Society-in-the-Loop: Programming the Algorithmic Social Contract

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, `SITL = HITL + Social Contract.'Comment: (in press), Ethics of Information Technology, 201

Interaction monitoring model of logo counseling website for college students’ healthy self-esteem

The purpose of this research is to develop the client-counselor interaction monitoring model of the logo counseling website. The model attempts to help counselors in guiding and helping the students (clients) to achieve healthy self-esteem. Machine learning techniques integrated into the model will ensure that the recommendations can be available for counselors and supervisors in the near real-time environment. For the first implementation, a chatbot application is developed and tested with excellent responses from the students. Further research is needed to implement the complete specifications of the interaction monitoring model on the logo counseling website

Content-based recommender support system for counselors in a suicide prevention chat helpline: Design and evaluation study

Background: The working environment of a suicide prevention helpline requires high emotional and cognitive awareness from chat counselors. A shared opinion among counselors is that as a chat conversation becomes more difficult, it takes more effort and a longer amount of time to compose a response, which, in turn, can lead to writer's block. Objective: This study evaluates and then designs supportive technology to determine if a support system that provides inspiration can help counselors resolve writer's block when they encounter difficult situations in chats with help-seekers. Methods: A content-based recommender system with sentence embedding was used to search a chat corpus for similar chat situations. The system showed a counselor the most similar parts of former chat conversations so that the counselor would be able to use approaches previously taken by their colleagues as inspiration. In a within-subject experiment, counselors' chat replies when confronted with a difficult situation were analyzed to determine if experts could see a noticeable difference in chat replies that were obtained in 3 conditions: (1) with the help of the support system, (2) with written advice from a senior counselor, or (3) when receiving no help. In addition, the system's utility and usability were measured, and the validity of the algorithm was examined. Results: A total of 24 counselors used a prototype of the support system; the results showed that, by reading chat replies, experts were able to significantly predict if counselors had received help from the support system or from a senior counselor (P=.004). Counselors scored the information they received from a senior counselor (M=1.46, SD 1.91) as significantly more helpful than the information received from the support system or when no help was given at all (M=-0.21, SD 2.26). Finally, compared with randomly selected former chat conversations, counselors rated the ones identified by the content-based recommendation system as significantly more similar to their current chats (β=.30, P<.001). Conclusions: Support given to counselors influenced how they responded in difficult conversations. However, the higher utility scores given for the advice from senior counselors seem to indicate that specific actionable instructions are preferred. We expect that these findings will be beneficial for developing a system that can use similar chat situations to generate advice in a descriptive style, hence helping counselors through writer's block

Detecting changes in help seeker conversations on a suicide prevention helpline during the COVID− 19 pandemic: in-depth analysis using encoder representations from transformers

Background: Preventatives measures to combat the spread of COVID− 19 have introduced social isolation, loneliness and financial stress. This study aims to identify whether the COVID-19 pandemic is related to changes in suicide-related problems for help seekers on a suicide prevention helpline. Methods: A retrospective cohort study was conducted using chat data from a suicide prevention helpline in the Netherlands. The natural language processing method BERTopic was used to detect common topics in messages from December 1, 2019 until June 1, 2020 (N = 8589). Relative topic occurrence was compared before and during the lock down starting on March 23, 2020. The observed changes in topic usage were likewise analyzed for male and female, younger and older help seekers and help seekers living alone. Results: The topic of the COVID-19 pandemic saw an 808% increase in relative occurrence after the lockdown. Furthermore, the results show that help seeker increased mention of thanking the counsellor (+ 15%), and male and young help seekers were grateful for the conversation (+ 45% and + 32% respectively). Coping methods such as watching TV (− 21%) or listening to music (− 15%) saw a decreased mention. Plans for suicide (− 9%) and plans for suicide at a specific location (− 15%) also saw a decreased mention. However, plans for suicide were mentioned more frequently by help seekers over 30 years old (+ 11%) or who live alone and (+ 52%). Furthermore, male help seekers talked about contact with emergency care (+ 43%) and panic and anxiety (+ 24%) more often. Negative emotions (+ 22%) and lack of self-confidence (+ 15%) were mentioned more often by help seekers under 30, and help seekers over 30 saw an increased mention of substance abuse (+ 9%). Conclusion: While mentions of distraction, social interaction and plans for suicide decreased, expressions of gratefulness for the helpline increased, highlighting the importance of contact to help seekers during the lockdown. Help seekers under 30, male or who live alone, showed changes that negatively related to suicidality and should be monitored closely

