Social Computing

Alpine lakes (those located above the 2000 m tree line) are excellent sentinels of climate change as their chemistry and biology respond rapidly to environmental forcing. The Swiss alps are host to over 1500 alpine lakes, many of which have been newly mapped and thus never been studied2. Microorganisms play major ecological roles in these ecosystems, including primary production, cycling of elements, and attenuation of contaminants, but it is uncertain how physical climatic changes may affect microbial communities and their activities in alpine lakes. This project aims to: (i) record and monitor the unexplored microbial diversity in Swiss alpine lakes, and (ii) engage citizens in science and spread awareness about environmental conservation through participation in our field campaigns. In summary, 2000LAKES is a project of alpine citizen science aiming to understand the ecological impacts of climate change in alpine lakes and to promote the conservation of alpine microbial ecosystems joining forces between scientists and citizens.

The goal of the here submitted proposal is to finance a first year of research as a concrete first step towards the creation of the Center for Leadership and New Technologies (Unil, Idiap/EPFL, IMD). The Center that we aim to create long term will include AI and virtual reality among other technologies in relation to leadership. The Center will develop tools for assessing and developing leadership, conduct research with respect to new technologies related to leadership, as well as showcase our developments and empirical results for the corporate world (e.g., writing white papers, organizing symposia and conferences). Ideally, firms would turn to the Center for advice, training, and thought leadership on the topic of new technologies and leadership. IMD will be crucial in creating the link with companies and will be able to use the new technologies for their teaching and training. A first concrete project for which we ask for seed funding from the Trans4 consortium concerns the development of a collection of software modules that will be able to automatically detect leadership skills from videotaped speeches using voice and body language information. The algorithms developed will be able to automatically detect perceived leadership based on voice and video samples. We will train an algorithm to infer leadership (e.g., trustworthiness, competence as a strategic leader, competence as a transformational leader etc.) automatically based on vocal cues and body language automatically detected by the machine. We will train the algorithms with ground truth data that we will collect from a panel of evaluators (e.g., MTurk workers) on either selfpresentation videos (e.g., video CVs on YouTube) or on public speaking videos (e.g., TED Talks). Given that the quality of the algorithm depends on the quality of the training data (i.e., ground truth), we will put extra care and effort in producing this training data. The so developed software modules can then be used for leadership skill assessment and for leadership skill training and development. It can be seen as a stand‐alone outcome but at the same time it can be incorporated to the Charismometer algorithm that John and Philip have already developed and it can be added to work Daniel and Marianne have been doing in the past (on automatic extraction of nonverbal behavior from video). Basing the new development on existing work ensures that we do not start from scratch and that we can achieve the goal within one year of funding. The seed money project is thus at the same time a continuation of existing work and an important extension of it.

This project investigates social interaction in personnel selection interviews enhanced by digital technology. We will create a database of applicants participating in video interviews (applicants receive a list of interview questions from a recruiter online and then record themselves answering those questions), which are a newly emerging interview format. We will develop automated procedures for extracting relevant behavioral features from streams of applicants’ verbal and nonverbal behavior in these interviews. This information will be (1) linked to external criteria (e.g., hireability ratings by expert recruiters), (2) used to train machine learning algorithms, and (3) fedback to the applicants. We will assess applicants’ perceptions of this feedback, whether and how they use it to improve their performance in a second video interview a day later, and how they perceive data privacy issues related to the use of their data. The project addresses three issues mentioned in the call. First, how is digitalization transforming social ties? The selection interview is the gateway to employment and thus the potential beginning of one of the fundamental social ties in modernity: the work relationship. We explore a new format by which selection interviews are conducted in an online, asynchronous manner. Second, how is digitalization transforming the economy? The selection interview is an important personnel selection procedure, which itself is an important component of strategic talent management. The digitalization of talent management is rapidly expanding in practice, but is currently poorly understood in research. Third, how is digitalization transforming our subjective experience? Video interviews are a novel experience for many applicants. Machine learning techniques can be used to extract the applicants’ behaviors recorded on the videos and to some degree infer their personality and social skills. This information can then be fed back to the applicants, potentially changing their subjective experience of the video interview. However, questions like how such feedback is best provided and how the applicant apprehends and uses it are largely unexplored. The study will yield four main sets of outputs. First, the primary data from the study will lead to publications in scientific journals or conference proceedings in human-computer interaction and organizational psychology or human resources. Second, the data will be used to adapt an existing data collection platform and to improve the quality of algorithms to infer verbal and nonverbal behavior from videos. Third, data about user experiences will inform the development of evidence-based coaching programs for improving applicants’ performance. Fourth, the rich set of data and experience generated will constitute fruitful avenues for further research by the applicant team.

The project provides an augmented dialogue tool that exploits verbal and non-verbal indices to improve interviews’ quality. Its main business application is to support HR interviews.