The challenges faced by society today demand ways of thinking and designing that go beyond the individual. This dissertation is concerned with sustainable transitions, and focuses on everyday practices, technologies within those practices, and the role of design.

Empirically, the research focuses on the transition from gas boilers to heat pumps in Dutch homes. Implementing heat pumps for residential buildings on a large scale should reduce CO2 emissions and save energy while providing comfortable indoor climate of homes. However, in everyday life in households, heat pumps are often not used as the technology developers intended. Ethnography, design research and interviews with value chain professionals are used to gather data and better understand how everyday practices with technologies are performed and understood.

Based on this analysis, I question dominant human-centered design approaches, which prioritize individual users and align with current practices, arguing that they fall short in supporting societal transitions. In my work I take steps towards a more-than-individual-human-centered approach, which embraces the improvisational nature of everyday life and the co-performance of humans and technologies, with the goal of benefitting design work within sustainable transitions. ... Read more

This thesis addresses the semantic gap in visual understanding, improving visual models with semantic reasoning capabilities so they can handle tasks like image captioning, question-answering, and scene understanding. The main focus is on integrating visual and textual data, leveraging human cognitive insights, and developing a robust multi-modal foundation model. The research starts with the exploration of multi-modal data integration to enhance semantic and contextual reasoning in fine-grained scene recognition. The proposed multi-modal models, which combine visual and textual inputs, outperform traditional models that rely solely on visuals. This is particularly true in complex urban environments where visual ambiguities often occur. This method emphasizes the significance of semantic enrichment through multi-modal integration, which helps resolve visual ambiguities and improve scene understanding. ... Read more

Robots are increasingly navigating our living environments and must navigate socially to be accepted. While existing socially aware navigation (SAN) approaches enable robots to interpret and communicate social information to navigate efficiently, safely, and in a socially acceptable manner, they often overlook potential conflicts and errors in real-world human-robot interactions. This thesis contributes to SAN by investigating how robots can adapt their inappropriate navigation behavior based on human feedback (perceived appropriateness), leading to smoother and less error-prone human-robot interactions.... ... Read more

Contestability, i.e., a property that makes AI systems open to human intervention throughout their lifecycles, has been claimed to be essential for counteracting algorithmic harms. By enabling decision subjects to influence algorithmic outputs, contestable AI systems aim to safeguard decision subjects' rights to autonomy and dignity. Despite the interest and relevance of contestability in HCI, little is known about whether and how elements of contestable AI systems can empower decision subjects in algorithmic decision-making. In this dissertation, we aim to generate empirical insights into decision subjects' needs for and fairness perceptions towards contestable AI systems in decision-making. By focusing on decision subjects, this dissertation leads to a set of recommendations for organizations setting up algorithmic decision-making processes. These recommendations encourage organizations to account for the interests of those impacted by algorithmic decisions from the early stages of the design process. ... Read more

Modern factories showlittle resemblance to the assembly lines froma century ago. Nowadays, a single human might be responsible for setting up, operating, and maintaining a complex chain of machines. This shift has made the work of factory operators more cognitively demanding, requiring constant monitoring and problem-solving. Key to tackling these challenges is the effective collaboration of human operators in exchanging ideas and knowledge, from high-level problem-solving strategies to solutions for emerging issues. Yet, the heightened productivity requirements and small shifts mean fewer opportunities to share this knowledge face-to-face and reporting practices are deprioritized. Thus, the manufacturing industry faces a knowledge management crisis.
This dissertation investigates the integration of conversational AI assistants into manufacturing settings to facilitate knowledge sharing among factory operators. Capitalizing on recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), this research investigates the designing and evaluation of conversational AI tools that efficiently capture and share human knowledge on the factory floor while addressing operator needs and concerns. The introduction of conversational AI assistants for knowledge sharing—referred to as cognitive assistants (CA) in this work—in factory environments promises significant benefits but comes with numerous challenges.... ... Read more

