Ethical Considerations and Trustworthy Industrial AI Systems

The ethics of AI in industrial environments is a new field within applied ethics, with notable dynamics but no well-established issues and no standard overviews. It poses many more challenges than similar consumer and general business applications, and the digital transformation of industrial sectors has brought into the ethical picture even more considerations to address. This relates to integrating AI and autonomous learning machines based on neural networks, genetic algorithms, and agent architectures into manufacturing processes. This article presents the ethical challenges in industrial environments and the implications of developing, implementing, and deploying AI technologies and applications in industrial sectors in terms of complexity, energy demands, and environmental and climate changes. It also gives an overview of the ethical considerations concerning digitising industry and ways of addressing them, such as potential impacts of AI on economic growth and productivity, workforce, digital divide, alignment with trustworthiness, transparency, and fairness. Additionally, potential issues concerning the concentration of AI technology within only a few companies, human-machine relationships, and behavioural and operational misconduct involving AI are examined. Manufacturers, designers, owners, and operators of AI—as part of autonomy and autonomous industrial systems—can be held responsible if harm is caused. Therefore, the need for accountability is also addressed, particularly related to industrial applications with non-functional requirements such as safety, security, reliability, and maintainability supporting the means of AI-based technologies and applications to be auditable via an assessment either internally or by a third party. This requires new standards and certification schemes that allow AI systems to be assessed objectively for compliance and results to be repeatable and reproducible. This article is based on work, findings, and many discussions within the context of the AI4DI project.publishedVersio

A Flexible and Future-Proof Power Model for Cellular Base Stations

The power efficiency of cellular base stations is a crucial element to maintain sustainability of future mobile networks. To investigate future network concepts, a good power model is required which is highly flexible to evaluate the diversity of power saving options. This paper presents an advanced power model which supports a broad range of network scenarios and base station types, features and configurations. In addition to the power consumption, the model also provides values on the hardware sleep capabilities (sleep depths, transition times, power savings). The paper also discusses the technology trends and scaling factors which are used to predict the power consumption of base stations up to the year 2020. Two use cases are described, illustrating the power savings over different sleep depths, and quantifying the power consumption evolution over different technology generationsstatus: submitte

33.1 Energy-Scalable OFDM Transmitter Design and Control

Orthogonal Frequency Division Multiplexing (OFDM) is the modulation of choice for broadband wireless communications. Unfortunately, it comes at the cost of a very low energy efficiency of the analog transmitter. Numerous circuit-level and signal processing techniques have been proposed to improve that energy efficiency. However more disruptive improvement can be achieved at system-level, capitalizing on energy-scalable design and circuit reconfiguration to match the user requirements and operation environment. We describe the design of such an energyscalable reconfigurable transmitter as well as its control strategy. Based on measurement carried out on the physical realization of this transmitter, the benefit of system-level energy management is shown. Energy-efficiency scalability ranges over 30%, which translates in an average system-level energy improvement of up to 40 % compared to a non-scalable system

Programmable Systems for Intelligence in Automobiles (PRYSTINE): Final results after Year 3

Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3