This results in significant privacy risks, such as automated influence [7], manipulation [54], and potential security breaches. Yet, while companies invest heavily in acquiring and analyzing their users' personal data, users without extensive research or background knowledge lack awareness of the associated privacy risks [29] or have distorted perceptions of risks [31], which results in irrational decisions [1]. Regulations such as the GDPR [25] force companies to communicate data management practices and users' rights regarding their data in privacy policies, to enhance users' decision-making. However, evidence shows that companies focus on compliance, effectively targeting lawyers instead of users [78], so users rarely read privacy policies [61]. Using LLMs to automatically assess privacy policies is a promising approach to solve this issue [37, 72, 91]. Yet, no prior work evaluates their impact on understandability and risk awareness from a user's perspective through a user study. Additionally, to the best of our knowledge, no existing tool combines LLM-based automatic privacy policy assessment with: (i) dynamic evaluation criteria not focused on compliance but tailored to type of platform (e.g., e-commerce or health services); (ii) an interactive dashboard; and (iii) a chat for open conversations with the LLM with (iv) customizable explanations and responses that adapt to the user's preferences for detail and complexity. To address these gaps, we introduce PRISMe (Privacy Risk Information Scanner for Me), a Chrome extenstion with the above features designed to empower users in making informed privacy decisions. To evaluate PRISMe, we conducted a scenario-based, mixed-methods user study with a qualitative focus.

Artificial intelligence through machine learning is increasingly used in the digital society. Solutions based on machine learning bring both great opportunities, thus coined "Software 2.0," but also great challenges for the engineering community to tackle. Due to the experimental approach used by data scientists when developing machine learning models, agility is an essential characteristic. In this keynote address, we discuss two contemporary development phenomena that are fundamental in machine learning development, i.e., notebook interfaces and MLOps. First, we present a solution that can remedy some of the intrinsic weaknesses of working in notebooks by supporting easy transitions to integrated development environments. Second, we propose reinforced engineering of AI systems by introducing metaphorical buttresses and rebars in the MLOps context. Machine learning-based solutions are dynamic in nature, and we argue that reinforced continuous engineering is required to quality assure the trustworthy AI systems of tomorrow.

With the growing capabilities of autonomous vehicles, there is a higher demand for sophisticated and pragmatic quality assurance approaches for machine learning-enabled systems in the automotive AI context. The use of simulation-based prototyping platforms provides the possibility for early-stage testing, enabling inexpensive testing and the ability to capture critical corner-case test scenarios. Simulation-based testing properly complements conventional on-road testing. However, due to the large space of test input parameters in these systems, the efficient generation of effective test scenarios leading to the unveiling of failures is a challenge. This paper presents a study on testing pedestrian detection and emergency braking system of the Baidu Apollo autonomous driving platform within the SVL simulator. We propose an evolutionary automated test generation technique that generates failure-revealing scenarios for Apollo in the SVL environment. Our approach models the input space using a generic and flexible data structure and benefits a multi-criteria safety-based heuristic for the objective function targeted for optimization. This paper presents the results of our proposed test generation technique in the 2021 IEEE Autonomous Driving AI Test Challenge. In order to demonstrate the efficiency and effectiveness of our approach, we also report the results from a baseline random generation technique. Our evaluation shows that the proposed evolutionary test case generator is more effective at generating failure-revealing test cases and provides higher diversity between the generated failures than the random baseline.

Artificial Intelligence (AI) is increasingly used in critical applications. Thus, the need for dependable AI systems is rapidly growing. In 2018, the European Commission appointed experts to a High-Level Expert Group on AI (AI-HLEG). AI-HLEG defined Trustworthy AI as 1) lawful, 2) ethical, and 3) robust and specified seven corresponding key requirements. To help development organizations, AI-HLEG recently published the Assessment List for Trustworthy AI (ALTAI). We present an illustrative case study from applying ALTAI to an ongoing development project of an Advanced Driver-Assistance System (ADAS) that relies on Machine Learning (ML). Our experience shows that ALTAI is largely applicable to ADAS development, but specific parts related to human agency and transparency can be disregarded. Moreover, bigger questions related to societal and environmental impact cannot be tackled by an ADAS supplier in isolation. We present how we plan to develop the ADAS to ensure ALTAI-compliance. Finally, we provide three recommendations for the next revision of ALTAI, i.e., life-cycle variants, domain-specific adaptations, and removed redundancy.

