Research Projects

Trust in Digital Interactions

Challenge

Advancing digitalization in more and more areas of society and the economy leads to more personal interactions being replaced with digital interactions and to the increased relevance of trust in digital interactions. This also affects the interaction between customers and companies.

Approach

This project aims at exploring

– How can new technologies improve human-machine interactions? And how can they not?
– When can personalization improve human-machine interactions? And when can it not?
– When are digital interactions preferable to personal interactions? And when are they not?
– What does promote trust in human-machine interactions? And what does not?

The project addresses these questions in the context of self-service technology. Varying digital interventions are tested in field experiments with an assistance servicebot

Expected Results

A better understanding of how trust is built in human-machine interactions and how self-service technologies can be improved

Project Status

Ongoing

Keywords

self-service Marketing, trust, trustworthiness, interventions, chatbot, personalization, machine learning

Researchers

Dr. Andrea Ferrario, Joseph Ollier         

Digital Stress Interventions

Challenge

The increasing digitalization of our daily lives is creating working environments that are both more dynamic and more challenging to navigate. This places new demands on office workers as they struggle to adapt and to redefine the boundaries between work and leisure. Work-related stress (i.e. the harmful physical and psychological responses that occur when the personal and situational resources of employees are insufficient to allow them to cope with the day-to-day pressures of work) has become a prevalent problem in our society. According to the Swiss Job-Stress-Index 2018 (Gesundheitsförderung Schweiz), more than one in four Swiss workers experiences work-related stress and emotional exhaustion, which has detrimental effects on public health and the economy. However, new technologies also have the potential to alleviate these effects, facilitating the unobtrusive real-time detection of stress (e.g. through wearables) as well as the efficient delivery of targeted personalized interventions.

Approach

Due to the interdisciplinarity and complexity of the topic, this project is a collaboration of PhD students from the Mobiliar Lab and the Chair of Cognitive Sciences at ETH Zurich (D-GESS). We combine our expertise in computer science, mathematics and psychology to deliver high quality research on the automatic detection of stress and the evaluation of different stress interventions with respect to timing and (digital) delivery method. We test our hypotheses in controlled laboratory experiments and validate them in field experiments.

Expected Results

Insights into the automatic detection of stress in office environments, the key mechanisms and trigger points that underlie related negative behaviors and cognitions, and the design of effective digital interventions against them, including the examination of different digital delivery methods

Project Status

Ongoing

Keywords

work-related stress, stress detection, digital interventions, wearables, machine learning, eHealth

Researchers

Jasmine Kerr, Mara Nägelin, Raphael Weibel

Home Safety

Challenge

Simulation and prediction of crime is an important pillar of prevention. Due to the increasing digitalization of our daily lives, new data sources are becoming available. The project Home Safety investigates how new data sources and future-oriented methods of analysis can improve the simulation and prediction of crime and, as a result, public safety.

Approach

This project aims at providing support in estimating and predicting crime through analysis of the past events and forecasting the potential risk level for the future. The forecasts and simulations are based on historical records. In addition, external data sources are integrated to increase the accuracy of the models.

Results

One part of the research focuses on the prediction of theft and robbery in urban areas. New data such as subway entries and exits, check-ins on social media networks and taxi rides are used to map human activity. Using machine learning, we can predict the areas in a city where crimes can be expected. A new model improves the prediction rate by up to 30% compared to previous models.
Another part of the research simulates the movements of potential criminals in cities. The model uses publicly available data to map the urban structure; it also uses data from a social media network to spatially map human activity in the city. The movements of potential offenders are simulated through agent based modelling. The developed model can in particular serve to test theories as well as a variety of prevention strategies.

Project Status

Ongoing

Keywords

data mining, risk estimation, prevention, crowdsourcing, insurance

Researchers

Cristina Kadar, Raquel Rosés Brüngger

Holographic Data Science Education

Challenge

Data science is redefining organizational decision-making in most areas of our lives; yet this transformation is fraught −especially in management − with noteworthy methodological complexity. Traditional data science educational offerings introduce inherent difficulties to students that impede their learning objectives, through
1. the low level of interaction during the learning experience, as well as delayed feedback (this stands in stark contrast to case-based learning),
2. the difficulty in finding an entry point to data science that is properly balanced with their educational background(s), and
3. limited capabilities to reduce complexity through mathematical constructs only.

