Personal Social Ontology (PSO), it is proposed, is how an individual perceives the ontological properties of terms. For example, an absolute fatalist would arguably use terms that remove any form of agency from a person. Such fatalism has the impact of ontologically defining acts such as winning, victory and success in a manner that is contrary to how a non-fatalist would ontologically define them. While both the said fatalist and non-fatalist would agree on the dictionary definition of these terms, they would differ on specifically how they can be brought about. This difference between the two individuals can be induced from their usage of these terms, i.e., the co-occurrence of these terms with other terms. As such a quantification of this such co-occurrence offers an avenue to characterise the social ontological views of the speaker. In this paper we ask, what specific term co-occurrence should be measured in order to obtain a valid and reliable psychometric measure of a persons social ontology? We consider the social psychology and social neuroscience literature to arrive at a list of social concepts that can be considered principal features of personal social ontology, and then propose an NLP pipeline to capture the articulation of these terms in language.

This story is part of Future Tense Fiction, a monthly series of short stories from Future Tense and Arizona State University's Center for Science and the Imagination about how technology and science will change our lives. Amanda sat at her desk, picking at the same $30 Little Gem salad she ordered daily, suffering a small burning sensation in her gut that was triggered either by acid reflux or the dying embers of her rapidly expiring conscience. Of course, it was standard procedure for her husband to demand that the security firm Dark Metal surveil potential new hires for any of his multibillion-dollar companies, but this was the first time Amanda had been involved in contracting the private intelligence agency herself. Seedlings is your venture, Reid had promised her, even though he'd named himself CEO. I want you to take the lead on this. Amanda was COO of Seedlings and reported to her husband, who dismissed Amanda's concerns about the legal ramifications of their actions. Worrying about the law was something poor people did, Reid insisted. Besides, she'd never seen Reid do anything that nefarious with this type of information. But Maggie Everett was the type of candidate that pleased Reid. Amanda had done her job, which was to find Maggie, and the people at Dark Metal had done theirs, which was to surveil her and create a comprehensive biographical profile. This seemed like overkill to Amanda. Maggie wasn't in the running to become a high-profile executive at one of Reid's billion-dollar firms. She was being interviewed to work at a preschool. Certainly, Seedlings differed from other private preschools--there was the possibility Maggie would be exposed to confidential information. But this was what NDAs were for. Unleashing a network of spies upon a poor teacher who would ultimately be responsible for 10 toddlers seemed like an absurd waste of resources. And this was just Phase 1. Phase 2 would have to wait until after Maggie was hired, of course. Amanda reopened Dark Metal's inch-thick dossier. The logline: Maggie was smart but stupid. Smart: She'd majored in English at Yale, then received an MFA in creative writing from Brown, and finally a master's in early childhood education from Columbia. Stupid: She'd accumulated $103,345 in student debt, which she'd never pay off unless she took a job somewhere like Seedlings.

Decisions such as which movie to watch next, which song to listen to, or which product to buy online, are increasingly influenced by recommender systems and user models that incorporate information on users' past behaviours, preferences, and digitally created content. Machine learning models that enable recommendations and that are trained on user data may unintentionally leverage information on human characteristics that are considered vulnerabilities, such as depression, young age, or gambling addiction. The use of algorithmic decisions based on latent vulnerable state representations could be considered manipulative and could have a deteriorating impact on the condition of vulnerable individuals. In this paper, we are concerned with the problem of machine learning models inadvertently modelling vulnerabilities, and want to raise awareness for this issue to be considered in legislation and AI ethics. Hence, we define and describe common vulnerabilities, and illustrate cases where they are likely to play a role in algorithmic decision-making. We propose a set of requirements for methods to detect the potential for vulnerability modelling, detect whether vulnerable groups are treated differently by a model, and detect whether a model has created an internal representation of vulnerability. We conclude that explainable artificial intelligence methods may be necessary for detecting vulnerability exploitation by machine learning-based recommendation systems.

MassMutual, a $30 billion per year life insurance company, had a problem. It was 2013 and, along with the rest of the insurance industry, it was bedeviled by fraud. According to FBI estimates, fraud sets the U.S. insurance industry (and policyholders) back by $40 billion a year. "We had to get much better at detecting fraud in real time," says Sears Merritt, MassMutual's chief of technology strategy and data science. So MassMutual launched an innovative collaboration between the company's data scientists and its line managers.

Vladimir Putin was not in attendance, but his loyal lieutenants were. On 14 July last year, the Russian prime minister, Dmitry Medvedev, and several members of his cabinet convened in an office building on the outskirts of Moscow. On to the stage stepped a boyish-looking psychologist, Michal Kosinski, who had been flown from the city centre by helicopter to share his research. "There was Lavrov, in the first row," he recalls several months later, referring to Russia's foreign minister. "You know, a guy who starts wars and takes over countries." Kosinski, a 36-year-old assistant professor of organisational behaviour at Stanford University, was flattered that the Russian cabinet would gather to listen to him talk. "Those guys strike me as one of the most competent and well-informed groups," he tells me. Kosinski's "stuff" includes groundbreaking research into technology, mass persuasion and artificial intelligence (AI) – research that inspired the creation of the political consultancy Cambridge Analytica. Five years ago, while a graduate student at Cambridge University, he showed how even benign activity on Facebook could reveal personality traits – a discovery that was later exploited by the data-analytics firm that helped put Donald Trump in the White House.

Data from millions of Facebook users who used a popular personality app, including their answers to intimate questionnaires, was left exposed online for anyone to access, a New Scientist investigation has found. Academics at the University of Cambridge distributed the data from the personality quiz app myPersonality to hundreds of researchers via a website with insufficient security provisions, which led to it being left vulnerable to access for four years. Gaining access illicitly was relatively easy. The data was highly sensitive, revealing personal details of Facebook users, such as the results of psychological tests. It was meant to be stored and shared anonymously, however such poor precautions were taken that deanonymising would not be hard.

We demonstrate a system for predicting gaming related properties from Twitter accounts. Our system predicts various traits of users based on the tweets publicly available in their profiles. Such inferred traits include degrees of tech-savviness and knowledge on computer games, actual gaming performance, preferred platform, degree of originality, humor and influence on others. Our system is based on machine learning models trained on crowd-sourced data. It allows people to select Twitter accounts of their fellow gamers, examine the trait predictions made by our system, and the main drivers of these predictions. We present empirical results on the performance of our system based on its accuracy on our crowd-sourced dataset.

We demonstrate an approach to predict latent personal attributes including user demographics, online personality, emotions and sentiments from texts published on Twitter. We rely on machine learning and natural language processing techniques to learn models from user communications. We first examine individual tweets to detect emotions and opinions emanating from them, and then analyze all the tweets published by a user to infer latent traits of that individual. We consider various user properties including age, gender, income, education, relationship status, optimism and life satisfaction. We focus on Ekman’s six emotions: anger, joy, surprise, fear, disgust and sadness. Our work can help social network users to understand how others may perceive them based on how they communicate in social media, in addition to its evident applications in online sales and marketing, targeted advertising, large scale polling and healthcare analytics.