Changes

<h3 style="text-decoration:none;">[https://www.stateofopendata.od4d.net/ State of Open Data]</h3>

<h3 style="text-decoration:none;">[https://www.stateofopendata.od4d.net/ State of Open Data]</h3>

<p class="author">by Tim Davies, Stephen B Walker, and Mor Rubinstein, on Open Data for Development</p>

<p class="author">by Tim Davies, Stephen B Walker, and Mor Rubinstein, on Open Data for Development</p>

<p>It’s been ten years since open data first broke onto the global stage. Over the past decade, thousands of programmes and projects around the world have worked to open data and use it to address a myriad of social and economic challenges. Meanwhile, issues related to data rights and privacy have moved to the centre of public and political discourse. As the open data movement enters a new phase in its evolution, shifting to target real-world problems and embed open data thinking into other existing or emerging communities of practice, big questions still remain.</p>

<p>(In English) It’s been ten years since open data first broke onto the global stage. Over the past decade, thousands of programmes and projects around the world have worked to open data and use it to address a myriad of social and economic challenges. Meanwhile, issues related to data rights and privacy have moved to the centre of public and political discourse. As the open data movement enters a new phase in its evolution, shifting to target real-world problems and embed open data thinking into other existing or emerging communities of practice, big questions still remain.</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<h3 style="text-decoration:none;">[https://arxiv.org/abs/1811.10154 Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead]</h3>

<h3 style="text-decoration:none;">[https://arxiv.org/abs/1811.10154 Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead]</h3>

<p class="author">Cynthia Rudin</p>

<p class="author">Cynthia Rudin</p>

<p>Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare and computer vision.</p>

<p>(In English) Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare and computer vision.</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<h2>Websites</h2>

<h2>Websites</h2>

<h3 style="text-decoration:none;">[https://data2x.org/ Data2x]</h3>

<h3 style="text-decoration:none;">[https://data2x.org/ Data2x]</h3>

<p>Important data about women and girls is incomplete or missing. Through partnerships with UN agencies, governments, civil society, academics, and the private sector, Data2X is working for change.</p>

<p>(In English) Important data about women and girls is incomplete or missing. Through partnerships with UN agencies, governments, civil society, academics, and the private sector, Data2X is working for change.</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<h3 style="text-decoration:none;">[https://www.reddit.com/r/dataisbeautiful/top/?t=all /r/DataIsBeautiful]</h3>

<h3 style="text-decoration:none;">[https://www.reddit.com/r/dataisbeautiful/top/?t=all /r/DataIsBeautiful]</h3>

<p class="author">on Reddit</p>

<p class="author">on Reddit</p>

<p>DataIsBeautiful is for visualizations that effectively convey information. Aesthetics are an important part of information visualization, but pretty pictures are not the sole aim of this subreddit.</p>

<p>(In English) DataIsBeautiful is for visualizations that effectively convey information. Aesthetics are an important part of information visualization, but pretty pictures are not the sole aim of this subreddit.</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<h3 style="text-decoration:none;">[https://informationisbeautiful.net/ Information is beautiful]</h3>

<h3 style="text-decoration:none;">[https://informationisbeautiful.net/ Information is beautiful]</h3>

<p class="author">by David McCandless</p>

<p class="author">by David McCandless</p>

<p>Data, information, knowledge: we distil it into beautiful, useful graphics & diagrams. Information is Beautiful is dedicated to helping you make clearer, more informed decisions about the world. All our visualizations are based on facts and data: constantly updated, revised and revisioned.</p>

<p>(In English) Data, information, knowledge: we distil it into beautiful, useful graphics & diagrams. Information is Beautiful is dedicated to helping you make clearer, more informed decisions about the world. All our visualizations are based on facts and data: constantly updated, revised and revisioned.</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<h3 style="text-decoration:none;">[https://opendatacharter.net/ International Open Data Charter]</h3>

<h3 style="text-decoration:none;">[https://opendatacharter.net/ International Open Data Charter]</h3>

