The State of Open Data - AI, Data Literacy and the Private Sector
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A short overview of selected chapter findings from 'The State of Open Data: Histories and Horizon' presented to the 2019 Developing the Caribbean conference via webcast.
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The State of Open Data - AI, Data Literacy and the Private Sector
- 1. The State of Open Data Histories and Horizons @stateofopendata
- 2. About The State of Open Data The State of Open Data is the product of an 18 month journey: bringing almost 70 authors to provide 37 short essays, each reviewing the current state of open data from a different perspective. @stateofopendata
- 3. Open Data for Development (OD4D) is a global partnership working to help create Open Data ecosystems around the world in order to spur social change, increase government transparency, and support the Global Goals for Sustainable Development. OD4D has received funding from IDRC, the World Bank, Global Affairs Canada, UK DFID, and the Hewlett Foundation, supporting the work of over 50 organizations around the world on Open Data policy, research, standards, and capacity building.
- 5. 5 lenses ● Historical ● Regional ● Sectoral ● Cross-cutting Issues ● Stakeholders @stateofopendata
- 8. Back to the future: demands for data Mid-2000, business and AI researchers calling for more public data. Their calls join with the civic tech open government agenda. Open data is born.
- 9. Mixed progress ● Many examples of open data in use within AI and enterprise... ● ...but not at the scale promised… ● … because of weaknesses in data infrastructure and data literacy.
- 11. Data literacy When organisations have access to a range of data skills, they can be more effective in making use of open data. Understanding “data as a team sport” can help move from a perspective on individual data literacy to an understanding of organisational data maturity.
- 12. Data literacy Data literacy building should be ● Long-term ● Social learning ● Collaborative ● Justice-focussed Figure 1: Generalised conceptual model of data literacy activities. Starting point Koltay (2015) Source: http://ci-journal.org/index.php/ciej/article/view/1348/1222
- 13. Private sector: opportunities Data users Intermediaries Data providers
- 14. Private sector: opportunities Data users Intermediaries Data providers Figure 2: Sector of companies using open data. Source: Calculated from Open Data Impact Map data
- 15. