Safety in human in the loop systems, systems that change behavior with human input, is difficult to achieve. This difficulty can cost lives. As desired system capability grows, so too does the requisite complexity of the system. This complexity can result in designers not accounting for every use case of the system and unintentionally designing in unsafe behavior. Furthermore, complexity of operation and control can result in operators becoming confused during use or receiving insufficient training in the first place. All these cases can result in unsafe operations. One method of improving safety is implementing the use of formal models during the design process. These formal models can be analyzed mathematically to detect dangerous conditions, but can be difficult to produce without time, money, and expertise.<br> This document details the study of potential methods for constructing formal models autonomously from recorded observations of system use, minimizing the need for system expertise, saving time, money, and personnel in this safety critical process. I first discuss how different system characteristics affect system modeling, isolating specific traits that most clearly affect the modeling process Then, I develop a technique for modeling a simple, digital, menu-based system based on a record of user inputs. This technique attempts to measure the availability of different inputs for the user, and then distinguishes states by comparing input availabilities. From there, I compare paths between states and check for shared behaviors. I then expand the general procedure to capture the behavior of a flight simulator. This system more closely resembles real-world safety critical systems and can therefore be used to approximate a real use case of the method outlined. I use machine learning tools for statistical analysis, comparing patterns in system behavior and user behaviors. Last, I discuss general conclusions on how the modeling approaches outlined in this document can be improved and expanded upon.<br> For simple systems, we find that inputs alone can produce state machines, but without corresponding system information, they are less helpful for determining relative safety of different use cases than is needed. Through machine learning, we find that records of complex system use can be decomposed into sets of nominal and anomalous states but determining the causal link between user inputs and transitions between these conditions is not simple and requires further research.

Machine Learning (ML) and Artificial Intelligence (AI) impact many aspects of human life, from recommending a significant other to assist the search for extraterrestrial life. The area develops rapidly and exiting unexplored design spaces are constantly laid bare. The focus in this work is one of these areas; ML systems where decisions concerning ML model training, usage and selection of target domain lay in the hands of domain experts.  This work is then on ML systems that function as a tool that augments and/or enhance human capabilities. The approach presented is denoted Human In Command ML (HIC-ML) systems. To enquire into this research domain design experiments of varying fidelity were used. Two of these experiments focus on augmenting human capabilities and targets the domains commuting and sorting batteries. One experiment focuses on enhancing human capabilities by identifying similar hand-painted plates. The experiments are used as illustrative examples to explore settings where domain experts potentially can: independently train an ML model and in an iterative fashion, interact with it and interpret and understand its decisions.  HIC-ML should be seen as a governance principle that focuses on adding value and meaning to users. In this work, concrete application areas are presented and discussed. To open up for designing ML-based products for the area an abstract model for HIC-ML is constructed and design guidelines are proposed. In addition, terminology and abstractions useful when designing for explicability are presented by imposing structure and rigidity derived from scientific explanations. Together, this opens up for a contextual shift in ML and makes new application areas probable, areas that naturally couples the usage of AI technology to human virtues and potentially, as a consequence, can result in a democratisation of the usage and knowledge concerning this powerful technology.

<p dir="ltr">Recognition of human behavior and object status plays a significant role in context-aware applications. Although researchers have explored methods to detect users’ activities, there still exists a research gap where end-users are not able to build personalized applications that accurately recognize their activities in the physical environment. Therefore, in this thesis, to fill the gap, we explore different in-situ context-aware Augmented Reality (AR) applications that leverage hand-object recognition and support users to rapidly author context-aware applications by referring to their activities. We first explore the possibility that end-users develop AR applications with customized freehand interactions and introduce GesturAR, an end-to-end authoring tool that supports users to create freehand AR applications through embodied demonstration and visual programming. Next, we explore hand interactions with physical objects and propose ARnnotate, an AR system enabling end-users to create custom data. To further study context-aware applications with haptic feedback, we present Ubi Edge, an AR authoring tool that allows end-users to customize edges on daily objects as tangible user interface (TUI) inputs to control varied digital functions. We develop an integrated AR device and a vision-based detection workflow to track 3D edges and detect the touch interaction between fingers and edges. Finally, to enable end-users to apply AR applications in different physical environments, we propose AdapTUI, a system that can automatically adapt the geometric-based TUIs in various environments. In summary, our contribution lies in the development of several context-aware AR applications that effectively sense users’ activities and facilitate users’ authoring process intuitively and conveniently.</p>

<p dir="ltr">Bias and fairness issues in artificial intelligence algorithms are major concerns as people do not want to use AI software they cannot trust. This work uses college admissions data as a case study to develop methodology to define and detect bias, and then introduces a new method for interactive bias mitigation.</p><p dir="ltr">Admissions data spanning six years was used to create machine learning-based predictive models to determine whether a given student would be directly admitted into the School of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed predictive models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted.</p><p dir="ltr">Then, using a variety of bias and fairness metrics, we analyzed these predictive models to detect biases the models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We found that high accuracy rates can mask underlying algorithmic bias towards these sensitive groups.</p><p dir="ltr">Finally, we describe our method for bias mitigation which uses a combination of machine learning and user interaction. Because bias is intrinsically a subjective and context-dependent matter, it requires human input and feedback. Our approach allows the user to iteratively and incrementally adjust bias and fairness metrics to change the training dataset for an AI model to make the model more fair. This interactive bias mitigation approach was then used to successfully decrease the biases in three AI models in the context of undergraduate student admissions.</p>

Human-in-the-loop simulators have long been used in the research community as well as in industry. The aviation field has been the pioneers in the use of simulators for design purposes. In contrast, corresponding activities in the automotive area have been less widespread. Published reports on experimental activities based on human-in-the-loop simulations have focused on methods used in the study, but nobody seems to have taken a step back and looked at the wider methodological picture of Simulator-Based Design. The purpose of this thesis is to fill this gap by drawing, in part, upon the author’s long experience in this field. In aircraft and lately also in ground vehicles there has been a technology shift from pure mechanics to computer-based systems. The physical interface has turned into screen-based solutions. This trend towards glass has just begun for ground vehicles. This development in vehicle technology has opened the door for new design approaches, not only for design itself, but also for the development process. Simulator-Based Design (SBD) is very compatible with this trend. The first part of this thesis proposes a structure for the process of SBD and links it to the corresponding methodology for software design. In the second part of the thesis the focus changes from methodology to application and specifically to the design of three-dimensional situation displays. Such displays are supposed to support the human operator with a view of a situation beyond the more or less limited visual range. In the aircraft application interest focuses on the surrounding air traffic in the light of the evolving free-flight concept, where responsibility for separation between aircraft will be (partly) transferred from ground-based flight controllers to air crews. This new responsibility must be supported by new technology and the situational view must be displayed from the perspective of the aircraft. Some basic design questions for such 3D displays were investigated resulting in an adaptive interface approach, where the current situation and task govern the details of information presentation. The thesis also discusses work on situation displays for ground vehicles. The most prominent example may be the Night Vision system, where the road situation ahead is depicted on a screen in the cab. The existing systems are based on continuous presentation, an approach that we have questioned, since there is strong evidence for negative behavioral adaptation. This means, for example, that the driver will drive faster, since vision has been enhanced, and thereby consume the safety margins that the system was supposed to deliver. Our investigation supports a situation-dependant approach and no continuous presentation. In conclusion, the results from our simulator-based studies showed advantages for adaptive interface solutions. Such design concepts are much more complicated than traditional static interfaces. This finding emphasizes the need for more dynamic design resources in order to have a complete understanding of the situation-related interface changes. The use of human-in-the-loop simulators and deployment of Simulator-Based Design will satisfy this need.

