The Advancing Data Justice project is a Global Partnership on AI (GPAI) initiative, led by The Alan Turing Institute. Researchers from the Institute have been collaborating with twelve Policy Pilot Partner organisations from Asia, Oceania, Africa, and South America, and each of these have been working to understand what data justice might look like in their distinct contexts. The aims of the project are 1) to gain a better understanding of the current state of research in the field to better inform future research directions, and 2) to create a guide for policymakers, developers, and communities affected by AI, comprising advice on what they should consider in their practice, use and experience of AI systems. As part of the project, the team have recently launched the first instalment of a documentary series which tracks the work of the project partners. They discuss how data-driven technologies can be deployed in a way which is compatible with values of social justice.

We frequently hear in the 21st century that data is the new oil. Those who controlled oil flows in the 1970s had a near stranglehold on the global economy. Today, those who hold data might well control the new economy. Data, however, is diffuse, hard to track and nearly impossible to regulate, which could have unparalleled implications for human rights and religious freedom. Big data companies have poured billions into research to bring technology and data into direct contact with us every day through artificial intelligence.

RAMSTEIN AIR BASE, Germany – The United States marshaled 40 allies on Tuesday to furnish Ukraine with long-term military aid in what could become a protracted battle against the Russian invasion, and Germany said it would send dozens of armored anti-aircraft vehicles. It was a major policy shift for a country that had wavered over fear of provoking Russia. The announcement by Germany, Europe's biggest economy and one of Russia's most important Western trading partners, was among many signals on Tuesday pointing to further escalation in the war and disappointment for diplomacy. Germany's shift on weapons also was seen as a strong affirmation of a toughened message by the administration of U.S. President Joe Biden, which has said it wants to see Russia not only defeated in Ukraine but seriously weakened from the conflict that Russian President Vladimir Putin began two months ago. The increasing flow of Western weapons into Ukraine -- including howitzers, armed drones, tanks and ammunition -- also amounted to another sign that a war Putin had expected would divide his Western adversaries had instead drawn them much closer together.

Hypernymy plays a fundamental role in many AI tasks like taxonomy learning, ontology learning, etc. This has motivated the development of many automatic identification methods for extracting this relation, most of which rely on word distribution. We present a novel model HyperBox to learn box embeddings for hypernym discovery. Given an input term, HyperBox retrieves its suitable hypernym from a target corpus. For this task, we use the dataset published for SemEval 2018 Shared Task on Hypernym Discovery. We compare the performance of our model on two specific domains of knowledge: medical and music. Experimentally, we show that our model outperforms existing methods on the majority of the evaluation metrics. Moreover, our model generalize well over unseen hypernymy pairs using only a small set of training data.

We propose a novel federated learning paradigm to model data variability among heterogeneous clients in multi-centric studies. Our method is expressed through a hierarchical Bayesian latent variable model, where client-specific parameters are assumed to be realization from a global distribution at the master level, which is in turn estimated to account for data bias and variability across clients. We show that our framework can be effectively optimized through expectation maximization (EM) over latent master's distribution and clients' parameters. We also introduce formal differential privacy (DP) guarantees compatibly with our EM optimization scheme. We tested our method on the analysis of multi-modal medical imaging data and clinical scores from distributed clinical datasets of patients affected by Alzheimer's disease. We demonstrate that our method is robust when data is distributed either in iid and non-iid manners, even when local parameters perturbation is included to provide DP guarantees. Moreover, the variability of data, views and centers can be quantified in an interpretable manner, while guaranteeing high-quality data reconstruction as compared to state-of-the-art autoencoding models and federated learning schemes.

Acoustic scene classification is a process of characterizing and classifying the environments from sound recordings. The first step is to generate features (representations) from the recorded sound and then classify the background environments. However, different kinds of representations have dramatic effects on the accuracy of the classification. In this paper, we explored the three such representations on classification accuracy using neural networks. We investigated the spectrograms, MFCCs, and embeddings representations using different CNN networks and autoencoders. Our dataset consists of sounds from three settings of indoors and outdoors environments - thus the dataset contains sound from six different kinds of environments. We found that the spectrogram representation has the highest classification accuracy while MFCC has the lowest classification accuracy. We reported our findings, insights as well as some guidelines to achieve better accuracy for environment classification using sounds.

We propose a novel methodology for estimating the epidemiological parameters of a modified SIRD model (acronym of Susceptible, Infected, Recovered and Deceased individuals) and perform a short-term forecast of SARS-CoV-2 virus spread. We mainly focus on forecasting number of deceased. The procedure was tested on reported data for Poland. For some short-time intervals we performed numerical test investigating stability of parameter estimates in the proposed approach. Numerical experiments confirm the effectiveness of short-term forecasts (up to 2 weeks) and stability of the method. To improve their performance (i.e.

Conventional vehicles are certified through classical approaches, where different physical certification tests are set up on test tracks to assess required safety levels. These approaches are well suited for vehicles with limited complexity and limited interactions with other entities as last-second resources. However, these approaches do not allow to evaluate safety with real behaviors for critical and edge cases, nor to evaluate the ability to anticipate them in the mid or long term. This is particularly relevant for automated and autonomous driving functions that make use of advanced predictive systems to anticipate future actions and motions to be considered in the path planning layer. In this paper, we present and analyze the results of physical tests on proving grounds of several predictive systems in automated driving functions developed within the framework of the BRAVE project. Based on our experience in testing predictive automated driving functions, we identify the main limitations of current physical testing approaches when dealing with predictive systems, analyze the main challenges ahead, and provide a set of practical actions and recommendations to consider in future physical testing procedures for automated and autonomous driving functions.

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25, 256 MRI scans from 6, 314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing.

Current Instance Transfer Learning (ITL) methodologies use domain adaptation and sub-space transformation to achieve successful transfer learning. However, these methodologies, in their processes, sometimes overfit on the target dataset or suffer from negative transfer if the test dataset has a high variance. Boosting methodologies have been shown to reduce the risk of overfitting by iteratively re-weighing instances with high-residual. However, this balance is usually achieved with parameter optimization, as well as reducing the skewness in weights produced due to the size of the source dataset. While the former can be achieved, the latter is more challenging and can lead to negative transfer. We introduce a simpler and more robust fix to this problem by building upon the popular boosting ITL regression methodology, two-stage TrAdaBoost.R2. Our methodology,~\us{}, is a boosting and random-forest based ensemble methodology that utilizes importance sampling to reduce the skewness due to the source dataset. We show that~\us{}~performs better than competitive transfer learning methodologies $63\%$ of the time. It also displays consistency in its performance over diverse datasets with varying complexities, as opposed to the sporadic results observed for other transfer learning methodologies.