Motivation: In today’s increasingly digital world, making sense of personal data has become a valuable approach for individuals to understand their behavior and reflect on lived experiences. Across fields such as personal informatics, data visualization, and design, supporting reflection through sensemaking of personal data is increasingly recognized as a critical for promoting behavior change and cultivating deeper self-understanding. Within the literature on collaborative sensemaking, pair collaboration has emerged as a particularly promising strategy for supporting reflective practices. It offers unique advantages— such as the integration of diverse perspectives, focused dyadic comparison, and rich interaction dynamics—that are especially effective for surfacing tacit knowledge and generating deeper insights. Despite this promise, most existing research in personal informatics and visualization has focused on individual engagement with personal data, with limited exploration of collaborative or group-level interactions. In particular, the distinctive characteristics of pair collaboration remain underexplored in the context of personal data. This reveals a significant gap in understanding how to effectively support pair sensemaking of personal data in order to foster reflection.
Objective: To address this gap, this dissertation introduces and develops the pair sensemaking of personal data approach—a novel method for fostering reflection through pair engagement with personal data. This approach emphasizes the integration of both one’s own and others’ data, dyadic comparison within subjective data representations, and reciprocal, reflective dialogues between pairs to co-construct self-knowledge and deepen personal reflection. To realize this objective, the thesis adopts a progressive, mixed-methods research strategy, unfolding across three empirical studies. Each study incrementally explores and refines how pair sensemaking of personal data can be effectively designed to enhance reflective engagement and foster meaningful insights into lived experiences. ... Read more

Conversational agents (CAs) are increasingly being adopted across various domains, with enterprises particularly interested in leveraging these technologies to explore new opportunities. CAs, such as Copilot, are transforming human-computer collaboration in the workplace by serving as effective work assistants. Additionally, their advanced natural language understanding capabilities position them as powerful tools for facilitating crowdsourcing within enterprises.
This thesis investigates the dual role of CAs in enterprises: as work assistants for employees and as facilitators of crowdsourcing. By acting as work assistants, CAs interact with a broad range of employees, making them well-suited to gather valuable insights that contribute to the company’s knowledge base and support collaborative problem-solving. The central research question explores how to effectively engage and motivate employees in this crowdsourcing process through CA personality design, which is relatively unexplored. Drawing on the "Computers Are Social Actors" (CASA) framework by Nass et al. (1994), this thesis examines CA personality design, aiming to enhance human-CA interaction by eliciting social responses from employees and thereby increasing their engagement.
We approached these research questions from three perspectives: (1) designing and specifying CA personalities for effective implementation in enterprises, (2) identifying the most and least promising qualities of CA personality designs in this context, and (3) designing an empirical study within the enterprise to measure which personality archetype design is the most effective.
We reviewed literature on the context of conversational crowdsourcing in enterprise settings, and CA design methods with a focus on CA anthropomorphism. These findings informed the initial design phase, where CA personality archetypes were tested in an enterprise environment. Based on these insights, we developed design guidelines for enterprise CA personality design. After refining the archetypes, we proposed three unique CA personalities aimed at enhancing employee participation in enterprise crowdsourcing through their interactions with CAs. These archetypes were then implemented in the enterprise's internal chat platform for an experiment, where employee interactions with the CAs were collected and analysed both quantitatively and qualitatively.
The results of our experiment reveal how different CA personalities impact user engagement in enterprise crowdsourcing, demonstrating how distinct CA personalities can be effectively translated into LLM prompts and perceived by users. The study also highlights the relationship between CA personality and the triggering of users' social responses, as observed through sentiment analysis. Additionally, our research identifies the challenge of balancing social engagement with maintaining professionalism in enterprise CAs to build user trust.
This thesis concludes with a discussion of the findings and their implications for future research in enterprise CA design.
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This thesis aims to design the future roles of human operators in an autonomous bus system in Autonomous Airside Operations at Schiphol Airport. The research was conducted in collaboration with Delft University of Technology and Royal Schiphol Group, focusing specifically on the implementation of autonomous buses for passenger transportation between terminals and aircraft.

The study employs a human-centered design approach since the successful integration of technology relies not only on its technical capabilities but also on human operators, who will work in and interact with an autonomous bus system. By placing humans at the center of the design process, a valuable future human operator role is created, that contributes to the successful implementation of an autonomous bus system in AAO.

To emphathize with the current bus operations, context studies at Schiphol Airport are conducted with 3 key roles in the current bus operation. Additionally, 11 expert interviews were conducted to gather different perspectives on the future human operator role in AAO.

The context research revealed 4 current operator responsibilities besides driving-related tasks. Additionally, based on the expert interviews, 5 future human operator responsibilities in an Autonomous Airside Operations are identified. Combinations between current and future responsibilities resulted in the design of 4 potential future scenarios, each with a different future human operator role.

