Science in the Era of ChatGPT, Large Language Models and AI: Challenges for Research Ethics Review and How to Respond

Large language models of artificial intelligence (AI) such as ChatGPT find remarkable but controversial applicability in science and research. This paper reviews epistemological challenges, ethical and integrity risks in science conduct. This is with the aim to lay new timely foundations for a high-quality research ethics review in the era of AI. The role of AI language models as a research instrument and subject is scrutinized along with ethical implications for scientists, participants and reviewers. Ten recommendations shape a response for a more responsible research conduct with AI language models

ДЕЦЕНТРАЛИЗАЦИЯ В ЦИФРОВОМ ОБЩЕСТВЕ: ПАРАДОКС ДИЗАЙНА

Digital societies come with a design paradox: On the one hand, technologies, such as Internet of Things, pervasive and ubiquitous systems, allow a distributed local intelligence in interconnected devices of our everyday life such as smart phones, smart thermostats, self-driving cars, etc. On the other hand, Big Data collection and storage is managed in a highly centralized fashion, resulting in privacy-intrusion, surveillance actions, discriminatory and segregation social phenomena. What is the difference between a distributed and a decentralized system design? How “decentralized” is the processing of our data nowadays? Does centralized design undermine autonomy? Can the level of decentralization in the implemented technologies influence ethical and social dimensions, such as social justice? Can decentralization convey sustainability? Are there parallelisms between the decentralization of digital technology and the decentralization of urban development?Цифровая трансформация основывается на автоматизированных процессах и инвестициях в новые технологии: искусственный интеллект, блокчейн, анализ данных и интернет вещей. Но в центре успешной стратегии цифровой трансформации все равно находится человек. Цифровая трансформация порождает парадоксы новых моделей: с одной стороны, распространяются повсеместно технологии, такие, как интернет вещей, большие данные позволяют улучшить продукты и услуги для потребителей, предложить им новую ценность и т. д. Но, с другой стороны, аналитика данных и их хранение управляются высокоцентрализованным способом, приводящим к вторжению в частную жизнь людей, контролю за их действиями, к дискриминационным и сегрегационным социальным явлениям. В статье рассматриваются вопросы: каково различие между распределенным и децентрализованным системным проектированием? Как возможна организация «децентрализованной» обработки персональных  данных в наше время? Подрывают ли централизованный сбор и обработка данных автономию? Может ли децентрализация во внедренных технологиях влиять на этические и социальные параметры, такие, как социальная справедливость? Ведет ли децентрализация к  устойчивости функционирования систем? Есть ли взаимосвязь между децентрализацией цифровых технологий и децентрализацией городского развития?В статье делается вывод о том, что децентрализаванные системы имеют гораздо большую эффективность в современных условиях и являются альтернативой или естественной адаптацией к сложившимся условиям. Например, децентрализованное производство электроэнергии делает людей одновременно производителями и потребителями, что приводит к повышению энергоэффективности. Точно так же аналитика данных не является монополией систем больших данных. Анализ может также быть выполнен полностью децентрализованным способом как общественное благо с использованием коллективного разума

Appliance-Level Flexible Scheduling for Socio-Technical Smart Grid Optimization

Participation in residential energy demand response programs requires an active role by consumers. They contribute flexibility in how they use their appliances as the means to adjust energy consumption, and reduce demand peaks, possibly at the expense of their own comfort (e.g., thermal). Understanding the collective potential of appliance-level flexibility for reducing demand peaks is challenging and complex. For instance, physical characteristics of appliances, usage preferences, and comfort requirements all influence consumer flexibility, adoption, and effectiveness of demand response programs. To capture and study such socio-technical factors and trade-offs, this paper contributes a novel appliance-level flexible scheduling framework based on consumers' self-determined flexibility and comfort requirements. By utilizing this framework, this paper studies (i) consumers' usage preferences across various appliances, as well as their voluntary contribution of flexibility and willingness to sacrifice comfort for improving grid stability, (ii) impact of individual appliances on the collective goal of reducing demand peaks, and (iii) the effect of variable levels of flexibility, cooperation, and participation on the outcome of coordinated appliance scheduling. Experimental evaluation using a novel dataset collected via a smartphone app shows that higher consumer flexibility can significantly reduce demand peaks, with the oven having the highest system-wide potential for this. Overall, the cooperative approach allows for higher peak-shaving compared to non-cooperative schemes that focus entirely on the efficiency of individual appliances. The findings of this study can be used to design more cost-effective and granular (appliance-level) demand response programs in participatory and decentralized Smart Grids

Optimization of privacy-utility trade-offs under informational self-determination

The pervasiveness of Internet of Things results in vast volumes of personal data generated by smart devices of users (data producers) such as smart phones, wearables and other embedded sensors. It is a common requirement, especially for Big Data analytics systems, to transfer these large in scale and distributed data to centralized computational systems for analysis. Nevertheless, third parties that run and manage these systems (data consumers) do not always guarantee users’ privacy. Their primary interest is to improve utility that is usually a metric related to the performance, costs and the quality of service. There are several techniques that mask user-generated data to ensure privacy, e.g. differential privacy. Setting up a process for masking data, referred to in this paper as a ‘privacy setting’, decreases on the one hand the utility of data analytics, while, on the other hand, increases privacy. This paper studies parameterizations of privacy settings that regulate the trade-off between maximum utility, minimum privacy and minimum utility, maximum privacy, where utility refers to the accuracy in the estimations of aggregation functions. Privacy settings can be universally applied as system-wide parameterizations and policies (homogeneous data sharing). Nonetheless they can also be applied autonomously by each user or decided under the influence of (monetary) incentives (heterogeneous data sharing). This latter diversity in data sharing by informational self-determination plays a key role on the privacy-utility trajectories as shown in this paper both theoretically and empirically. A generic and novel computational framework is introduced for measuring privacy-utility trade-offs and their Pareto optimization. The framework computes a broad spectrum of such trade-offs that form privacy-utility trajectories under homogeneous and heterogeneous data sharing. The practical use of the framework is experimentally evaluated using real-world data from a Smart Grid pilot project in which energy consumers protect their privacy by regulating the quality of the shared power demand data, while utility companies make accurate estimations of the aggregate load in the network to manage the power grid. Over 20,000 differential privacy settings are applied to shape the computational trajectories that in turn provide a vast potential for data consumers and producers to participate in viable participatory data sharing systems