Increasingly, Tax Administrations (TAs) use new ICTs to be more effective and efficient in their management, and the digitalization process has accelerated exponentially in the current circumstances. Within this new technology, Artificial Intelligence (AI) presents multiple benefits for TAs, since it transforms data into a knowledge and impact asset for tax and customs management, and thus can achieve the intelligent use of such data and the way it interacts with taxpayers. The combination of AI, Internet of Things (IoT), Data Analysis and Data Analytics, will give exponential benefits through the collection and analysis of a large volume of taxpayer data in real time for better decision making that will positively impact several administrative areas of the TAs. In the collection function, AI is used to predict the collection, in customs at airports with facial recognition systems, among many other uses that will surely continue to be enhanced in the future. In this commentary, I would like to share some concrete examples of AI applied in audits or audits, both in massive or extensive controls and in intensive controls.

While artificial intelligence (AI)-based decision-making systems are increasingly popular, significant concerns on the potential discrimination during the AI decision-making process have been observed. For example, the distribution of predictions is usually biased and dependents on the sensitive attributes (e.g., gender and ethnicity). Numerous approaches have therefore been proposed to develop decision-making systems that are discrimination-conscious by-design, which are typically batch-based and require the simultaneous availability of all the training data for model learning. However, in the real-world, the data streams usually come on the fly which requires the model to process each input data once "on arrival" and without the need for storage and reprocessing. In addition, the data streams might also evolve over time, which further requires the model to be able to simultaneously adapt to non-stationary data distributions and time-evolving bias patterns, with an effective and robust trade-off between accuracy and fairness. In this paper, we propose a novel framework of online decision tree with fairness in the data stream with possible distribution drifting. Specifically, first, we propose two novel fairness splitting criteria that encode the data as well as possible, while simultaneously removing dependence on the sensitive attributes, and further adapts to non-stationary distribution with fine-grained control when needed. Second, we propose two fairness decision tree online growth algorithms that fulfills different online fair decision-making requirements. Our experiments show that our algorithms are able to deal with discrimination in massive and non-stationary streaming environments, with a better trade-off between fairness and predictive performance.

As the decisions made or influenced by machine learning models increasingly impact our lives, it is crucial to detect, understand, and mitigate unfairness. But even simply determining what "unfairness" should mean in a given context is nontrivial: there are many competing definitions, and choosing between them often requires a deep understanding of the underlying task. It is thus tempting to use model explainability to gain insights into model fairness, however existing explainability tools do not reliably indicate whether a model is indeed fair. In this work we present a new approach to explaining fairness in machine learning, based on the Shapley value paradigm. Our fairness explanations attribute a model's overall unfairness to individual input features, even in cases where the model does not operate on sensitive attributes directly. Moreover, motivated by the linearity of Shapley explainability, we propose a meta algorithm for applying existing trainingtime fairness interventions, wherein one trains a perturbation to the original model, rather than a new model entirely. By explaining the original model, the perturbation, and the fair-corrected model, we gain insight into the accuracy-fairness tradeoff that is being made by the intervention. We further show that this meta algorithm enjoys both flexibility and stability benefits with no loss in performance. Machine learning has repeatedly demonstrated astonishing predictive power due to its capacity to learn complex relationships from data.

The consensus problem is a fundamental problem in multi-agent systems which requires a group of processes (or agents) to reliably and timely agree on a single data value. Although extensively discussed in the context of distributed computing it's not exclusive to this field, also being present in our society in a variety of situations such as in democratic elections, the legislative process, jury trial proceedings, and so forth. It's solved through the employment of a consensus protocol governing how processes (agents) interact with one another. It may seem redundant but, to solve the consensus problem, first all processes agree to follow the same consensus protocol. Some of these processes may fail or be unreliable in other ways (such as in a conflict of interest situation) so consensus protocols must be fault tolerant or resilient.

