This list of readings about journalism and AI is based on research for the Polis report on AI and Journalism published in November 2018. We will update this list and welcome suggestions for further readings to: c.h.beckett@lse.ac.uk What is machine learning and why should I care? AI is going to save journalism – here's how Is AI and journalism a good mix? First in the world: Yle's smart news assistant Voitto ensures that you don't miss the news you want to read Can science writing be automated?

At the G7 meeting in Montreal last year, Justin Trudeau told WIRED he would look into why more than 100 African artificial intelligence researchers had been barred from visiting that city to attend their field's most important annual event, the Neural Information Processing Systems conference, or NeurIPS. Now the same thing has happened again. More than a dozen AI researchers from African countries have been refused visas to attend this year's NeurIPS, to be held next month in Vancouver. This means an event that shapes the course of a technology with huge economic and social importance will have little input from a major portion of the world. The conference brings together thousands of researchers from top academic institutions and companies, for hundreds of talks, workshops, and side meetings at which new ideas and theories are hashed out. Tẹjúmádé Àfọ njá, a master's student from Nigeria who is studying at Saarland University in Germany, posted her rejection letter to Twitter.

US Defense Secretary Mark Esper warned during a speech on artificial intelligence at the National Security Commission on Artificial Intelligence public conference Tuesday (Nov. "Beijing has made it abundantly clear that it intends to be the world leader in AI by 2030," Esper said. "While the US faces a mighty task in transitioning the world's most advanced military to new AI-enabled systems, China believes it can leapfrog our current technology and go straight to the next generation." Middle East countries banned from purchasing advanced US drones due to a weapons embargo are increasingly gravitating towards Chinese defense manufacturers. The drone sales are supporting China's expansion across the Middle East, which is home to many strategic US military bases, as well as, future and current routes for Beijing's Belt and Road Initiative.

Microsoft and Warner Bros. have collaborated to successfully store and retrieve the entire 1978 iconic "Superman" movie on a piece of glass roughly the size of a drink coaster, 75 by 75 by 2 millimeters thick. It was the first proof of concept test for Project Silica, a Microsoft Research project that uses recent discoveries in ultrafast laser optics and artificial intelligence to store data in quartz glass. Machine learning algorithms read the data back by decoding images and patterns that are created as polarized light shines through the glass. The hard silica glass can withstand being boiled in hot water, baked in an oven, microwaved, flooded, scoured, demagnetized and other environmental threats that can destroy priceless historic archives or cultural treasures if things go wrong. It represents an investment by Microsoft Azure to develop storage technologies built specifically for cloud computing patterns, rather than relying on storage media designed to work in computers or other scenarios.

An increasing number of works in natural language processing have addressed the effect of bias on the predicted outcomes, introducing mitigation techniques that act on different parts of the standard NLP pipeline (data and models). However, these works have been conducted in isolation, without a unifying framework to organize efforts within the field. This leads to repetitive approaches, and puts an undue focus on the effects of bias, rather than on their origins. Research focused on bias symptoms rather than the underlying origins could limit the development of effective countermeasures. In this paper, we propose a unifying conceptualization: the predictive bias framework for NLP . We summarize the NLP literature and propose a general mathematical definition of predictive bias in NLP along with a conceptual framework, differentiating four main origins of biases: label bias, selection bias, model overamplification, and semantic bias . We discuss how past work has countered each bias origin. Our framework serves to guide an introductory overview of predictive bias in NLP, integrating existing work into a single structure and opening avenues for future research.