The study provides 4 human operator roles in potential future scenarios where human operators remain crucial in an autonomous bus system, which is: (1) the role of the safety driver to enable autonomous driving as an addition to manual operations; (2) the role of host/hostess or fleet coordinator to provide onboard assistance; (3) the role of the (de)boarding assistant to ensure a safe boarding and deboarding procedure; and (4) the role of the rescuer to immediately operate in case of an emergency. These scenarios are further developed into animation videos, which serve as speculative design artifacts to envision, reflect, and discuss potential future scenarios.

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Current literature that aims to describe the use of (Gen)AI in the domain of design is hindered by the lack of conceptual integration of design theories which in turn blurs the connection between the design process and human-AI interaction and collaboration frameworks. This thesis’ objective is to contribute to the conceptual understanding of LLM-based design process augmentation. This thesis shows how such LLM-based design process augmentation can look like by establishing 1) a time-based framework of a human – Augmentation-System collaborative design process, 2) a perspective on the design process augmentation capabilities of LLMs, and 3) an augmentation system architecture and interface for the practical implementation of these theoretical considerations. Other contributions that have enabled these three main contributions include 1) the unification of existing design theories (problem-solution co-evolution, situated Function-Behavior-Structure (FBS) framework, Concept-Knowledge theory, and Uncertainty Driven Action (UDA) model) to attend to a wider range of person-related characteristics required for the description of a collaborative process of design, 2) the connection between human intellect augmentation and creativity literature, resulting in 3) the application of creativity literature in the domain of GenAI and specifically LLMs, and 4) the application of the layers of behavior as described in the UDA (Uncertainty Driven Action) model for the creation of a computational augmentation system. Finally, a reflection on the feasibility and desirability of different forms and applications of augmentation systems is provided. The results of this thesis have created a fundament for further research into the direction of the initial objective, as the results have increased the conceptual power to describe and explain process, output, and other augmentation-related phenomena connected to the domain of human-LLM (or GenAI) co-design. ... Read more

Over the last two decades, the machine learning (ML) field has witnessed a dramatic expansion, propelled by burgeoning data volumes and the advancement of computational technologies. Deep learning (DL) in particular has demonstrated remarkable success across a wide range of domains, including healthcare, mobility, life sciences, and energy systems. This success has been further accelerated by the availability and efficiency of open-source ML frameworks like TensorFlow and PyTorch, making ML methodologies more accessible than ever.

However, this rapid growth has brought its own set of challenges. The proliferation of ML models and related artifacts, such as datasets, have brought abundant information during the ML lifecycle. The descriptive and property information of these artifacts is referred as metadata. Yet current practices, such as model cards used in public model zoos and tools to track metadata within scripts, cannot fully captured the metadata of these artifacts, let alone a standardized approach for their management, and access. In addition, the prevailing practice of managing ML/DL scripts via traditional software repositories, while adequate for software engineering, falls short in addressing the unique needs of ML workflows, such as model reuse and comparative analysis. These practices hinder the effective use of structured and comprehensive metadata representation. This disconnect points to a pressing need for improved methodologies and tools in the ML field.

In response to these challenges, this thesis delves into the development and exploitation of structured metadata representations within ML model zoos. In Chapter 2, we first propose a metamodel that represent different types of metadata, thus transforming the metadata from being merely descriptive to being queryable and machine-readable. The structured nature of our metamodel allows for more efficient querying and retrieval of information, which is a substantial improvement over the traditional, text-based descriptions.

Additionally, the thesis explores the use of metadata to optimize various ML processes, particularly in the selection of appropriate models for specific tasks, i.e., model inference and fine-tuning. In Chapter 3, we investigate the optimization of ML inference queries in heterogeneous model zoos using a Mixed-Integer-Programming-based optimizer. This optimizer, which considers multiple objectives such as accuracy and inference speed, provides a robust framework for model selection and execution planning. In Chapter 4, the research extends to model selection for fine-tuning. We investigate on predicting model performance, particularly accuracy, in scenarios where data domains shift, thus negating the need for constant model fine-tuning. By selectively choosing only the most promising candidates, this method substantially lowers the computational burden and associated costs of extensive model fine-tuning.