Mixed-Initiative Real-Time Topic Modeling & Visualization for Crisis Counseling

Text-based counseling and support systems have seen an increasing proliferation in the past decade. We present Fathom, a natural language interface to help crisis counselors on Crisis Text Line, a new 911-like crisis hotline that takes calls via text messaging rather than voice. Text messaging opens up the opportunity for software to read the messages as well as people, and to provide assistance for human counselors who give clients emotional and practical support. Crisis counseling is a tough job that requires dealing with emotionally stressed people in possibly life-critical situations, under time constraints. Fathom is a system that provides topic modeling of calls and graphical visualization of topic distributions, updated in real time. We develop a mixed-initiative paradigm to train coherent topic and word distributions and use them to power real-time visualizations aimed at reducing counselor cognitive overload. We believe Fathom to be the first real-time computational framework to assist in crisis counseling.Massachusetts Institute of Technology. Media Laboratory (Reid Hoffman Fellowship)Google (Firm) (Faculty Grant

The Use of Smartphone-Based Interventions to Improve Mental Health

Mental disorders are highly prevalent and cause a high burden of disease. Even though effective treatment approaches exist, there is a large treatment gap and a substantial proportion of non-responders to traditional face-to-face psychotherapy. New treatment approaches are required. The Grand Challenges in Global Mental Health initiative called for the development of mobile and IT technologies to increase access to evidence-based care. Internet-based psychotherapeutic interventions have been found efficacious. Smartphones are widespread, ensure high availability of their users, and are equipped with technologically rich sensors. In the context of electronic Health (eHealth) and mobile Health (mHealth), the potential of smartphone-based interventions has been recognized. Due to the relative novelty and interdisciplinarity of the field, several open research questions remain. This dissertation focuses on three selected research questions on how smartphone-based interventions can be used to improve mental health. The first publication provides evidence of the applicability of smartphone-based psychotherapeutic micro-interventions evoking mood changes in a real-world setting in a non-clinical sample (N = 27; n obs. = 335 micro-intervention sessions) across 13 days; data was collected in a larger randomized trial. Based on data from the same study, in the second publication, evidence is provided for the utility of a machine learning-based random forest (RF) algorithm for the prediction of smartphone-based psychotherapeutic micro-intervention success regarding mood amelioration, based on contextual information. In publication 3, based on data from healthy participants in a randomized controlled trial (N = 132), we explored whether efficacy expectancies could be successfully induced in a smartphone-based placebo mental health intervention lasting 20 days, in the context of digital placebo effects. These findings may pave the way for future endeavors to provide personalized digital mental health interventions and to further promote the promising field of eHealth and mHealth, in line with the precision medicine approach

A Systematic Review of Social Presence: Definition, Antecedents, and Implications

Social presence, or the feeling of being there with a “real” person, is a crucial component of interactions that take place in virtual reality. This paper reviews the concept, antecedents, and implications of social presence, with a focus on the literature regarding the predictors of social presence. The article begins by exploring the concept of social presence, distinguishing it from two other dimensions of presence—telepresence and self-presence. After establishing the definition of social presence, the article offers a systematic review of 233 separate findings identified from 152 studies that investigate the factors (i.e., immersive qualities, contextual differences, and individual psychological traits) that predict social presence. Finally, the paper discusses the implications of heightened social presence and when it does and does not enhance one's experience in a virtual environment