Technology giants such as Alibaba and IBM are eating startup innovators' lunch. These behemoths are seeking to devour even more market share by publishing patents at unprecedented speed in emerging technologies such as blockchain. As some of the richest companies on the planet, the corporations have the resources to manage the laborious search of existing patents and to overcome the outdated administrative hurdles so that they can file for intellectual property rights. Patents are definitely old school. Patent laws started with the rise of the nation-state, so they began in the 18th century and were then fully developed in the 19th century.

New technology is a double-edged sword in the fight against modern slavery and human trafficking. Digital solutions are fuelling ever more complex criminal schemes whilst also providing authorities and organisations with the means to tackle human exploitation more effectively. In this webinar, we explore how data analytics, artificial intelligence and blockchain are helping detect and prevent forced labour. Our speakers bring to the table diverse experience tracing modern slavery across the public, private and research sectors.

Atmospheric modelling has recently experienced a surge with the advent of deep learning. Most of these models, however, predict concentrations of pollutants following a data-driven approach in which the physical laws that govern their behaviors and relationships remain hidden. With the aid of real-world air quality data collected hourly in different stations throughout Madrid, we present a case study using a series of data-driven techniques with the following goals: (1) Find systems of ordinary differential equations that model the concentration of pollutants and their changes over time; (2) assess the performance and limitations of our models using stability analysis; (3) reconstruct the time series of chemical pollutants not measured in certain stations using delay coordinate embedding results.

We propose a novel framework for non-parametric policy evaluation in static and dynamic settings. Under the assumption of selection on observables, we consider treatment effects of the population, of sub-populations, and of alternative populations that may have alternative covariate distributions. We further consider the decomposition of a total effect into a direct effect and an indirect effect (as mediated by a particular mechanism). Under the assumption of sequential selection on observables, we consider the effects of sequences of treatments. Across settings, we allow for treatments that may be discrete, continuous, or even text. Across settings, we allow for estimation of not only counterfactual mean outcomes but also counterfactual distributions of outcomes. We unify analyses across settings by showing that all of these causal learning problems reduce to the re-weighting of a prediction, i.e. causal adjustment. We implement the re-weighting as an inner product in a function space called a reproducing kernel Hilbert space (RKHS), with a closed form solution that can be computed in one line of code. We prove uniform consistency and provide finite sample rates of convergence. We evaluate our estimators in simulations devised by other authors. We use our new estimators to evaluate continuous and heterogeneous treatment effects of the US Jobs Corps training program for disadvantaged youth.

This paper investigates the dependence of existing state-of-the-art person re-identification models on the presence and visibility of human faces. We apply a face detection and blurring algorithm to create anonymized versions of several popular person re-identification datasets including Market1501, DukeMTMC-reID, CUHK03, Viper, and Airport. Using a cross-section of existing state-of-the-art models that range in accuracy and computational efficiency, we evaluate the effect of this anonymization on re-identification performance using standard metrics. Perhaps surprisingly, the effect on mAP is very small, and accuracy is recovered by simply training on the anonymized versions of the data rather than the original data. These findings are consistent across multiple models and datasets. These results indicate that datasets can be safely anonymized by blurring faces without significantly impacting the performance of person reidentification systems, and may allow for the release of new richer re-identification datasets where previously there were privacy or data protection concerns.

The European Commission has proposed strictly regulating AI systems that meet two conditions: they are used in sectors and in a manner where significant risks are likely to occur. But Germany has called on the EU to abandon its proposal, arguing that tougher rules should apply for all sectors that use AI and even for AI applications that do not pose a significant risk. This is not the first time that Germany has called for stricter regulation of AI, but as Germany has taken over the EU Council presidency, its perspective is likely to have more influence on the Commission's regulatory choices. But following Germany's advice would have far-reaching negative implications for innovation in the EU. First, imposing stricter rules on lower-risk AI systems would achieve little in the way of consumer protection because these systems already pose little risk to consumers and existing consumer protection laws apply. It does not make sense to require AI-powered dating apps to undergo the same level of scrutiny as credit scoring tools.