Query: data for good

title: Visualizing Data for Good
abstract: Good data facilitates acting upon pressing issues, while visualizations help bring the data to life, offering important insights to policy makers and the public. A specific area where awareness building is crucial, is the one of social inequalities around the world. This paper presents the work of 17 student team projects, developed during the "Data Visualization" course of the University of Twente at the spring semester 2018, focusing on the topic of enlightening the public over social inequalities, targeting different sustainable development goals, from the ones set by the United Nations. Based on truthful, functional and insightful data visualizations, social inequalities can be effectively communicated to stakeholders and the public, raising awareness on different unjust issues happening around the globe.

title: Data for Good: Abstract
abstract: I use the tagline "Data for Good" to state paronomastically how we as a community should be promoting data science, especially in training future generations of data scientists. First, we should use data science for the good of humanity and society. Data science should be used to better people's lives. Data science should be used to improve relationships among people, organizations, and institutions. Data science, in collaboration with other disciplines, should be used to help tackle societal grand challenges such as climate change, education, energy, environment, healthcare, inequality, and social justice. Second, we should use data in a good manner. The acronym FATES suggests what "good" means. Fairness means that the models we build are used to make unbiased decisions or predictions. Accountability means to determine and assign responsibility to someone or to something for a judgment made by a machine. Transparency means being open and clear to the end user about how an outcome, e.g. a classification, a decision, or a prediction, is made. Ethics for data science means paying attention to both the ethical and privacy preserving collection and use of data as well as the ethical decisions that the automated systems we build will make. Safety and security (yes, two words for one "S" means ensuring that the systems we build are safe (do no harm) and secure (guard against malicious behavior)

title: Data for good in Africa
abstract: Data has been rightly acknowledged as the future oil of the global economy. As a result of the large amounts of data being produced globally by individuals, households, organizations and nations, the potential for mining insights from aggregated big data has emerged, besides other uses of data. The potential multifaceted use of data for private and public good is fueled by super fast telecommunication networks which power communications and data capture, transfer, storage and processing. However, while developed economies are pressing ahead to harness the potential of data to transform their societies, Africa has lagged behind in the harnessing of data for public good. In many African countries, basic access to data is still a major challenge, and the policy landscape which enables data for public good is largely lacking. This paper gives an overview of the situation in Africa, where the infrastructure and data policy deficit is hampering the continent's efforts towards digital development.

title: Data Resources for Data for Good Researchers

title: Data for Good On the Job!

title: What makes for good views for contrastive learning
abstract: Contrastive learning between multiple views of the data has recently achieved state of the art performance in the field of self supervised representation learning. Despite its success, the influence of different view choices has been less studied. In this paper, we use empirical analysis to better understand the importance of view selection, and argue that we should reduce the mutual information (MI) between views while keeping task relevant information intact. To verify this hypothesis, we devise unsupervised and semi supervised frameworks that learn effective views by aiming to reduce their MI. We also consider data augmentation as a way to reduce MI, and show that increasing data augmentation indeed leads to decreasing MI and improves downstream classification accuracy. As a by product, we also achieve a new state of the art accuracy on unsupervised pre training for ImageNet classification $73\ top 1 linear readoff with a ResNet 50) In addition, transferring our models to PASCAL VOC object detection and COCO instance segmentation consistently outperforms supervised pre training. Code:this http URL

title: Big Data for Policy Analysis: The Good, The Bad, and The Ugly
abstract: Big data holds tremendous potential for public policy analysis. At the same time, its use prompts a number of issues related to statistical bias, privacy, equity, and governance, among others. Accordingly, there is a need to formulate, evaluate, and implement policies that not only mitigate the risks, but also maximize the benefits of using big data for policy analysis. This poses a number of challenges, which are highlighted in this essay.

title: Big Data for Social Good
abstract: The use of large scale and new, emerging sources of data to make better decisions has been increasing over the past several years. Corporations have been the first to act on this potential in industries such as search, advertising, finance, surveillance, retail, and manufacturing. At the same time, organizations focused on social good are realizing the potential as well but face several challenges as they seek to become more data driven. The biggest challenge they face is a paucity of examples and case studies on how data can be used for social good. This special issue of Big Data is targeted at tackling that challenge and focuses on highlighting some exciting and impactful examples of work that uses data for social good. The special issue is just one example of the recent surge in such efforts by the data science community. Some examples of these efforts include:

title: Data for the good of everyone

title: Good Enough Compositional Data Augmentation
abstract: We propose a simple data augmentation protocol aimed at providing a compositional inductive bias in conditional and unconditional sequence models. Under this protocol, synthetic training examples are constructed by taking real training examples and replacing (possibly discontinuous) fragments with other fragments that appear in at least one similar environment. The protocol is model agnostic and useful for a variety of tasks. Applied to neural sequence to sequence models, it reduces error rate by as much as 87% on diagnostic tasks from the SCAN dataset and 16% on a semantic parsing task. Applied to n gram language models, it reduces perplexity by roughly 1% on small corpora in several languages.