Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context

This paper provides an empirical and conceptual account on seeing machine learning models as part of a sociotechnical system to identify relevant vulnerabilities emerging in the context of use. As ML is increasingly adopted in socially sensitive and safety-critical domains, many ML applications end up not delivering on their promises, and...

Data-efficient machine learning methods in the ME-TIME study: Rationale and design of a longitudinal study to detect atrial fibrillation and heart failure from wearables

Background: Smartwatches enable continuous and noninvasive time series monitoring of cardiovascular biomarkers like heart rate (from photoplethysmograms), step counter, skin temperature, et cetera; as such, they have promise in assisting in early detection and prevention of cardiovascular disease. Although these biomarkers may not be directly...

Causal scientific explanations from machine learning

Machine learning is used more and more in scientific contexts, from the recent breakthroughs with AlphaFold2 in protein fold prediction to the use of ML in parametrization for large climate/astronomy models. Yet it is unclear whether we can obtain scientific explanations from such models. I argue that when machine learning is used to conduct...

Less Machine (=) More Vision: Approaches towards Practical and Efficient Machine Vision with Applications in Face Analysis

Machines that interact with humans can do so better if they can also visually understand us, but they have limited resources to do so. The main topic of this dissertation is contrasting the use of resources by machine vision systems against the accuracy obtained by them. This thesis focuses on reducing the need for data, memory, and computation...

Development and validation of an early warning model for hospitalized COVID-19 patients: a multi-center retrospective cohort study

Background: Timely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used Early warning scores (EWSs) underestimate illness severity in COVID-19 patients and therefore, we developed an early warning model...

Interpretable neural network with limited weights for constructing simple and explainable HI using SHM data

Recently, companies all over the world have been focusing on the improvement of autonomous health management systems in order to enhance performance and reduce downtime costs. To achieve this, the remaining useful life predictions have been given remarkable attention. These predictions depend on the proper designing process and the quality of...

Contestable AI by Design: Towards a Framework

As the use of AI systems continues to increase, so do concerns over their lack of fairness, legitimacy and accountability. Such harmful automated decision-making can be guarded against by ensuring AI systems are contestable by design: responsive to human intervention throughout the system lifecycle. Contestable AI by design is a small but...

Optimising High-Rise Buildings for Self-Sufficiency in Energy Consumption and Food Production Using Artificial Intelligence: Case of Europoint Complex in Rotterdam

The increase in global population, which negatively affects energy consumption, CO2 emissions, and arable land, necessitates designing sustainable habitation alternatives. Self-sufficient high-rise buildings, which integrate (electricity) generation and efficient usage of resources with dense habitation, can be a sustainable solution for future...

Explainable artificial intelligence in forensics: Realistic explanations for number of contributor predictions of DNA profiles

Machine learning obtains good accuracy in determining the number of contributors (NOC) in short tandem repeat (STR) mixture DNA profiles. However, the models used so far are not understandable to users as they only output a prediction without any reasoning for that conclusion. Therefore, we leverage techniques from the field of explainable...

Evaluating XAI: A comparison of rule-based and example-based explanations

Current developments in Artificial Intelligence (AI) led to a resurgence of Explainable AI (XAI). New methods are being researched to obtain information from AI systems in order to generate explanations for their output. However, there is an overall lack of valid and reliable evaluations of the effects on users' experience of, and behavior in...

AI supported noise analyses for structure design requirements definition

The artificial intelligence (AI) field has encountered a turning point mainly due to advancements in machine learning, which allows systems to learn, improve, and perform a specific task through data without being explicitly programmed. Machine learning can be utilized with machining processes to improve product quality levels and...

Data-driven modelling of energy demand response behaviour based on a large-scale residential trial

Recent years have seen an increasing interest in Demand Response (DR), as a means to satisfy the growing flexibility needs of modern power grids. This increased flexibility is required due to the growing proportion of intermittent renewable energy generation into the energy mix, and increasing complexity in demand profiles from the...

Short and long-term innovations on dietary behavior assessment and coaching: Present efforts and vision of the pride and prejudice consortium

Overweight, obesity and cardiometabolic diseases are major global health concerns. Lifestyle factors, including diet, have been acknowledged to play a key role in the solution of these health risks. However, as shown by numerous studies, and in clinical practice, it is extremely challenging to quantify dietary behaviors as well as influencing...

Technology and More-Than-Human Design

Are we reaching the limits of what human-centered and user-centered design can cope with? Developing new design methodologies and tools to unlock the potentials of data technologies such as the Internet of Things, Machine Learning and Artificial Intelligence for the everyday job of design is necessary but not sufficient. There is now a need to...

Interpretable confidence measures for decision support systems

Decision support systems (DSS) have improved significantly but are more complex due to recent advances in Artificial Intelligence. Current XAI methods generate explanations on model behaviour to facilitate a user's understanding, which incites trust in the DSS. However, little focus has been on the development of methods that establish and...

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation...

On domain-adaptive machine learning

Artificial intelligence, and in particular machine learning, is concerned with teaching computer systems to perform tasks. Tasks such as autonomous driving, recognizing tumors in medical images, or detecting suspicious packages in airports. Such systems learn by observing examples, i.e. data, and forming a mathematical description of what types...

Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation

Growing interest in eXplainable Artificial Intelligence (XAI) aims to make AI and machine learning more understandable to human users. However, most existing work focuses on new algorithms, and not on usability, practical interpretability and efficacy on real users. In this vision paper, we propose a new research area of eXplainable AI for...