Approach

This project offers a new format for data science education at ETH involving collaborative learning between student bodies with different backgrounds and holographic reality technology. The goal is to improve learning strategies in order to
1. better understand data science methods, including machine learning modeling and decision-making, and
2. analyze & evaluate results with an eye on the trade-off between model performance and interpretability
through a use case-based interactive team experience with 3D immersive technology, i.e. holographic reality.

The project is in collaboration with the Chair of Management Information Systems (MIS) at the ETHZ department of Management, Technology, and Economics (MTEC).

Expected Results

Improvement of students’
1. motivation and interest in learning data science due to the use of emerging technology like holographic reality,
2. understanding of data science project components through a modular and interactive journey in a physical 3D space augmented with holograms,
3. already existing skills in dealing with complexity through immersive and interactive data analytics routines and the promotion of new sets of skills (e.g. psychomotor) for an augmented learning experience, and
4. capabilities in identifying actionable insights after performing predictive modeling in order to maximize success in data science projects through 3D model interactions (critical thinking).

Project Status

Ongoing

Keywords

data science, higher education, holographic reality, collaborative learning

Researchers

Dr. Andrea Ferrario

Data Science for Actuaries

Challenge

The insurance industry is undergoing profound change: data and technology are transforming the insurance business, enabling to management of new sources of risks and the realization of unseen opportunities. New professional players are coming on the market: actuaries now have the opportunity to become active actors of the data and technology transformation or be at risk of slowly being pushed aside by data scientist newcomers.

Approach

Augment the actuarial skill set and capabilities through a series of data science tutorials containing both theory on machine learning modelling methodologies and end-to-end insurance use cases. These are supported by scripts in R, Python and H2O.

The project is part of the SAV* Datascience working group initiative.

*Schweizerische Aktuarvereinigung (The Swiss Association of Actuaries)

Expected Results

Scientific publications to further develop the actuarial skill set by including state-of-the-art machine learning methodologies applied in fully reproducible insurance use cases.

Project Status

Ongoing

Keywords

data Science, actuarial science, insurance

Researchers

Dr. Andrea Ferrario

Trust, Ethics and Algorithms

Challenge

A wide debate about the ethics of what is commonly denoted as ‘artificial intelligence’ has emerged; it captures the tensions, doubts and fears that are manifested in a greater portion of modern societies when confronted with the conception and use of artificial intelligence-based services and products, in different domains of everyday life.

The plethora of available ethical guidelines from both institutions and companies attemps to be an answer to the urgent questions arising from such interdisciplinary debate. However, the lack of consistency around definitions of artificial intelligence, algorithms and modeling, trust as well as trustworthiness, strongly impacts the quality of scientific inferences and hinders the possibility of consistently and efficiently tackling the aforementioned challenges.

Approach

Focus on the fundamentals, i.e.
– trust and trustworthiness: definitions, analysis of accounts and scenarios (human-to-human, e-trust etc.)
– ontology of artificial intelligence and statistical learning algorithms
– the role of transparency and interpretability on artificial intelligence: explanations as communication, the use of counterfactuals in machine learning research
– review of ethical guidelines in relation to artificial intelligence: limitations of top-down approaches (e.g. from a selection fundamental human rights to ethical values and requirements for ethical artificial intelligence), the role of bottom-up contextualized approaches around data-pipelines of data-driven or artificial intelligence-empowered services and products.

The project is run in collaboration with the Institute of Biomedical Ethics and History of Medicine (IBME) at the University of Zurich and the Collegium Helveticum.

Expected Results

Improved understanding of the properties of trust and trustworthiness dynamics as well as explainability and transparency in the presence of selected artificial intelligences in a contextualized setting. Contributions to the debate on the ethics of artificial intelligence by addressing the peculiarities of predictive modeling and algorithms from a philosophical perspective, with the creation of context-driven guidelines.

Project Status

Ongoing

Keywords

ethics, explainable artificial intelligence, trust, algorithms, philosophy of technology 

Researchers

Dr. Andrea Ferrario

Contextualized Digital Reminiscences  

Challenge

Semantic analysis of multi-scale health data represents a powerful scientific tool to study healthy aging.
The key challenge is to harness the scientific value of multi-scale health data (from both individuals and populations) for a contextualized understanding of health dynamics, including interventions. The latter need to consider the 4 pillars of the healthy aging paradigm, i.e.
– functional abilities
– intrinsic capacities
– environments
– decision-making processes of individuals

Approach

We focus on reminiscence, i.e. the recollection of significant life experiences, in a healthy aging paradigm by performing data analytics and machine learning (e.g. NLP) on unstructured data to automatically classify reminiscence events and types during everyday life. Automated classification models support the design of digital interventions aimed at the promotion or support of the healthy aging paradigm.