<p>The Open Data Charter is a collaboration between over 150 governments and organisations working to open up data based on a shared set of principles. We promote policies and practices that enable governments and CSOs to collect, share, and use well-governed data, to respond effectively and accountably to the following focus areas: anti-corruption, climate action and pay equity.</p>

<p>(In English) The Open Data Charter is a collaboration between over 150 governments and organisations working to open up data based on a shared set of principles. We promote policies and practices that enable governments and CSOs to collect, share, and use well-governed data, to respond effectively and accountably to the following focus areas: anti-corruption, climate action and pay equity.</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<h3 style="text-decoration:none;">[https://www.oecd.org/gov/digital-government/open-government-data.htm OECD Open Government data]</h3>

<h3 style="text-decoration:none;">[https://www.oecd.org/gov/digital-government/open-government-data.htm OECD Open Government data]</h3>

<p>Open Government Data (OGD) is a philosophy- and increasingly a set of policies - that promotes transparency, accountability and value creation by making government data available to all. Public bodies produce and commission huge quantities of data and information. By making their datasets available, public institutions become more transparent and accountable to citizens. By encouraging the use, reuse and free distribution of datasets, governments promote business creation and innovative, citizen-centric services.</p>

<p>(In English) Open Government Data (OGD) is a philosophy- and increasingly a set of policies - that promotes transparency, accountability and value creation by making government data available to all. Public bodies produce and commission huge quantities of data and information. By making their datasets available, public institutions become more transparent and accountable to citizens. By encouraging the use, reuse and free distribution of datasets, governments promote business creation and innovative, citizen-centric services.</p>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>

<h3 style="text-decoration:none;">[https://theodi.org/ Open Data Institute]</h3>

<h3 style="text-decoration:none;">[https://theodi.org/ Open Data Institute]</h3>

<p>The ODI is a non-profit with a mission to work with companies and governments to build an open, trustworthy data ecosystem. We work with a range of organisations, governments, public bodies and civil society to create a world where data works for everyone.</p>

<p>(In English) The ODI is a non-profit with a mission to work with companies and governments to build an open, trustworthy data ecosystem. We work with a range of organisations, governments, public bodies and civil society to create a world where data works for everyone.</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<h3 style="text-decoration:none;">[https://www.data.gov/ US Government Open Data]</h3>

<h3 style="text-decoration:none;">[https://www.data.gov/ US Government Open Data]</h3>

<p>Find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more.</p>

<p>(In English) Find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more.</p>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>

[[Image:Data-interoperatiblity_guide-UN.png|150px|Data Interoperability Guide]]

[[Image:Data-interoperatiblity_guide-UN.png|150px|Data Interoperability Guide]]

<h3 style="text-decoration:none;">[https://unstats.un.org/wiki/display/InteropGuide/Introduction Data Interoperability Guide]</h3>

<h3 style="text-decoration:none;">[https://unstats.un.org/wiki/display/InteropGuide/Introduction Data Interoperability Guide]</h3>

<p class="author">by Luis Gonzalez, on the UN Statistics Wiki</p>

<p class="author">Luis Gonzalez, on the UN Statistics Wiki</p>

<p>Over the years, countless systems that do not talk to one another have been created within and across organizations for the purposes of collecting, processing and disseminating data for development. With the proliferation of different technology platforms, data definitions and institutional arrangements for managing, sharing and using data, it has become increasingly necessary to dedicate resources to integrate the data necessary to support policy-design and decision-making. Interoperability is the ability to join-up and merge data without losing meaning (JUDS 2016). In practice, data is said to be interoperable when it can be easily re-used and processed in different applications, allowing different information systems to work together. Interoperability is a key enabler for the development sector to become more data-driven.</p>

<p>(In English) Over the years, countless systems that do not talk to one another have been created within and across organizations for the purposes of collecting, processing and disseminating data for development. With the proliferation of different technology platforms, data definitions and institutional arrangements for managing, sharing and using data, it has become increasingly necessary to dedicate resources to integrate the data necessary to support policy-design and decision-making. Interoperability is the ability to join-up and merge data without losing meaning (JUDS 2016). In practice, data is said to be interoperable when it can be easily re-used and processed in different applications, allowing different information systems to work together. Interoperability is a key enabler for the development sector to become more data-driven.</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

