13 research outputs found
Multi-Objective Optimization for Value-Sensitive and Sustainable Basket Recommendations
Sustainable consumption aims to minimize the environmental and societal
impact of the use of services and products. Over-consumption of services and
products leads to potential natural resource exhaustion and societal
inequalities as access to goods and services becomes more challenging. In
everyday life, a person can simply achieve more sustainable purchases by
drastically changing their lifestyle choices and potentially going against
their personal values or wishes. Conversely, achieving sustainable consumption
while accounting for personal values is a more complex task as potential
trade-offs arise when trying to satisfy environmental and personal goals. This
article focuses on value-sensitive design of recommender systems, which enable
consumers to improve the sustainability of their purchases while respecting
personal and societal values. Value-sensitive recommendations for sustainable
consumption are formalized as a multi-objective optimization problem, where
each objective represents different sustainability goals and personal values.
Novel and existing multi-objective algorithms calculate solutions to this
problem. The solutions are proposed as personalized sustainable basket
recommendations to consumers. These recommendations are evaluated on a
synthetic dataset, which comprises three established real-world datasets from
relevant scientific and organizational reports. The synthetic dataset contains
quantitative data on product prices, nutritional values, and environmental
impact metrics, such as greenhouse gas emissions and water footprint. The
recommended baskets are highly similar to consumer purchased baskets and
aligned with both sustainability goals and personal values relevant to health,
expenditure, and taste. Even when consumers would accept only a fraction of
recommendations, a considerable reduction of environmental impact is observed.Comment: Second Draft, merged appendix to main text, stressed the importance
of straight-through estimators for fractional decoupling, updated
nomenclature and reference
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
Near-optimal control of dynamical systems with neural ordinary differential equations
Optimal control problems naturally arise in many scientific applications
where one wishes to steer a dynamical system from a certain initial state
to a desired target state in finite time .
Recent advances in deep learning and neural network-based optimization have
contributed to the development of methods that can help solve control problems
involving high-dimensional dynamical systems. In particular, the framework of
neural ordinary differential equations (neural ODEs) provides an efficient
means to iteratively approximate continuous time control functions associated
with analytically intractable and computationally demanding control tasks.
Although neural ODE controllers have shown great potential in solving complex
control problems, the understanding of the effects of hyperparameters such as
network structure and optimizers on learning performance is still very limited.
Our work aims at addressing some of these knowledge gaps to conduct efficient
hyperparameter optimization. To this end, we first analyze how truncated and
non-truncated backpropagation through time affect runtime performance and the
ability of neural networks to learn optimal control functions. Using analytical
and numerical methods, we then study the role of parameter initializations,
optimizers, and neural-network architecture. Finally, we connect our results to
the ability of neural ODE controllers to implicitly regularize control energy.Comment: 27 pages, 17 figure
Decentralized Collective Learning for Self-managed Sharing Economies
The Internet of Things equips citizens with a phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This article contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic tradeoffs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies
Implicit energy regularization of neural ordinary-differential-equation control
Although optimal control problems of dynamical systems can be formulated
within the framework of variational calculus, their solution for complex
systems is often analytically and computationally intractable. In this Letter
we present a versatile neural ordinary-differential-equation control (NODEC)
framework with implicit energy regularization and use it to obtain
neural-network-generated control signals that can steer dynamical systems
towards a desired target state within a predefined amount of time. We
demonstrate the ability of NODEC to learn control signals that closely resemble
those found by corresponding optimal control frameworks in terms of control
energy and deviation from the desired target state. Our results suggest that
NODEC is capable to solve a wide range of control and optimization problems,
including those that are analytically intractable.Comment: 5 pages, 3 figure
Neural Ordinary Differential Equation Control of Dynamics on Graphs
We study the ability of neural networks to calculate feedback control signals
that steer trajectories of continuous time non-linear dynamical systems on
graphs, which we represent with neural ordinary differential equations (neural
ODEs). To do so, we present a neural-ODE control (NODEC) framework and find
that it can learn feedback control signals that drive graph dynamical systems
into desired target states. While we use loss functions that do not constrain
the control energy, our results show, in accordance with related work, that
NODEC produces low energy control signals. Finally, we evaluate the performance
and versatility of NODEC against well-known feedback controllers and deep
reinforcement learning. We use NODEC to generate feedback controls for systems
of more than one thousand coupled, non-linear ODEs that represent epidemic
processes and coupled oscillators.Comment: Fifth version improves and clears notatio
How value-sensitive design can empower sustainable consumption
In a so-called overpopulated world, sustainable consumption is of existential importance. However, the expanding spectrum of product choices and their production complexity challenge consumers to make informed and value-sensitive decisions. Recent approaches based on (personalized) psychological manipulation are often intransparent, potentially privacy-invasive and inconsistent with (informational) self-determination. By contrast, responsible consumption based on informed choices currently requires reasoning to an extent that tends to overwhelm human cognitive capacity. As a result, a collective shift towards sustainable consumption remains a grand challenge. Here, we demonstrate a novel personal shopping assistant implemented as a smart phone app that supports a value-sensitive design and leverages sustainability awareness, using experts’ knowledge and ‘wisdom of the crowd’ for transparent product information and explainable product ratings. Real-world field experiments in two supermarkets confirm higher sustainability awareness and a bottom-up behavioural shift towards more sustainable consumption. These results encourage novel business models for retailers and producers, ethically aligned with consumer preferences and with higher sustainability
Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping
Big data collection practices using Internet of Things (IoT) pervasive
technologies are often privacy-intrusive and result in surveillance, profiling,
and discriminatory actions over citizens that in turn undermine the
participation of citizens to the development of sustainable smart cities.