Artificial intelligence (AI) is revolutionizing contemporary industries and being applied in application domains ranging from recommendation systems to self-driving cars. In scenarios in which humans are interacting with an AI, inaccurate algorithms could lead to human mistreatment or even harmful events. Human-in-the-loop computing is a machine learning approach desiring hybrid intelligence, the combination of human and machine intelligence, to achieve accurate and interpretable results. This thesis applies human-in-the-loop computing in a Design Science Research project with a Swedish manufacturing company to make operational processes more efficient. The thesis aims to investigate emerging design principles useful for designing machine learning algorithms of hybrid intelligence. Hereby, the thesis has two key contributions: First, a theoretical framework is built that comprises general design knowledge originating from Information Systems (IS) research. Second, the analysis of empirical findings leads to the review of general IS design principles and to the formulation of useful design principles for human-in-the-loop computing. Whereas the principle of AI-readiness improves the likelihood of strategical AI success, the principle of hybrid intelligence shows how useful it can be to trigger a demand for human-in-the-loop computing in involved stakeholders. The principle of use case-marketing might help designers to promote the customer benefits of applying human-in-the-loop computing in a research setting. By utilizing the principle of power relationship and the principle of human-AI trust, designers can demonstrate the humans’ power over AI and build a trusting human-machine relationship. Future research is encouraged to extend and specify the formulated design principles and employ human-in-the-loop computing in different research settings. With regard to technological advancements in brain-machine interfaces, human-in-the-loop computing might even become much more critical in the future.

<p>With the advents in hardware techniques and mobile computing powers, Augmented Reality (AR) has been promising in various areas of our everyday life and work. By superimposing virtual assets onto the real world, the boundary between the digital and physical spaces has been significantly blurred, which bridges a large amount of digital augmentation and intelligence with the surroundings of the physical reality. Meanwhile, thanks to the increasing developments of Artificial Intelligence (AI) perception algorithms such as object detection, scene reconstruction, and human tracking, the dynamic behaviors of digital AR content have extensively been associated with the physical contexts of both humans and environments. This context-awareness enabled by the emerging techniques enriches the potential interaction modalities of AR experiences and improves the intuitiveness and effectiveness of the digital augmentation delivered to the consumers. Therefore, researchers are gradually motivated to include more contextual information in the AR domain to create novel AR experiences used for augmenting their activities in the physical world.</p> <p>    </p> <p>On a broader level, our work in this thesis focuses on novel designs and modalities that combine contextual information with AR content behaviors in context-aware AR experiences. In particular, we design the AR experiences by inspecting different types of contexts from the real world, namely 1) human actions, 2) physical entities, and 3) interactions between humans and physical environments. To this end, we explore 1) software and hardware modules, and conceptual models that perceive and interpret the contexts required by the AR experiences, and 2) supportive authoring tools and interfaces that enable users and designers to define the associations of the AR contents and the interaction modalities leveraging the contextual information. In this thesis, we mainly study the following workflows: 1) designing adaptive AR tutoring systems for human-machine-interactions, 2) customizing human-involved context-aware AR applications, 3) authoring shareable semantic-aware AR experiences, and 4) enabling hand-object-interaction datasets collection for scalable context-aware AR application deployment. We further develop the enabling techniques and algorithms including 1) an adaptation model that adaptively vary the AR tutoring elements based on the real-time learner's interactions with the physical machines, 2) a customized video-see-through AR headset for pervasive human-activity detecting, 3) a semantic adaptation model that adjusts the spatial relationships of the AR contents according to the semantic understanding of different physical entities and environments, and 4) an AR-based interface that empowers novice users to collect high-quality datasets used for training user- and cite-specific networks in hand-object-interaction-aware AR applications.</p> <p><br></p> <p>Takeaways from the research series include 1) the usage of the modern AI modules effectively enlarges both the spatial and contextual scalability of AR experiences, and 2) the design of the authoring systems and interfaces lowers the barrier for end-users and domain experts to leverage AI outputs in the creation of AR experiences that are tailored for target users. We conclude that involving AI techniques in both the creation and implementation stages of AR applications is crucial to building an intelligent, adaptive, and scalable ecosystem of context-aware AR applications.</p>