[[Image:Data-Ethics-Canvas.jpg|150px|The Data Ethics Canvas]]

[[Image:Data-Ethics-Canvas.jpg|150px|The Data Ethics Canvas]]

<h3 style="text-decoration:none;">[https://theodi.org/article/the-data-ethics-canvas-2021/ The Data Ethics Canvas]</h3>

<h3 style="text-decoration:none;">[https://theodi.org/article/the-data-ethics-canvas-2021/ The Data Ethics Canvas]</h3>

<p class="author">by Dave Tarrant, James Maddison, Olivier Thereaux</p>

<p class="author">Dave Tarrant, James Maddison, Olivier Thereaux</p>

<p>The Data Ethics Canvas is a tool for anyone who collects, shares or uses data. It helps identify and manage ethical issues – at the start of a project that uses data, and throughout. It encourages you to ask important questions about projects that use data, and reflect on the responses. The Data Ethics Canvas provides a framework to develop ethical guidance that suits any context, whatever the project’s size or scope.</p>

<p>(In English) The Data Ethics Canvas is a tool for anyone who collects, shares or uses data. It helps identify and manage ethical issues – at the start of a project that uses data, and throughout. It encourages you to ask important questions about projects that use data, and reflect on the responses. The Data Ethics Canvas provides a framework to develop ethical guidance that suits any context, whatever the project’s size or scope.</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

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<h3 style="text-decoration:none;">[https://brief.montrealethics.ai/ The AI Ethics Brief]</h3>

<h3 style="text-decoration:none;">[https://brief.montrealethics.ai/ The AI Ethics Brief]</h3>

<p class="author">from the Montreal AI Ethics Institute</p>

<p class="author">from the Montreal AI Ethics Institute</p>

<p>The Montreal AI Ethics Institute is an international non-profit organization democratizing AI ethics literacy. Subscribe to get full access to the newsletter and have the latest from the field of AI ethics delivered right to your inbox every week. Never miss an update from the work being done at the Montreal AI Ethics Institute and our thoughts on research and development in the field from around the world.</p>

<p>(In English) The Montreal AI Ethics Institute is an international non-profit organization democratizing AI ethics literacy. Subscribe to get full access to the newsletter and have the latest from the field of AI ethics delivered right to your inbox every week. Never miss an update from the work being done at the Montreal AI Ethics Institute and our thoughts on research and development in the field from around the world.</p>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>

<h2>Podcasts</h2>

<h2>Podcasts</h2>

[[Image:Women-in-data-science-podcast.PNG|150px|Women in Data Science podcast, Stanford University]]

[[Image:Women-in-data-science-podcast.PNG|150px|Women in Data Science podcast]]

<h3 style="text-decoration:none;">[https://www.widsconference.org/podcast.html Women in Data Science podcast]</h3>

<h3 style="text-decoration:none;">[https://www.widsconference.org/podcast.html Women in Data Science podcast]</h3>

<p>Leading women in data science share their work, advice, and lessons learned along the way with Professor Margot Gerritsen from Stanford University. Hear about how data science is being applied and having impact across a wide range of domains, from healthcare to finance to cosmology to human rights and more.</p>

<p class="author">from Stanford University</p>

<p>(In English) Leading women in data science share their work, advice, and lessons learned along the way with Professor Margot Gerritsen from Stanford University. Hear about how data science is being applied and having impact across a wide range of domains, from healthcare to finance to cosmology to human rights and more.</p>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>

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[[Image:Data-sceptic-podcast.PNG|150px|Data Skeptic podcast]]

[[Image:Data-sceptic-podcast.PNG|150px|Data Skeptic podcast]]

<h3 style="text-decoration:none;">[https://dataskeptic.com/ Data Skeptic podcast]</h3>

<h3 style="text-decoration:none;">[https://dataskeptic.com/ Data Skeptic podcast]</h3>

<p>Your trusted podcast, centered on data science, machine learning, and artificial intelligence.</p>

<p>(In English) Your trusted podcast, centered on data science, machine learning, and artificial intelligence.</p>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>

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