Overall, this thesis investigates the representation and application of metadata. The insights and methodologies presented not only improve the efficiency and effectiveness of ML workflows but also pave the way for further exploration in the integration of metadata within ML practices, highlighting the continual development and potential for advancements in ML.
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In this dissertation, we propose innovative approaches for assessing urban greenspace accessibility with a specific focus on factors affecting children’s access. Access to quality greenspaces in urban areas is crucial for fostering the health and well-being of children and providing spaces for recreation, socialization, and personal development. Unlike adults', children’s access to greenspaces is influenced by factors such as their daily activity patterns, levels of autonomy, and various physical and perceived barriers. While many studies in epidemiology, spatial equity, and urban planning aim to evaluate access to greenspaces for children across different urban scales and geographical contexts, they often utilize methods designed for the general population, overlooking the distinct factors affecting children's access. Our research addresses this gap by developing tailored methodologies that account for the specific needs and experiences of children in urban environments. ... Read more

Accessibility is a widely employed concept across a variety of disciplines to evaluate the degree to which individuals can reach a desired destination. Conventionally, accessibility is determined by the attractiveness of a destination and the associated travel cost to reach it. However, existing place-based accessibility measures do not differentiate between destinations accessible to individuals from a single demographic group and those accessible to individuals from diverse demographic groups. This hinders our ability to discern the encounter potential of different destinations. We address this gap by introducing the concept of co-accessibility to measure how accessible a given destination is to different individuals and demographic groups. ... Read more

Music annotation and transcription of music sheets are traditionally performed by experts. Although these processes result in high quality data, the scope of each effort is relatively narrow resulting in highly specialised and specific datasets of annotated music compositions, which leads to a fragmentation in the design efforts for automated tools. In music traditions such as classical music, the shortcomings of current digitization work- flows become even clearer: due to the vast corpus and varying stylistic intricacies, experts tend to have specific knowledge and take up projects that concern very specific periods or composers, limiting our reach in regards to conserving classical music information as a whole.

On the other hand, crowdsourcing has been successfully utilized in other domains for annotating different modalities (text, image, video, audio), despite the unreliable pool of expertise on online platforms. Commercially successful projects have utilized the crowd, which provided annotations of adequate quality. These annotations were later used to fuel machine learning methods that rely on bulks of annotated data to perform automatic classification, regression, and detection tasks. However, due to the complexity of music as an artifact, there are still only a few examples where the crowd was integrated into any form outside of subjective annotation tasks (e.g., indicate the mood of the excerpt).

In this thesis, we tackle this research gap of integrating the crowd in the annotation processes of music compositions. We surveyed current practices on Optical Music Recognition to identify parts where the crowd could assist, alongside proposing hybrid annotation workflows for music compositions. We studied the capabilities of online participants with unknown musical expertise, quantified their musical abilities, and related them to their performance in music annotation tasks. With our goal being to identify ways to expand the preservation efforts for classical music through the assistance of the general public, we investigated potential online sources of music information and prospective participants outside the currently available crowdsourcing platforms. To that end, we studied how composers’ popularity manifests on community-driven platforms through the interactions of music enthusiasts online. We also conducted interviews and focus group discussions with experts and semi-experts, to understand their quality requirements on semantically-rich digital music scores, and identified transcription patterns that could inform our task designs. We finally delivered our system architecture, which combines computer vision and algorithmic scheduling, with microtasks designed to be performed by human annotators in parallel.

Our findings show that with the right methods to quantify the musical competence of a person, paired with careful design of the annotation tasks and interfaces, we can successfully integrate the crowd in the music annotation processes, to generate meaningful and useful information regarding classical music compositions and beyond. This thesis enables future research by showcasing the versatility of the crowd and providing task design methods to accommodate their lack of formal training in the field. It also provides experimental methods to reliably identify how different music composition elements affect the crowd’s performance, alongside proposing user interface elements that can mediate the complexity of the artifacts. Practices such as those presented in this thesis can lead to scaling up our digitization efforts, generating accurate and useful annotations through the crowd, even in such domain-specific and knowledge-intensive topics as classical music compositions. ... Read more

Correctly processing accumulated information is beneficial for our survival. Berghman and Hekkert (2017) argue that this is why we humans derive pleasure from having a sense of aesthetics. These aesthetic experiences can be seen as our brain’s reward system for correctly perceiving and interpret- ing the world around us. While our senses have evolved to perceive and organise the physical world, these very mechanisms also come into play when we interact with the digital realm. Aesthetics in visual training datasets are of importance as it allows us to derive a sense of aesthetic pleasure from digital media. Integrating aesthetics into artificial intelligence, especially in text-to-image generators, becomes important to cater to humans psychological reward systems and to engage them at a deeper level.

