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The new Building Responsive Investments in Data for Gender Equality (BRIDGE) tool is designed for use by gender data focal points in national statistical offices (NSOs) of low- and middle-income countries and by their partners within the national statistical system (NSS) to communicate gender data priorities to domestic sources of financing and international donors.

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This consent-driven, publicly available dataset enables researchers to better evaluate the fairness and robustness of certain AI models. This was informed and shaped by a comprehensive literature review around relevant demographic categories and was created in consultation with internal experts in fields such as civil rights.

The post Introducing Casual Conversations v2: A More Inclusive Dataset to Measure Fairness appeared first on data.org.

Data observability is a set of principles that can be implemented in tools to ensure data is accurate, up-to-date, and complete. This principle is increasingly necessary, especially as traditional approaches to software monitoring fall short for high-volume, high-variety data. Unit tests, which assess small pieces of code for performance on discrete, deterministic tasks, get overwhelmed by the variety of acceptable shapes and values that real-world data can take.

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To ensure the most trustworthy and responsible data-driven decisions are made across organizations, this resource provides a central repository of curated, reviewed, and well-organized best practices for various data problems. These problems are both commonly encountered across organizations and are critical to a trustworthy use of data. This resource empowers people working with data to follow approved techniques to solve important products and problems.

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These tools provide users leverage to develop and interact with climate data sources. The ‘Models and Tools’ sheet collects analytics models that process/model raw data with output, such as synthesized indexes, hazard impact prediction, and assessment of risks related to climate, environment, and public health.

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Data can be a powerful tool, particularly in the social sector, with unique opportunities and challenges alike. By being more strategic in how we engage with data, we have an opportunity to advance our missions, increase impact, and promote equitable outcomes. This course was designed with and for social sector organizations seeking to build capacity in these areas.

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This document focuses on privacy-enhancing technologies (PETs) that protect data during analysis and dissemination of sensitive information so that the benefits of using data for official statistics can be realized while minimizing privacy risks to those entrusting sensitive data to National Statistics Offices (NSOs).

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Every organization is a data organization, involved in data collection, maintenance, and sharing. While businesses, governments, and other organizations — as well as the people and societies they serve — benefit immensely from data, legitimate concerns remain about how data is — or might be — used.

Data assurance is essential to enable trusted and trustworthy data practices. Unlocking the value of data requires that the individuals and organizations who collect, maintain, share, and use data are trusted. 

The post Data Assurance: What is it and Why do we Need it? appeared first on data.org.

Governments play a critical role in providing data that is vital to addressing today’s most pressing problems and improving people’s lives. It is essential that governments at all levels work collaboratively to share data across the public and private sectors in a responsible manner. This resource is a repository of recent policy developments concerning open data, data reuse, and data collaboration around the world.

The post The State of Open Data Policy Repository appeared first on data.org.

Social impact organizations (SIOs) can find sample code, templates, and other assets from across the Microsoft ecosystem. This GitHub repo provides lightweight solutions, such as a damage assessment visualizer, an open data community kit, and a data sharing accelerator, that are ready to use and easy to adapt — and is continually updated by the Tech for Social Impact team.

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As artificial intelligence (AI) becomes more and more present in our lives, there is a greater impetus for leaders to engage in inclusive and equitable AI practices. This playbook shares good practices for leaders to advance equity and inclusion in AI and machine learning (ML) language.

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