Attention-based models have shown significant improvement over traditional algorithms in several NLP tasks. The Transformer, for instance, is an illustrative example that generates abstract representations of tokens inputted to an encoder based on their relationships to all tokens in a sequence. Recent studies have shown that although such models are capable of learning syntactic features purely by seeing examples, explicitly feeding this information to deep learning models can significantly enhance their performance. Leveraging syntactic information like part of speech (POS) may be particularly beneficial in limited training data settings for complex models such as the Transformer. We show that the syntax-infused Transformer with multiple features achieves an improvement of 0.7 BLEU when trained on the full WMT '14 English to German translation dataset and a maximum improvement of 1.99 BLEU points when trained on a fraction of the dataset. In addition, we find that the incorporation of syntax into BERT fine-tuning outperforms baseline on a number of downstream tasks from the GLUE benchmark. Introduction Attention-based deep learning models for natural language processing (NLP) have shown promise for a variety of machine translation and natural language understanding tasks. For word-level, sequence-to-sequence tasks such as translation, paraphrasing, and text summarization, attention-based models allow a single token ( e.g., a word or subword) in a sequence to be represented as a combination of all tokens in the sequence (Luong, Pham, and Manning, 2015). The distributed context allows attention-based models to infer rich representations for tokens, leading to more robust performance.

In this paper, we develop a novel procedure for low-rank tensor regression, namely \underline{I}mportance \underline{S}ketching \underline{L}ow-rank \underline{E}stimation for \underline{T}ensors (ISLET). The central idea behind ISLET is \emph{importance sketching}, i.e., carefully designed sketches based on both the responses and low-dimensional structure of the parameter of interest. We show that the proposed method is sharply minimax optimal in terms of the mean-squared error under low-rank Tucker assumptions and under randomized Gaussian ensemble design. In addition, if a tensor is low-rank with group sparsity, our procedure also achieves minimax optimality. Further, we show through numerical studies that ISLET achieves comparable or better mean-squared error performance to existing state-of-the-art methods whilst having substantial storage and run-time advantages including capabilities for parallel and distributed computing. In particular, our procedure performs reliable estimation with tensors of dimension $p = O(10^8)$ and is $1$ or $2$ orders of magnitude faster than baseline methods.

This paper studies a tensor-structured linear regression model with a scalar response variable and tensor-structured predictors, such that the regression parameters form a tensor of order $d$ (i.e., a $d$-fold multiway array) in $\mathbb{R}^{n_1 \times n_2 \times \cdots \times n_d}$. This work focuses on the task of estimating the regression tensor from $m$ realizations of the response variable and the predictors where $m\ll n = \prod \nolimits_{i} n_i$. Despite the ill-posedness of this estimation problem, it can still be solved if the parameter tensor belongs to the space of sparse, low Tucker-rank tensors. Accordingly, the estimation procedure is posed as a non-convex optimization program over the space of sparse, low Tucker-rank tensors, and a tensor variant of projected gradient descent is proposed to solve the resulting non-convex problem. In addition, mathematical guarantees are provided that establish the proposed method converges to the correct solution under the right set of conditions. Further, an upper bound on sample complexity of tensor parameter estimation for the model under consideration is characterized for the special case when the individual (scalar) predictors independently draw values from a sub-Gaussian distribution. The sample complexity bound is shown to have a polylogarithmic dependence on $\bar{n} = \max \big\{n_i: i\in \{1,2,\ldots,d \} \big\}$ and, orderwise, it matches the bound one can obtain from a heuristic parameter counting argument. Finally, numerical experiments demonstrate the efficacy of the proposed tensor model and estimation method on a synthetic dataset and a neuroimaging dataset pertaining to attention deficit hyperactivity disorder. Specifically, the proposed method exhibits better sample complexities on both synthetic and real datasets, demonstrating the usefulness of the model and the method in settings where $n \gg m$.

In this paper, we investigate the effect of machine learning based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the group's predicted odds ratio from the model and observed odds ratio from the data. We then perform anonymization using a variational autoencoder (VAE) to synthesize an entirely new dataset that would ideally be drawn from the distribution of the original data. We repeat the anomalous subgroup discovery task on the new data and compare it to what was identified pre-anonymization. We evaluated our approach using publicly available datasets from the financial industry. Our evaluation confirmed that the approach was able to produce synthetic datasets that preserved a high level of subgroup differentiation as identified initially in the original dataset. Such a distinction was maintained while having distinctly different records between the synthetic and original dataset. Finally, we packed the above end to end process into what we call Utility Guaranteed Deep Privacy (UGDP) system. UGDP can be easily extended to onboard alternative generative approaches such as GANs to synthesize tabular data.

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