<p dir="ltr">In the context of a Human-in-the-Loop (HITL) system, the accuracy of reachability analysis plays a significant role in ensuring the safety and reliability of HITL systems. In addition, one can avoid unnecessary conservativeness by explicitly considering human control behavior compared to those methods that rely on the system dynamics alone. One possible approach is to use a Gaussian Mixture Model (GMM) to encode human control behavior using the Expectation-Maximization (EM) algorithm. However, relatively few works consider the admissible control input ranges due to physical limitations when modeling human control behavior. This could make the following reachability analysis overestimate the system's capability, thereby affecting the performance of the HITL system. To address this issue, this work presents a constrained stochastic reachability analysis algorithm that can explicitly account for the admissible control input ranges. By confining the ellipsoidal confidence region of each Gaussian component using Sequential Quadratic Programming (SQP), we probabilistically constrain the GMM as well as the corresponding stochastic reachable sets. A comprehensive mathematical analysis of how the constrained GMM can affect the stochastic reachable sets is provided in this work. Finally, the proposed stochastic reachability analysis algorithm is validated via an illustrative numerical example.</p>

A paradigm shift is emerging in system reliability and maintainability. The military and industrial sectors are moving away from the traditional breakdown and scheduled maintenance to adopt concepts referred to as Condition Based Maintenance (CBM) and Prognostic Health Management (PHM). In addition to signal processing and subsequent diagnostic and prognostic algorithms these new technologies involve storage of large volumes of both quantitative and qualitative information to carry out maintenance tasks effectively. This not only requires research and development in advanced technologies but also the means to store, organize and access this knowledge in a timely and efficient fashion. Knowledge-based expert systems have been shown to possess capabilities to manage vast amounts of knowledge, but an intelligent systems approach calls for attributes like learning and adaptation in building autonomous decision support systems. This research presents an integrated knowledge-based approach to diagnostic reasoning for CBM of engineering systems. A two level diagnosis scheme has been conceptualized in which first a fault is hypothesized using the observational symptoms from the system and then a more specific diagnostic test is carried out using only the relevant sensor measurements to confirm the hypothesis. Utilizing the qualitative (textual) information obtained from these systems in combination with quantitative (sensory) information reduces the computational burden by carrying out a more informed testing. An Industrial Language Processing (ILP) technique has been developed for processing textual information from industrial systems. Compared to other automated methods that are computationally expensive, this technique manipulates standardized language messages by taking advantage of their semi-structured nature and domain limited vocabulary in a tractable manner. A Dynamic Case-based reasoning (DCBR) framework provides a hybrid platform for diagnostic reasoning and an integration mechanism for the operational infrastructure of an autonomous Decision Support System (DSS) for CBM. This integration involves data gathering, information extraction procedures, and real-time reasoning frameworks to facilitate the strategies and maintenance of critical systems. As a step further towards autonomy, DCBR builds on a self-evolving knowledgebase that learns from its performance feedback and reorganizes itself to deal with non-stationary environments. A unique Human-in-the-Loop Learning (HITLL) approach has been adopted to incorporate human feedback in the traditional Reinforcement Learning (RL) algorithm.

A helicopter can be used to transport a load hanging from a suspension cable. This technique is frequently used in construction, firefighting, and disaster relief operations, among other applications. Unfortunately, the suspended load swings, which makes load positioning difficult and can degrade control of the helicopter. This dissertation investigates the use of input shaping (a command-filtering technique for reducing vibration) to mitigate the load swing problem. The investigation is conducted using two different, but complementary, approaches. One approach studies manual tracking tasks, where a human attempts to make a cursor follow an unpredictably moving target. The second approach studies horizontal repositioning maneuvers on small-scale helicopter systems, including a novel testbed that limits the helicopter and suspended load to move in a vertical plane. Both approaches are used to study how input shaping affects control of a flexible element (the suspended load) and a driven base (the helicopter). In manual tracking experiments, conventional input shapers somewhat degraded control of the driven base but greatly improved control of the flexible element. New input shapers were designed to improve load control without negatively affecting base control. A method for adjusting the vibration-limiting aggressiveness of any input shaper between unshaped and fully shaped was also developed. Next, horizontal repositioning maneuvers were performed on the helicopter testbed using a human-pilot-like feedback controller from the literature, with parameter values scaled to match the fast dynamics of the model helicopter. It was found that some input shapers reduced settling time and peak load swing when applied to Attitude Command or Translational Rate Command response types. When the load was used as a position reference instead of the helicopter, the system was unstable without input shaping, and adding input shaping to a Translational Rate Command was able to stabilize the load-positioning system. These results show the potential to improve the safety and efficiency of helicopter suspended load operations.