. The availability of social media data spanning millions of users now makes it possible to automatically derive characteristics from behavioral data-language use-at large scale. Taking advantage of linguistic information available through Facebook, we study the process of inferring a new set of potential human traits based on unprompted language use. We subject these new traits to a comprehensive set of evaluations and compare them with a popular five factor model of personality. We find that our language-based trait construct is often more generalizable in that it often predicts non-questionnaire-based outcomes better than questionnaire-based traits (e.g. entities someone likes, income and intelligence quotient), while the factors remain nearly as stable as traditional factors. Our approach suggests a value in new constructs of personality derived from everyday human language use

Cross-Departmental Materials Development Through Lesson Study

資料・研究ノー

Do Facebook status updates reflect subjective well-being?

Ministry of Education, Singapore under its Academic Research Funding Tier