Despite efforts to mitigate the inherent risks and biases of artificial intelligence (AI) algorithms, these algorithms can disproportionately impact culturally marginalized groups. A range of approaches has been proposed to address or reduce these risks, including the development of ethical guidelines and principles for responsible AI, as well as technical solutions that promote algorithmic fairness. Drawing on design justice, expansive learning theory, and recent empirical work on participatory AI, we argue that mitigating these harms requires a fundamental re-architecture of the AI production pipeline. This re-design should center co-production, diversity, equity, inclusion (DEI), and multidisciplinary collaboration. We introduce an augmented AI lifecycle consisting of five interconnected phases: co-framing, co-design, co-implementation, co-deployment, and co-maintenance. The lifecycle is informed by four multidisciplinary workshops and grounded in themes of distributed authority and iterative knowledge exchange. Finally, we relate the proposed lifecycle to several leading ethical frameworks and outline key research questions that remain for scaling participatory governance.

Self-supervised representation learning for human action recognition has developed rapidly in recent years. Most of the existing works are based on skeleton data while using a multi-modality setup. These works overlooked the differences in performance among modalities, which led to the propagation of erroneous knowledge between modalities while only three fundamental modalities, i.e., joints, bones, and motions are used, hence no additional modalities are explored. In this work, we first propose an Implicit Knowledge Exchange Module (IKEM) which alleviates the propagation of erroneous knowledge between low-performance modalities. Then, we further propose three new modalities to enrich the complementary information between modalities. Finally, to maintain efficiency when introducing new modalities, we propose a novel teacher-student framework to distill the knowledge from the secondary modalities into the mandatory modalities considering the relationship constrained by anchors, positives, and negatives, named relational cross-modality knowledge distillation. The experimental results demonstrate the effectiveness of our approach, unlocking the efficient use of skeleton-based multi-modality data. Source code will be made publicly available at https://github.com/desehuileng0o0/IKEM.

Firm clusters are seen as having a positive effect on innovations, what can be interpreted as economies of scale or knowledge spillovers. The processes underlying the success of these clusters remain difficult to isolate. We propose in this paper a stylised agent-based model to test the role of geographical proximity and informal knowledge exchanges between firms on the emergence of innovations. The model is run on synthetic firm clusters. Sensitivity analysis and systematic model exploration unveil a strong impact of interaction distance on innovations, with a qualitative shift when spatial interactions are more intense. Model bi-objective optimisation shows a compromise between innovation and product diversity, suggesting trade-offs for clusters in practice. This model provides thus a first basis to systematically explore the interplay between firm cluster geography and innovation, from an evolutionary perspective.

We investigate conjunctive query inseparability of description logic (DL) knowledge bases (KBs) with respect to a given signature, a fundamental problem for KB versioning, module extraction, forgetting and knowledge exchange. We study the data and combined complexity of deciding KB query inseparability for fragments of Horn-ALCHI, including the DLs underpinning OWL 2 QL and OWL 2 EL. While all of these DLs are P-complete for data complexity, the combined complexity ranges from P to EXPTIME and 2EXPTIME. We also resolve two major open problems for OWL 2 QL by showing that TBox query inseparability and the membership problem for universal UCQ-solutions in knowledge exchange are both EXPTIME-complete for combined complexity.

New technologies and data sources have tremendous potential to improve statistical production. They offer a way to generate statistics in a more timely, accurate and cost-efficient manner. Yet, keeping up with the pace of change is challenging, especially for National Statistical Organisations (NSOs) that must innovate with care to maintain a "gold standard" in their outputs. International cooperation between NSOs and other official statistical bodies is one way to help accelerate change in a responsible way. In 2021, the Office for National Statistics (ONS) and the United Nations Economic Commission for Europe (UNECE) Machine Learning Group (ML 2021) demonstrated the benefits of international cooperation for technological advance.

It won't be in the Olympics anytime soon but Oki Mek considers artificial intelligence "a team sport." As the chief artificial intelligence officer for the Department of Health and Human Services, Mek may be a little biased, but as his agency works through its AI strategy -- released in January -- collaboration and knowledge exchange will be paramount. The strategy aims to promote AI adoption, and to ensure that algorithms are fair, legal and ethical. Three core pieces of the strategy are adoption and bringing the entire department up to speed on the language of AI; scaling best practices, and accelerated adoption. As for the first piece, Mek said culture change plays a pivotal role. "The main risks here is not AI itself, it's not the technology itself, it's more of a culture shift.

Not only does this provide useful information to users in the moment, but it has also helped raise awareness and increase the adoption of Lexikon. Since launching the Lexikon Slack Bot, we've seen a sustained 25% increase in the number of Lexikon links shared on Slack per week. You just listened to a track by a new artist on your Discover Weekly and you're hooked. You want to hear more and learn about the artist. So, you go to the artist page on Spotify where you can check out the most popular tracks across different albums, read an artist bio, check out playlists where people tend to discover the artist, and explore similar artists.

This paper develops the concept of knowledge and its exchange using Semantic Web technologies. It points out that knowledge is more than information because it embodies the meaning, that is to say semantic and context. These characteristics will influence our approach to represent and to treat the knowledge. In order to be adopted, the developed system needs to be simple and to use standards. The goal of the paper is to find standards to model knowledge and exchange it with an other person. Therefore, we propose to model knowledge using UML models to show a graphical representation and to exchange it with XML to ensure the portability at low cost. We introduce the concept of ontology for organizing knowledge and for facilitating the knowledge exchange. Proposals have been tested by implementing an application on the design knowledge of a pen.

We investigate knowledge exchange among commercial organi sations, the rationale behind it and its effects on the marke t. Knowledge exchange is known to be beneficial for industry, bu t in order to explain it, authors have used high level concept s like network effects, reputation and trust. We attempt to formal ise a plausible and elegant explanation of how and why compan ies adopt information exchange and why it benefits the market as a whole when this happens. This explanation is based on a multi - agent model that simulates a market of software providers. E ven though the model does not include any high-level concept s, information exchange naturally emerges during simulation s as a successful profitable behaviour. The conclusions reac hed by this agent-based analysis are twofold: (1) A straightforward se t of assumptions is enough to give rise to exchange in a softwa re market. This work was carried out when M. Chli and P . The growth of the Internet as a medium of knowledge exchange has stimulated a lot of scientific interest origina ting from various disciplines. The willingness of individua ls, organisations as well as commercial firms to share information via the Internet has been remarkable. In some sectors like scientific research, the communication of newly acquir ed knowledge and expertise in a field is considered vital for the ir advancement. On the other hand, in other sectors, the benefit s of such exchanges may not be obvious. For instance, it might even be considered damaging for pharmaceutical companies t o make public any innovations generated by their Research and Development (R&D) process. In spite of this view, exchange o f intellectual property in some industries occurs quite freq uently and in various different ways. These include the forming of strategic partnerships, the participation in open source s oftware projects and the publication of scientific papers by researc h labs that are part of commercial companies. W e study the knowledge exchange that occurs in the software industry. In particular, we focus on analysing the rationale behind this exchange as well as its effect on the industry. The complexity of software requirements is a char - acteristic that distinguishes the software market from oth ers.