The project is run in collaboration with the Department of Psychology – Gerontopsychology at the University of Zurich and the Collegium Helveticum.

Expected Results

Improved understanding of contextualized reminiscing through data analytics and machine learning and design, deployment as well as testing of digital interventions. Specification of domain specific models of healthy aging, including the clarification of terminology to support interdisciplinary scientific collaboration.

Project Status

Ongoing

Keywords

healthy aging, semantic analysis, reminiscing, machine Learning

Researchers

Dr. Andrea Ferrario

SME Opportunities

Challenge

Small and medium enterprises (SMEs) play an important role in the economy of many countries. This is especially true for Switzerland where SMEs represent more than 99% of all businesses and about 68% of all jobs, acting as the country’s backbone for growth. With the emergence of data mining in the research field of SME revenue and growth, researchers have recently turned their focus to applying data-mining techniques to revenue and growth prediction. However, most of the state-of-the-art prediction models for revenue and growth only include one or few data types such as financial or operational data, which cannot explain the whole and complex context of the modern business environment.

Approach

To better understand SMEs’ business performance, we enhance existing business prediction models by leveraging the potential of data mining and web mining for SME growth prediction. Web mining methods are explored with the goal to automatically extract valuable information hidden in the web, whereas data mining methods are applied in order to develop SME growth prediction models with high performance and applicability.

Results

The features derived from the data sources differ significantly in their relevance for the prediction models. The existence of a patent, business sector and age of the entrepreneur were particulary important for the models. The occurrence of SME revenue growth can be predicted using information from the different data sources with approximately 70% of accuracy.
Large amounts of web content are essential for predicting SME growth based on web mining. Only sectors with business areas which are sufficiently covered on the web, such as the hospitality sector, are suitable. Using web mining, the growth of restaurants’ revenues can be predicted with 68% accuracy.
As a secondary result, the project revealed that – on average – SMEs with one or two patents grew 10% faster than comparable SMEs without patents. This growth effect is even stronger at 13.4% for SMEs with inventor teams consisting of both men and women. With respect to the frequency of patent applications the differences between sectors are considerable. Manufacturers of precision devices and electrical elements applied for the most patents.

Project Status

Completed

Keywords

business intelligence, general loss insurance, web mining, business performance prediction

Researchers

Dr. Daniel Müller, Dr. Funk Te

Smart Consumers

Challenge

The rapid development and penetration of smart and ubiquitous technologies, such as mobile devices and Web 2.0 platforms, have significantly changed the behavior and interactions of consumers. Companies’ offers can be compared more easily. Moreover, consumers want to be able to reach call centers quickly and engage with companies via multiple channels. Yet, according to findings from the World Insurance Report 2013, only 30% of customers report having positive experiences with their insurers, thus clearly indicating that insurance companies should better understand their customers’ needs.

Approach and Results

This project analyzed the interaction between customers and insurers in terms of channels used for interaction in three studies.
The first study analyzed the buying behavior of insurance customers in a multichannel distribution system. The analysis compared customers who bought their insurance policy directly online on the website (Group 1) with customers who first obtained information on the price and product on the website, but then bought their insurance policy through the agency (so-called RoPo customers, Group 2), and customers that were solely in contact with the agency during the purchasing process (Group 3). The study suggests that customers in Group 1 and 2 are younger, more likely to live in urban environments and more likely to be new clients. Moreover, customers of Group 2 had the highest average insurance premium. The average premium volume of these customers was over 5% higher than for those in Group 1 for two out of three insurance products examined.
The second study focused in particular on the buying behavior of RoPo customers (research online, purchase offline). The aim was to understand how much time these customers need to make the decision to purchase. It was shown that RoPo customers took an average of 19 working days from researching the product online to buying it at the agency. This timespan varies depending on the type of insurance product. It is lowest for travel insurance with 15 working days, while customers need an average of 20 working days to decide on household/personal liability and motor insurance policies.
The third study aimed to predict cancellations and renewals of existing policies as well as purchases of additional products (cross-buying). It was shown that it is possible to make an accurate prediction of the future buying behavior of RoPo customers based on their linked internet data. The error rate of the models to predict cancellations and purchases of additional products was as low as 20% to 25%.

Project Status

Completed

Keywords

data mining, channel performance, web analytics, insurance, smart consumers

Researchers

Dr. Stefan Mau