This thesis is focused on investigating the annotation method used in the development of the LAION- Aesthetics V2 datasets and comparing it to other annotation methods for measuring aesthetics. The purpose is to explore whether there are more suitable alternatives to the current annotation method (where people are asked to annotate images with the instruction ”how much do you like this image on a scale from 1 to 10?”, (Schuhmann, 2022) which is not backed by literature to actually measure aesthetics), and to evaluate the alignment between the LAION Aesthetics Predictor scores and human ratings.

This thesis explores different distinct levels of inquiry: one focuses on the design of instructions for image annotation tasks (alternative task design), while the other centers around measuring aesthetics during the annotation process (alternative metrics). Both lines of inquiry are supported by relevant literature, indicating their potential capacity to capture aesthetics. In addition to comparing alternative annotation methods, this thesis investigates three hypotheses related to the annotation of aesthetics within the project’s context.

Four experiments are conducted using crowdsourcing to compare alternative task design and alternative metrics. The experiments include semantic concept activation, different phrasing of the annotation instruction, and alternative modalities (such as ranking and two-alternative forced choice). Next to these four experiments, a separate fifth experiment is deployed which looks into the evaluation of image content versus overall image liking. Two post hoc analyses are performed, one which compares scores that the LAION Aesthetics predictor assigns to the stimulus set to human image liking ratings, and one examining the influence of region on image liking ratings.

The LAION aesthetics approach performed equal to the alternatives with scientific backing. The ranking treatment even performed worse. For this data, region did not impact image liking ratings. No significant difference was found between participants’ overall image liking and content liking. The LAION Aesthetics predictor scores partially aligned with human liking ratings but showed some disparities, par- ticularly in extreme ratings. Qualitative analysis suggests that more research is necessary to make a judgement on whether ”liking” is a relevant and appropriate approach for capturing aesthetics.

The limitations of the experiments include small sample sizes and the focus on a specific image class (buildings). Recommendations for future research include exploring different image classes, investigating other ranking modalities, and considering n-alternative forced choice experiments. It is also suggested to examine the influence of regions on aesthetic experiences in more detail, explore Gibbs Sampling with People for measuring image aesthetics, and explore different demographic groups and contexts.
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This thesis delves into the development of a project that aims to boost the awareness and willingness of TU Delft's research staff to share laptop data, needed to promote the further adoption of the Circular Economy (CE) in the Information and Communication Technology (ICT) domain. The project navigates the intricate landscape of data privacy challenges, particularly focusing on laptop hardware and organizational device management, with the ultimate goal of supporting AI processes such as predictive maintenance that require laptop-specific data, which can be categorized as personal data.
The project employs the Agile methodology and the Double Diamond Model to iteratively design user interfaces that visually articulate informed consent for ICT (i.e. laptop) data sharing for CE processes, such as laptop’s repair, reuse, and return. The main objective is to help cultivate a more sustainable ICT service in TU Delft by enhancing CE data sharing transparency and offering incentives for informed consent with the User Interface (UI) design. The design of the user interface aims to simplify the comprehension of consent for data sharing, bring more transparency, and raise awareness about data privacy. This approach is expected to motivate the research staff to actively participate in data sharing, thereby contributing to sustainability efforts within the organization. The thesis culminates with the presentation of key findings and potential avenues for future exploration and research, providing valuable insights into the interplay of data privacy, user experience, and sustainability within ICT services. ... Read more

Immersive technologies, such as head-mounted devices for Virtual Reality (VR), have experienced increased availability on the consumer market. This opens up numerous possibilities for scientific research and industrial applications. But, configuring and combining data from various sources to create an effective visualization to explore, collaborate in and interact with, has only been researched in limited aspects. This thesis aims to demonstrate the feasibility of integrating these diverse aspects into a single application while preserving their individual potential.

To achieve this objective, an extensive analysis of the literature was done to establish the state of the art. All aspects necessary to create a single application were investigated to define how much of the literature fully or partly implemented them. Findings of the literature were summarized through definition, progress, limitations and improvements.

Based on the analysis, a proof of concept was designed and implemented, which established a virtual reality framework for real-time analysis and exploration of heterogeneous urban data. The framework involved the development of both a client and server applications with real-time communication. The server application processes and caches input data based on a structured configuration file, while the client application renders and visualizes points and shapes based on instructions from the server. The layered processing approach in this proof of concept facilitated real-time data handling and exploration. Which also makes it possible to collaborate with multiple people working on or manipulating the same data at the same time.

