Averting Robot Eyes

Home robots will cause privacy harms. At the same time, they can provide beneficial services—as long as consumers trust them. This Essay evaluates potential technological solutions that could help home robots keep their promises, avert their eyes, and otherwise mitigate privacy harms. Our goals are to inform regulators of robot-related privacy harms and the available technological tools for mitigating them, and to spur technologists to employ existing tools and develop new ones by articulating principles for avoiding privacy harms. We posit that home robots will raise privacy problems of three basic types: (1) data privacy problems; (2) boundary management problems; and (3) social/relational problems. Technological design can ward off, if not fully prevent, a number of these harms. We propose five principles for home robots and privacy design: data minimization, purpose specifications, use limitations, honest anthropomorphism, and dynamic feedback and participation. We review current research into privacy-sensitive robotics, evaluating what technological solutions are feasible and where the harder problems lie. We close by contemplating legal frameworks that might encourage the implementation of such design, while also recognizing the potential costs of regulation at these early stages of the technology

Towards a Digital Ecosystem of Trust: Ethical, Legal and Societal Implications

The European vision of a digital ecosystem of trust rests on innovation, powerful technological solutions, a comprehensive regulatory framework and respect for the core values and principles of ethics. Innovation in the digital domain strongly relies on data, as has become obvious during the current pandemic. Successful data science, especially where health data are concerned, necessitates establishing a framework where data subjects can feel safe to share their data. In this paper, methods for facilitating data sharing, privacy-preserving technologies, decentralization, data altruism, as well as the interplay between the Data Governance Act and the GDPR, are presented and discussed by reference to use cases from the largest pan-European social science data research project, SoBigData++. In doing so, we argue that innovation can be turned into responsible innovation and Europe can make its ethics work in digital practice

What matters? Unlocking householders’ flexibility towards cooling automation in India

In emerging economies like India, where air conditioners are projected to triple by 2050 — mostly from household use — demand response programs such as cooling automation have gained currency as a suitable approach to address peak electricity from cooling demand. Environmentally commoning/intentional communities are classic contexts in which flexible cooling consumption might be easily realised. Utilising materialist theory and a six-month cooling automation trial and workshops with twenty households in an intentional community in South India, this study explores factors that shape householders’ pliability or rejection of cooling automation. Results reveal that while commoning identity plays a significant role in householders\u27 flexibility towards automation, extreme heat creates a clash between householders\u27 environmental beliefs and comfort needs, altering their response to automation. We conclude by discussing the theoretical implications arising from these findings and suggest how utilities could respond to these dynamics to foster a transition to a low-carbon energy system

Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review

The Human Activity Recognition (HAR) tasks automatically identify human activities using the sensor data, which has numerous applications in healthcare, sports, security, and human-computer interaction. Despite significant advances in HAR, critical challenges still exist. Game theory has emerged as a promising solution to address these challenges in machine learning problems including HAR. However, there is a lack of research work on applying game theory solutions to the HAR problems. This review paper explores the potential of game theory as a solution for HAR tasks, and bridges the gap between game theory and HAR research work by suggesting novel game-theoretic approaches for HAR problems. The contributions of this work include exploring how game theory can improve the accuracy and robustness of HAR models, investigating how game-theoretic concepts can optimize recognition algorithms, and discussing the game-theoretic approaches against the existing HAR methods. The objective is to provide insights into the potential of game theory as a solution for sensor-based HAR, and contribute to develop a more accurate and efficient recognition system in the future research directions

Energy Forensics Analysis

The energy consumed by a building can reveal information about the occupants and their activities inside the building. This could be utilized by industries and law enforcement agencies for commercial or legal purposes. Utility data from Smart Meter (SM) readings can reveal detailed information that could be mapped to foretell resident occupancy and type of appliance usage over desired time intervals. However, obtaining SM data in the United States is laborious and subjected to legal and procedural constraints. This research develops a user-driven simulation tool with realistic data options and assumptions of potential human behavior to determine energy usage patterns over time without any utility data. In this work, factors such as occupant number, the possibility of place being occupied, thermostat settings, building envelope, appliances used in households, appliance capacities, and the possibility of using each appliance, weather, and heating-cooling systems specifications are considered. For five specific benchmarked scenarios, the range of the random numbers is specified based on assumed potential human behavior for occupancy and energy-consuming appliances usage possibility, with respect to the time of the day, weekday, and weekends. The simulation is developed using the Visual Basic Application (VBA)® in Microsoft Excel®, based on the discrete-event Monte Carlo Simulation (MCS). This simulation generates energy usage patterns and electricity and natural gas costs over 30-minutes intervals for one year. The simulated energy usage and the cost are reflected in the sensitivity analysis by comparing factors such as occupancy, appliance type, and time of the week. This work is intended to facilitate the analysis of building occupants\u27 activities by various stakeholders, subject to all legal provisions that apply. It is not intended for the general public to pursue these activities because legal ramifications might be involved

Information Sharing Tears of Irony: An Exploratory Study of the Information Sharing Paradox in the Intelligence Community

The sharing of information across government intra- and inter-agencies provides enormous benefits to Intelligence operations, but it also poses risks to Intelligence organizations’ operational capability. These benefits and risks of sharing information within Intelligence Communities introduce a paradox that disturbs decision-making abilities and affect existing and future relationships with local and national Intelligence partners. With this paradox, there exist particular forces that affect the paradox, such as organizational factors and the behavior of an information sharer, the responsible actor that decides on how, when and with whom to share the information. Combining the two can produce a positive (desired) outcome that leads to successful mission accomplishment or negative (inadvertent) outcome that leads to loss of information disclosed or intentional loss of valuable information. An inadvertent outcome could result in an impact to the national defense of the United States. Do Intelligence Analysts share information when the risks outweigh the benefits? This research examines how understanding the paradox of information sharing is a critical element in understanding the behavior of Intelligence Analysts’ decision-making in Intelligence operations

Metalearning-Informed Competence in Children: Implications for Responsible Brain-Inspired Artificial Intelligence

This paper offers a novel conceptual framework comprising four essential cognitive mechanisms that operate concurrently and collaboratively to enable metalearning (knowledge and regulation of learning) strategy implementation in young children. A roadmap incorporating the core mechanisms and the associated strategies is presented as an explanation of the developing brain's remarkable cross-context learning competence. The tetrad of fundamental complementary processes is chosen to collectively represent the bare-bones metalearning architecture that can be extended to artificial intelligence (AI) systems emulating brain-like learning and problem-solving skills. Utilizing the metalearning-enabled young mind as a model for brain-inspired computing, this work further discusses important implications for morally grounded AI.Comment: 27 pages, 3 figure