Tournesol: Permissionless Collaborative Algorithmic Governance with Security Guarantees

Recommendation algorithms play an increasingly central role in our societies. However, thus far, these algorithms are mostly designed and parameterized unilaterally by private groups or governmental authorities. In this paper, we present an end-to-end permissionless collaborative algorithmic governance method with security guarantees. Our proposed method is deployed as part of an open-source content recommendation platform https://tournesol.app, whose recommender is collaboratively parameterized by a community of (non-technical) contributors. This algorithmic governance is achieved through three main steps. First, the platform contains a mechanism to assign voting rights to the contributors. Second, the platform uses a comparison-based model to evaluate the individual preferences of contributors. Third, the platform aggregates the judgements of all contributors into collective scores for content recommendations. We stress that the first and third steps are vulnerable to attacks from malicious contributors. To guarantee the resilience against fake accounts, the first step combines email authentication, a vouching mechanism, a novel variant of the reputation-based EigenTrust algorithm and an adaptive voting rights assignment for alternatives that are scored by too many untrusted accounts. To provide resilience against malicious authenticated contributors, we adapt Mehestan, an algorithm previously proposed for robust sparse voting. We believe that these algorithms provide an appealing foundation for a collaborative, effective, scalable, fair, contributor-friendly, interpretable and secure governance. We conclude by highlighting key challenges to make our solution applicable to larger-scale settings.Comment: 31 pages, 5 figure

Model based collaborative design & optimization of blended wing body aircraft configuration: AGILE EU project

Novel configuration design choices may help achieve revolutionary goals for reducing fuel burn, emission and noise, set by Flightpath 2050. One such advance configuration is a blended wing body. Due to multi-diciplinary nature of the configuration, several partners with disciplinary expertise collaborate in a Model driven ‘AGILE MDAO framework’ to design and evaluate the novel configuration. The objective of this research are : - To create and test a model based collaborative framework using AGILE Paradigm for novel configuration design & optimization, involving large multinational team. Reduce setup time for complex MDO problem. - Through Multi fidelity design space exploration, evaluate aerodynamic performance - The BWB disciplinary analysis models such as aerodynamics, propulsion, onboard systems, S&C were integrated and intermediate results are published in this report

Streamlining Cross-Organizational Aircraft Development: Results from the AGILE Project

The research and innovation AGILE project developed the next generation of aircraft Multidisciplinary Design and Optimization processes, which target significant reductions in aircraft development costs and time to market, leading to more cost-effective and greener aircraft solutions. The high level objective is the reduction of the lead time of 40% with respect to the current state-of-the-art. 19 industry, research and academia partners from Europe, Canada and Russia developed solutions to cope with the challenges of collaborative design and optimization of complex products. In order to accelerate the deployment of large-scale, collaborative multidisciplinary design and optimization (MDO), a novel methodology, the so-called AGILE Paradigm, has been developed. Furthermore, the AGILE project has developed and released a set of open technologies enabling the implementation of the AGILE Paradigm approach. The collection of all the technologies constitutes AGILE Framework, which has been deployed for the design and the optimization of multiple aircraft configurations. This paper focuses on the application of the AGILE Paradigm on seven novel aircraft configurations, proving the achievement of the project’s objectives

Aircraft Geometry and Meshing with Common Language Schema CPACS for Variable-Fidelity MDO Applications

This paper discusses multi-fidelity aircraft geometry modeling and meshing with the common language schema CPACS. The CPACS interfaces are described, and examples of variable fidelity aerodynamic analysis results applied to the reference aircraft are presented. Finally, we discuss three control surface deflection models for Euler computation

Integration aspects of the collaborative aero‑structural design of an unmanned aerial vehicle