Nevertheless, real-time data analytics and aggregate information from IoT
devices open up tremendous opportunities for managing smart city
infrastructures. The privacy-enhancing aggregation of distributed sensor data,
such as residential energy consumption or traffic information, is the research
focus of this paper. Citizens have the option to choose their privacy level by
reducing the quality of the shared data at a cost of a lower accuracy in data
analytics services. A baseline scenario is considered in which IoT sensor data
are shared directly with an untrustworthy central aggregator. A grouping
mechanism is introduced that improves privacy by sharing data aggregated first
at a group level compared as opposed to sharing data directly to the central
aggregator. Group-level aggregation obfuscates sensor data of individuals, in a
similar fashion as differential privacy and homomorphic encryption schemes,
thus inference of privacy-sensitive information from single sensors becomes
computationally harder compared to the baseline scenario. The proposed system
is evaluated using real-world data from two smart city pilot projects. Privacy
under grouping increases, while preserving the accuracy of the baseline
scenario. Intra-group influences of privacy by one group member on the other
ones are measured and fairness on privacy is found to be maximized between
group members with similar privacy choices. Several grouping strategies are
compared. Grouping by proximity of privacy choices provides the highest privacy
gains. The implications of the strategy on the design of incentives mechanisms
are discussed
Supporting Sustainable Decision-Making with Value-Sensitive Artificial Intelligence
Sustainability is a term that is becoming increasingly prevalent, as several recent catastrophic events are often attributed to the impact that modern lifestyle has on the environment. Moreover, the term extends also to sustainability of operational democratic societies, where availability and accessibility to several critical infrastructures is ensured for individuals. Yet, achieving sustainability can be challenging. Recent reports from the United Nations conclude that improving decision-making processes on several levels, from institutional level policy making to individual level everyday decisions, supports sustainable development. Typically, deciding on more "sustainable" solutions often leads to complex multi-objective optimization problems, which are not trivial to solve. Modern artificial intelligence (AI) provides several methods to handle complex problems, particularly within the fields of machine learning and optimization. Nevertheless, AI has also given rise to new challenges, especially related to privacy, autonomy, and personal values and morals. Thus, the need for value-sensitive AI systems arises, where values and preferences are included directly in the system design. The current thesis pursues the paradigm of efficient value-sensitive AI, mainly focusing on sustainable decision-making problems, by providing experimental, empirical, and methodological arguments. Three main design approaches are considered: centralized, decentralized, and a hybrid combination of both. First, an AI system applies centralized controls to simulations of critical infrastructure components, such as power grids. The results show the ability of said AI controls to stabilize and sustain the operation of critical infrastructures. Yet, centralized approaches may restrict and suppress personal freedoms and autonomy, especially when applied on individuals. Thus, a decentralized approach is evaluated next in the domain of sustainable product recommendations. The proposed AI system receives explicit input from individuals regarding their morals and values, and then calculates personalized ratings for sustainable products. Interactions between the decentralized system and individuals are evaluated in a field-study on two grocery stores. Statistically significant results confirm the system's ability to support individuals towards more sustainable purchases. Finally, hybrid combinations of centralized and decentralized approaches are evaluated. A novel privacy-preserving framework is proposed to calculate accurate aggregations of individual data without exposing the actual individual data to centralized systems. Additionally, a novel hybrid AI system is introduced and combined with the privacy-preserving framework to generate sustainable basket recommendations based on personal values and environmental objectives. Quantitative results on a synthetic dataset show a considerable reduction of environmental impact, even when users adopt only a fraction of the recommendations