AI solutions seem to appear in any and all application domains. As AI becomes more pervasive, the importance of quality assurance increases. Unfortunately, there is no consensus on what artificial intelligence means and interpretations range from simple statistical analysis to sentient humanoid robots. On top of that, quality is a notoriously hard concept to pinpoint. What does this mean for AI quality? In this paper, we share our working definition and a pragmatic approach to address the corresponding quality assurance with a focus on testing. Finally, we present our ongoing work on establishing the AIQ Meta-Testbed.

Understanding the heat use of customers is crucial for effective district heating (DH) operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce and very few studies have been focusing on this aspect. The deployment of smart meters offers a unique opportunity for researchers and DH utilities to analyze large-scale data and discover both typical, as well as atypical, patterns in the network. Heat load pattern discovery is a challenging task in DH systems, since a comprehensive analysis needs to involve many customers. Most of the past studies have relied on analysis of a small number of buildings, which are not shown to be picked as the representative examples. Therefore, the knowledge discovered in such studies is not enough to generalize for the entire network. In this work, we propose a data-driven approach that enables automatic discovery of heat load patterns in a complete district heating network. Our method clusters the buildings into different groups based on the characteristics of their load profiles, extracts the representative patterns for each of them, and detects abnormal profiles, i.e., the ones deviating from the expected behavior. We present the first comprehensive analysis of the heat load patterns by conducting a case study on all the buildings, in six customer categories, connected to two district heating networks in the south of Sweden. Our method has captured fifteen typical patterns among the heat load profiles of all buildings in our dataset. It shows that control strategies are not enough to explain the variability in the heat load behaviors. In conclusion, we demonstrate that the proposed approach has a great potential to develop knowledge about customers and their heat use habits in practice by automatically analyzing their typical and atypical profiles in large-scale.

Einride's autonomous T-Pod may look like a giant freezer on wheels, but it's likely to be on the road fairly soon. According to TechCrunch, the company announced today that the first customer deliveries of the self-driving transport vehicle will begin this fall. The T-pod can transport standard cargo pallets and travel up to 124 miles on a single charge. It can drive itself on highways, but a human will take over on main roads via remote. TechCrunch says that the trucks will use the Nvidia Drive AI platform to plan driving paths and intelligently sense the environments.

Einride has just revealed the prototype of the T-pod, its autonomous electric truck. The Swedish company's self-driving vehicle can transport 15 standard pallets and can travel 124 miles on one charge. And because there's no need for a person to sit inside of it, the T-pod also has no cab space and no windows -- giving it a very futuristically odd look. The truck uses a hybrid driverless system. While on highways, the T-pod drives itself, but on main roads, a human will remotely manage the driving system.

As more companies wade into the business of building artificial intelligence systems to help you drive (or do the driving for you), a startup founded by an ex-Apple computer vision specialist is open sourcing a huge dataset that can help them on their road to autonomy. Mapillary, a Swedish startup backed by Sequoia, Atomico and others that has built a database of 130 million images through crowdsourcing -- think open-source Street View -- is releasing a free dataset of 25,000 street-level images from 190 countries, with pixel-level annotations that can be used to train automotive AI systems. The Mapillary Vistas Dataset claims to be "the world's largest, most diverse dataset for object recognition on street-level imagery." As with the rest of Mapillary's photos, the startup builds its image database on top of Mapbox and OpenStreetMap maps. The dataset is free for both academic and commercial researchers, and if anyone wants to build the results into commercial products, they must pay a commercial license.

It could be the end of'heavy, noisy trucks with monstrous emissions'. A Swedish startup has unveiled a radical design for a driverless truck that runs entirely on electric power with a range of 124 miles on a single charge. Deemed'cargo with wheels', the'T-pod' is fully autonomous on highways and only needs human intervention when it exits onto local roads - and a human controls it remotely. A Swedish startup has unveiled a radical design for a driverless truck that runs entirely on electric power with a range of 124 miles on a single charge. Deemed'cargo with wheels', the'T-pod' is fully autonomous on highways and only needs human intervention on local roads T-Pod is driverless truck that runs entirely on electric power with a range of 124 miles on a single charge.