Overall aircraft design is a complex multidisciplinary process, which requires knowledge from many different fields such as structures, aerodynamics, systems and propulsion. For unconventional configurations lacking an empirical knowledge base, higher fidelity physics-based methods are required to reliably estimate the feasibility of a given new design concept. Analysis tools and results are provided by highly specialized groups of experts, possibly from different organizations. In the AGILE (aircraft 3rd generation MDO for innovative collaboration of heterogeneous teams of experts) project, new approaches to setting up cross-organizational collaborative aircraft design optimization workflows have been investigated, including the employment of common parametric aircraft configuration schema as a central common data schema and the provision of disciplinary analysis competences as callable services. Following this paradigm, the present paper details a distributed workflow to perform an aero-structural design optimization of an unmanned aerial vehicle (UAV) design. Taking advantage of disciplinary capabilities provided by several partners based in various locations across Europe, an integrated design workflow including a distributed and tightly coupled aero-structural analysis loop has been assembled using the process integration and design optimization system remote component environment developed at the German Aerospace Center. To enable the necessary load and displacement transfer between non-matching disciplinary meshes, a versatile and lightweight algorithm using radial basis functions has furthermore been implemented. The functionality of the workflow is demonstrated by performing the optimization on the baseline configuration of the UAV

Integration Aspects of the Collaborative Aero-Structural Design of an Unmanned Aerial Vehicle

Overall aircraft design is a complex multidisciplinary process, which requires knowledge from many different fields such as structures, aerodynamics, systems and propulsion. For unconventional configurations lacking an empirical knowledge base, higher fidelity physics-based methods provided by highly specialized Groups of experts are required to reliably estimate the feasibility of a new design concept. Following this premise, a collaborative workflow to perform an aero-structural design optimization of an unmanned aerial vehicle (UAV) design has been established as part of the AGILE project [1]. The disciplinary competences are provided by several partners based in various locations across Europe. Whereas the structural expertise is provided by Airbus Defence and Space, the CFD competence is provided by Airinnova and CFS Engineering, who also perform the analysis. The missing interfaces between the individual partners, as well as the overall integration are handled by the German Aerospace Center (DLR) Institute of System Architectures in Aeronautics, which also contributes the Common Parametric Aircraft Configuration Schema (CPACS) [2, 3], the common source data structure for the model generation. This paper details the integration tasks performed at DLR. An algorithm for load and displacement transfer between non-matching meshes has been implemented using radial basis functions [4, 5]. Furthermore, an executable workflow has been assembled using the Remote Component Environment (RCE) [6, 7] developed at DLR, which allows each partner to expose their competence as a callable service. The functionality of the workflow is demonstrated by performing the optimization on a set of design variations of the baseline configuration of the UAV

Tournesol: A quest for a large, secure and trustworthy database of reliable human judgments

Today's large-scale algorithms have become immensely influential, as they recommend and moderate the content that billions of humans are exposed to on a daily basis. These algorithms are the de-facto regulators of the information diet of billions of humans, from shaping opinions on public health information to organizing groups for social movements. This creates serious concerns, but also great opportunities to promote quality information [Hoa20, HFE21]. Addressing the concerns and seizing the opportunities is a challenging, enormous and fabulous endeavor [HE19], as intuitively appealing ideas often come with unforeseen unwanted side effects [EMH21], and as it requires us to think about what we truly and deeply prefer [Soa15]. To make progress, it is critical to understand how today's large-scale algorithms are built, and to determine what interventions will be most effective. Given that these algorithms rely heavily on machine learning, we make the following key observation: any algorithm trained on uncontrolled data must not be trusted. Indeed, a malicious entity could take control over the data, poison it with dangerously misleading or manipulative fabricated inputs, and thereby make the trained algorithm extremely unsafe. We thus argue that the first step towards safe and ethical large-scale algorithms must be the collection of a large, secure and trustworthy dataset of reliable human judgments. To achieve this, we introduce Tournesol, an open source platform available at https: //tournesol.app. Tournesol aims to collect a large database of human judgments on what algorithms ought to widely recommend (and what algorithms ought to stop widely recommending). In this paper, we outline the structure of the Tournesol database, the key features of the Tournesol platform and the main hurdles that must be overcome to make it a successful project. Most importantly, we argue that, if successful, Tournesol may then serve as the essential foundation for any safe and ethical large-scale algorithm