Integrating Cultural Knowledge into Artificially Intelligent Systems: Human Experiments and Computational Implementations

With the advancement of Artificial Intelligence, it seems as if every aspect of our lives is impacted by AI in one way or the other. As AI is used for everything from driving vehicles to criminal justice, it becomes crucial that it overcome any biases that might hinder its fair application. We are constantly trying to make AI be more like humans. But most AI systems so far fail to address one of the main aspects of humanity: our culture and the differences between cultures. We cannot truly consider AI to have understood human reasoning without understanding culture. So it is important for cultural information to be embedded into AI systems in some way, as well as for the AI systems to understand the differences across these cultures. The main way I have chosen to do this are using two cultural markers: motifs and rituals. This is because they are both so inherently part of any culture. Motifs are things that are repeated often and are grounded in well-known stories, and tend to be very specific to individual cultures. Rituals are something that are part of every culture in some way, and while there are some that are constant across all cultures, some are very specific to individual ones. This makes them great to compare and to contrast. The first two parts of this dissertation talk about a couple of cognitive psychology studies I conducted. The first is to see how people understood motifs. Is is true that in-culture people identify motifs better than out-culture people? We see that my study shows this to indeed be the case. The second study attempts to test if motifs are recognizable in texts, regardless of whether or not people might understand their meaning. Our results confirm our hypothesis that motifs are recognizable. The third part of my work discusses the survey and data collection effort around rituals. I collected data about rituals from people from various national groups, and observed the differences in their responses. The main results from this was twofold: first, that cultural differences across groups are quantifiable, and that they are prevalent and observable with proper effort; and second, to collect and curate a substantial culturally sensitive dataset that can have a wide variety of use across various AI systems. The fourth part of the dissertation focuses on a system I built, called the motif association miner, which provides information about motifs present in input text, like associations, sources of motifs, connotations, etc. This information will be highly useful as this will enable future systems to use my output as input for their systems, and have a better understanding of motifs, especially as this shows an approach of bringing out meaning of motifs specific to certain culture to wider usage. As the final contribution, this thesis details my efforts to use the curated ritual data to improve existing Question Answering system, and show that this method helps systems perform better in situations which vary by culture. This data and approach, which will be made publicly available, will enable others in the field to take advantage of the information contained within to try and combat some bias in their systems

Negotiating with a logical-linguistic protocol in a dialogical framework

This book is the result of years of reflection. Some time ago, while working in commodities, the author felt how difficult it was to decide the order in which to use arguments during a negotiation process. What would happen if we translated the arguments into cards and played them according to the rules of the Bridge game? The results were impressive. There was potential for improvement in the negotiation process. The investigation went deeper, exploring players, cards, deals and the information concealed in the players´ announcements, in the cards and in the deals. This new angle brought the research to NeuroLinguistic Patterns and cryptic languages, such as Russian Cards. In the following pages, the author shares her discovery of a new application for Logical Dialogues: Negotiations, tackled from basic linguistic structures placed under a dialogue form as a cognitive system which ‘understands’ natural language, with the aim to solve conflicts and even to serve peace

Linguistic Indicators of Severity and Progress in Online Text-based Therapy for Depression

Mental illnesses such as depression andanxiety are highly prevalent, and therapyis increasingly being offered online. Thisnew setting is a departure from face-to-face therapy, and offers both a challengeand an opportunity – it is not yet knownwhat features or approaches are likely tolead to successful outcomes in such a dif-ferent medium, but online text-based ther-apy provides large amounts of data for lin-guistic analysis. We present an initial in-vestigation into the application of compu-tational linguistic techniques, such as topicand sentiment modelling, to online ther-apy for depression and anxiety. We findthat important measures such as symptomseverity can be predicted with compara-ble accuracy to face-to-face data, usinggeneral features such as discussion topicand sentiment; however, measures of pa-tient progress are captured only by finer-grained lexical features, suggesting thataspects of style or dialogue structure mayalso be important

Use of complementary and alternative medicine in Norway: a cross-sectional survey with a modified Norwegian version of the international questionnaire to measure use of complementary and alternative medicine (I-CAM-QN)