Evaluation of the server application showed its capability of processing over 1.000.000 objects from GeoJSON data. After initial rendering, the client application could update over 4.000 objects with a latency of just 200ms, which is considered to be interactive. Feedback from experts further supported the validation of the proof of concept application as a viable solution to the research question.

This research established the validity of a configurable real-time analysis and exploration solution in virtual reality. With further development and research, this tool holds potential for being used in diverse environments dealing with urban data. ... Read more

Responding to the trend of increasing use of artificial intelligence (AI), we need to ensure applications of AI are designed, implemented, utilised and evaluated in a careful manner. Explainable AI, or XAI, meaning; - given a certain audience, the details and reasons of both technical processes of the algorithm-support system and the reasoning behind the system to make its functioning clear or
easy to understand - is one of the ways to responsibly design and implement AI systems. This research looks into AI-supported public decision-making processes in the Netherlands and the role and possible contribution of XAI in such a context. To this end, I conducted a mixed-method qualitative study; interviewing sixteen respondents from three key-actor groups within two Dutch national public sector executive bodies, additionally performing three observations and document-analysis. Differentiating between different phases of an AI system’s implementation life-cycle, the study unveils how the respective actors - managers, data scientists and domain experts/(potential) AI users - encounter various challenges in bringing an AI system from idea to production. The empirical findings show that many AI systems, whilst technically developed, are not deployed or adopted by the wider organisation. The study discerns the challenges hindering the AI implementation process from an organisational,
human and technical point of view. Moreover, the study highlights the need to approach XAI from a multi-purpose, multi-actor perspective; both acknowledging that various actors need different kinds of explanations, but also bridging different respective professional worldviews to apprehend one another.
XAI is often seen as a one-size-fits-all solution for various implementation challenges, however the study shows that certain challenges need to be addressed at least beyond traditional ways of XAI from a computer science perspective, and perhaps beyond XAI all together. As such, the insights of this
thesis contribute to generating a more realistic idea about the opportunities and limitations of XAI, within real-world AI implementation processes in the public sector. ... Read more

As the sharing of machine learning (ML) models has increased in popularity, more so-called model zoos are created. These repositories facilitate the sharing of models and their metadata, and other people to find and re-use an existing model. However, the metadata provided for models is insufficient, with little focus on practical aspects of a model such as performance and limitations. This leaves model zoos to provide little functinality beyond the sharing of existing models, and potential users unable to find an optimal model for their needs. In this thesis we focus on the reporting of model performance, and aim to answer two questions: (1) What are the limitations of model performance reporting found in current model zoos, and (2) How can we provide rich, comparable performance metadata for these models. As a result, we have created a framework which can be used to create a benchmarking system for ML models. Our framework enables the automatic evaluation of models from any source, and through its design encourages the collection of rich and comparable performance metadata for those models. To demonstrate the effectiveness of our framework, we have created a benchmarking system and evaluated 1215 models from existing model zoos, along with a new interface to view this new metadata, and have enabled new use-cases such as the performance comparison of ML models. ... Read more

Machine learning (ML) is an artificial intelligence technology that has a great potential for being adopted in various sectors of activities. Yet, it is now also increasingly recognized as a hazardous technology. Failures in the outputs of an ML system might cause physical or social harms. Besides, the development and deployment of an ML system itself are also argued to be harmful in certain contexts.

Surprisingly, these hazards persist in applications where ML technology has been deployed, despite the increasing amount of research performed by the ML research community. In this thesis, we task ourselves with the challenges of understanding the reasons for the subsistence of hazardous system’s output failures and of hazardous development and deployment processes in practice, and of developing solutions to further diagnose these hazardous failures (especially in the system’s outputs). For that, we investigate further the nature of the potential gap between research and the practices of those developers who build and deploy the systems. To do so, we survey major related ML research directions, surface developers practices and challenges, and search for types of (mis)alignment between theory and practices. There, among others, we find a lack of technical support for ML developers to identify the potential failures of their systems. Hence, we then tackle the development and evaluation of a human-in-the-loop, explainability-based, failure diagnosis method and user-interface for computer vision systems...
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Supporting designers working with AI is important, yet the tools they have at their disposal are in many ways not satisfactory. The aim of this project was to design a digital canvas tool which lets designers explore AI and ML, adopt the affordances that AI and ML as a design material give, and which can be used to map out and design integrated products, services and systems (iPSSs) by not just guiding the design process but by becoming an integral part of it. ... Read more