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Countries around the world are enacting pay transparency policies to combat pay discrimination. Since 2000, 71 percent of OECD countries have done so. Most are enacting transparency horizontally, revealing pay between coworkers doing similar work within a firm. While these policies have narrowed coworker wage gaps, they have also led to counterproductive peer comparisons and caused employers to bargain more aggressively, lowering average wages. Other pay transparency policies, without directly targeting discrimination, have benefited workers by addressing broader information frictions in the labor market. Vertical pay transparency policies reveal to workers pay differences across different levels of seniority. Empirical evidence suggests these policies can lead to more accurate and more optimistic beliefs about earnings potential, increasing employee motivation and productivity. Cross-firm pay transparency policies reveal wage differences across employers. These policies have encouraged workers to seek jobs at higher paying firms, negotiate higher pay, and sharpened wage competition between employers. We discuss the evidence on effects of pay transparency, and open questions.

We propose a technique for producing ‘visual explanations’ for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent and explainable. Our approach—Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say ‘dog’ in a classification network or a sequence of words in captioning network) flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, Grad-CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers (e.g.VGG), (2) CNNs used for structured outputs (e.g.captioning), (3) CNNs used in tasks with multi-modal inputs (e.g.visual question answering) or reinforcement learning, all without architectural changes or re-training. We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and apply it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into failure modes of these models (showing that seemingly unreasonable predictions have reasonable explanations), (b) outperform previous methods on the ILSVRC-15 weakly-supervised localization task, (c) are robust to adversarial perturbations, (d) are more faithful to the underlying model, and (e) help achieve model generalization by identifying dataset bias. For image captioning and VQA, our visualizations show that even non-attention based models learn to localize discriminative regions of input image. We devise a way to identify important neurons through Grad-CAM and combine it with neuron names (Bau et al. in Computer vision and pattern recognition, 2017) to provide textual explanations for model decisions. Finally, we design and conduct human studies to measure if Grad-CAM explanations help users establish appropriate trust in predictions from deep networks and show that Grad-CAM helps untrained users successfully discern a ‘stronger’ deep network from a ‘weaker’ one even when both make identical predictions. Our code is available at https://github.com/ramprs/grad-cam/, along with a demo on CloudCV (Agrawal et al., in: Mobile cloud visual media computing, pp 265–290. Springer, 2015) (http://gradcam.cloudcv.org) and a video at http://youtu.be/COjUB9Izk6E.

Research Summary We used interviews with elite informants as a case study to illustrate the need to expand the discussion of transparency and replicability to qualitative methodology. An analysis of 52 articles published in Strategic Management Journal revealed that none of them were sufficiently transparent to allow for exact replication, empirical replication, or conceptual replication. We offer 12 transparency criteria, and behaviorally‐anchored ratings scales to measure them, that can be used by authors as they plan and conduct qualitative research as well as by journal reviewers and editors when they evaluate the transparency of submitted manuscripts. We hope our article will serve as a catalyst for improving the degree of transparency and replicability of future qualitative research. Managerial Summary If organizations implement practices based on published research, will they produce results consistent with those reported in the articles? To answer this question, it is critical that published articles be transparent in terms of what has been done, why, and how. We investigated 52 articles published in Strategic Management Journal that reported interviewing elite informants (e.g., members of the top management team) and found that none of the articles were sufficiently transparent. These results lead to thorny questions about the trustworthiness of published research, but also important opportunities for future improvements about research transparency and replicability. We offer recommendations on 12 transparency criteria, and how to measure them, that can be used to evaluate past as well as future research using qualitative methods.

How does transparency, a key feature of central bank design, affect monetary policy makers’ deliberations? Theory predicts a positive discipline effect and negative conformity effect. We empirically explore these effects using a natural experiment in the Federal Open Market Committee in 1993 and computational linguistics algorithms. We first find large changes in communication patterns after transparency. We then propose a difference-in-differences approach inspired by the career concerns literature, and find evidence for both effects. Finally, we construct an influence measure that suggests the discipline effect dominates.

Transparent piezoelectrics are highly desirable for numerous hybrid ultrasound–optical devices ranging from photoacoustic imaging transducers to transparent actuators for haptic applications 1 – 7 . However, it is challenging to achieve high piezoelectricity and perfect transparency simultaneously because most high-performance piezoelectrics are ferroelectrics that contain high-density light-scattering domain walls. Here, through a combination of phase-field simulations and experiments, we demonstrate a relatively simple method of using an alternating-current electric field to engineer the domain structures of originally opaque rhombohedral Pb(Mg 1/3 Nb 2/3 )O 3 -PbTiO 3 (PMN-PT) crystals to simultaneously generate near-perfect transparency, an ultrahigh piezoelectric coefficient d 33 (greater than 2,100 picocoulombs per newton), an excellent electromechanical coupling factor k 33 (about 94 per cent) and a large electro-optical coefficient γ 33 (approximately 220 picometres per volt), which is far beyond the performance of the commonly used transparent ferroelectric crystal LiNbO 3 . We find that increasing the domain size leads to a higher d 33 value for the [001]-oriented rhombohedral PMN-PT crystals, challenging the conventional wisdom that decreasing the domain size always results in higher piezoelectricity 8 – 10 . This work presents a paradigm for achieving high transparency and piezoelectricity by ferroelectric domain engineering, and we expect the transparent ferroelectric crystals reported here to provide a route to a wide range of hybrid device applications, such as medical imaging, self-energy-harvesting touch screens and invisible robotic devices. The use of alternating-current electric fields to control domain size in ferroelectric crystals affords excellent transparency, piezoelectricity and birefringence.