Background - In recent decades complementary and alternative medicine (CAM) has been widely used worldwide as well as in Norway, where CAM is offered mainly outside the national health care service, mostly complementary to conventional treatment and fully paid for by the patients. With few exceptions, previous research has reported on frequency and associations of total CAM use in Norway rather than on single therapies and products. Therefore, in this present study we will map the use of CAM more precisely, including types of services, products, and self-help practices and further include reasons for use and helpfulness of the specific therapies used based on a modified Norwegian version of the I-CAM-Q (I-CAM-QN). Method - Computer assisted telephone interviews using I-CAM-QN were conducted with 2001 randomly selected Norwegians aged 16 and above using multistage sampling in January 2019 with age and sex quotas for each area. Weights based on sex, age, education, and region corrected for selection biases, so that results are broadly representative of the Norwegian population. Descriptive statistics were carried out using Pearson’s Chi-square tests and t-tests to identify group differences. Result - CAM use was reported by 62.2% of the participants during the prior12 months. Most participants had used natural remedies (47.4%), followed by self-help practices (29.1%) and therapies received from CAM providers (14.7%). Few of the participants had received CAM therapies from physicians (1.2%). Women were generally more likely to use CAM than men, younger people more likely than older, and participants with lower university education and income more likely than participants without university education, with higher university education and higher income. Mean number of visits per year to the different CAM providers ranged from 3.57 times to herbalists to 6.77 times to healers. Most of the participants found their use of CAM helpful. Conclusion - This study confirms that CAM is used by a considerable segment of the Norwegian population. We suspect that the number of participants reporting CAM use is greater when specific therapies are listed in the questionnaire as a reminder (as in the I-CAM-QN) compared to more general questions about CAM use. The CAM modalities used are mainly received from CAM providers operating outside public health care or administered by the participants themselves

GD-COMET: A Geo-Diverse Commonsense Inference Model

With the increasing integration of AI into everyday life, it's becoming crucial to design AI systems that serve users from diverse backgrounds by making them culturally aware. In this paper, we present GD-COMET, a geo-diverse version of the COMET commonsense inference model. GD-COMET goes beyond Western commonsense knowledge and is capable of generating inferences pertaining to a broad range of cultures. We demonstrate the effectiveness of GD-COMET through a comprehensive human evaluation across 5 diverse cultures, as well as extrinsic evaluation on a geo-diverse task. The evaluation shows that GD-COMET captures and generates culturally nuanced commonsense knowledge, demonstrating its potential to benefit NLP applications across the board and contribute to making NLP more inclusive.Comment: Accepted to EMNLP 2023 Main Conferenc

Animal genetics strategy and vision for Tanzania

Bill & Melinda Gates Foundatio

Hypnosis and Hypnotherapy: The Role of Traditional Versus Alternative Approach

Hypnosis is a state of mind that is characterized by focused attention and heightened receptivity for suggestions. It is either established by compliance with instructions or achieved naturally; the critical nature of the mind is bypassed during hypnosis and acceptable suggestions are delivered. Misperceptions about hypnosis by clinical practitioners and their clients have been shaped through years of inaccurate but interesting portrayals of hypnosis in books, plays, and movies. Part of the misperceptions is that individuals with seemingly magical powers to manipulate the unsuspecting innocent with their authoritative voice commands and penetrating eyes are depicted as hypnotists. This chapter will review the traditional and conventional approaches used in hypnosis, their advantages and disadvantages as well as where hypnosis is used as a complementary or alternative therapy to the modern day orthodox medicine. Despite the pejorative image display of hypnosis and misconceptions surrounding it, hypnosis still has numerous applications in contemporary medicine. Hypnotherapy conducted by a trained therapist is considered as a complementary or safe alternative to present day orthodox medication for numerous ailments

Are black friday deals worth it? Mining twitter users' sentiment and behavior response

The Black Friday event has become a global opportunity for marketing and companies’ strategies aimed at increasing sales. The present study aims to understand consumer behavior through the analysis of user-generated content (UGC) on social media with respect to the Black Friday 2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday. In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings towards the identified topics and offers published by the companies on Twitter. Thirdly and finally, a data-text mining process called textual analysis (TA) was performed to identify insights that could help companies to improve their promotion and marketing strategies as well as to better understand the customer behavior on social media. The results show that consumers had positive perceptions of such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA), insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on these results, we offer guidelines to practitioners to improve their social media communication. Our results also have theoretical implications that can promote further research in this area