There is growing interest in enhancing research transparency and reproducibility in economics and other scientific fields. We survey existing work on these topics within economics and discuss the evidence suggesting that publication bias, inability to replicate, and specification searching remain widespread in the discipline. We next discuss recent progress in this area, including through improved research design, study registration and pre-analysis plans, disclosure standards, and open sharing of data and materials, drawing on experiences in both economics and other social sciences. We discuss areas where consensus is emerging on new practices, as well as approaches that remain controversial, and speculate about the most effective ways to make economics research more credible in the future.

In this article, we develop the concept of Transparency by Design that serves as practical guidance in helping promote the beneficial functions of transparency while mitigating its challenges in automated-decision making (ADM) environments. With the rise of artificial intelligence (AI) and the ability of AI systems to make automated and self-learned decisions, a call for transparency of how such systems reach decisions has echoed within academic and policy circles. The term transparency, however, relates to multiple concepts, fulfills many functions, and holds different promises that struggle to be realized in concrete applications. Indeed, the complexity of transparency for ADM shows tension between transparency as a normative ideal and its translation to practical application. To address this tension, we first conduct a review of transparency, analyzing its challenges and limitations concerning automated decision-making practices. We then look at the lessons learned from the development of Privacy by Design, as a basis for developing the Transparency by Design principles. Finally, we propose a set of nine principles to cover relevant contextual, technical, informational, and stakeholder-sensitive considerations. Transparency by Design is a model that helps organizations design transparent AI systems, by integrating these principles in a step-by-step manner and as an ex-ante value, not as an afterthought.

The celebrated electronic properties of graphene 1 , 2 have opened the way for materials just one atom thick 3 to be used in the post-silicon electronic era 4 . An important milestone was the creation of heterostructures based on graphene and other two-dimensional crystals, which can be assembled into three-dimensional stacks with atomic layer precision 5 , 6 , 7 . Such layered structures have already demonstrated a range of fascinating physical phenomena 8 , 9 , 10 , 11 , and have also been used in demonstrating a prototype field-effect tunnelling transistor 12 , which is regarded to be a candidate for post-CMOS (complementary metal-oxide semiconductor) technology. The range of possible materials that could be incorporated into such stacks is very large. Indeed, there are many other materials with layers linked by weak van der Waals forces that can be exfoliated 3 , 13 and combined together to create novel highly tailored heterostructures. Here, we describe a new generation of field-effect vertical tunnelling transistors where two-dimensional tungsten disulphide serves as an atomically thin barrier between two layers of either mechanically exfoliated or chemical vapour deposition-grown graphene. The combination of tunnelling (under the barrier) and thermionic (over the barrier) transport allows for unprecedented current modulation exceeding 1 × 10 6 at room temperature and very high ON current. These devices can also operate on transparent and flexible substrates. A tunnelling transistor based on stacks of chemically grown graphene and other two-dimensional layers shows record performance.

Research Summary State‐owned enterprises (SOEs) are often more opaque than other types of firms. This opaqueness tends to generate resistance when SOEs undertake cross‐border acquisitions. Opaqueness can also aggravate concerns about an SOE's semipolitical nature and its susceptibility to agency problems, making gaining legitimacy harder. Data on attempted foreign acquisitions by Chinese firms were analyzed to compare the likelihood of deal completion between SOEs and firms with other forms of ownership. The SOEs' completion rate was 14% lower than that of other firms. Their disadvantage was shown to be alleviated when they could provide credible signals by being publicly listed (though only on an exchange in a well‐developed economy or by hiring reputable auditors). We also find that the disadvantage of SOEs was partially mediated by their opaqueness. Managerial Summary Opaqueness, or lack of transparency, is critical in many business transactions. In this article, we argue that the concept of opaqueness can help us understand why SOEs tend to have a lower likelihood of deal completion in cross‐border acquisitions. Our evidence suggests that opaqueness influences the relationship between state ownership and deal completion, and firms can improve their chance of success in cross‐border acquisitions by providing credible information, such as by listing on an exchange in a developed market or hiring a reputable auditor. These help mitigate the hazard of opaqueness.

The idea of artificial intelligence for social good (henceforth AI4SG) is gaining traction within information societies in general and the AI community in particular. It has the potential to tackle social problems through the development of AI-based solutions. Yet, to date, there is only limited understanding of what makes AI socially good in theory, what counts as AI4SG in practice, and how to reproduce its initial successes in terms of policies. This article addresses this gap by identifying seven ethical factors that are essential for future AI4SG initiatives. The analysis is supported by 27 case examples of AI4SG projects. Some of these factors are almost entirely novel to AI, while the significance of other factors is heightened by the use of AI. From each of these factors, corresponding best practices are formulated which, subject to context and balance, may serve as preliminary guidelines to ensure that well-designed AI is more likely to serve the social good.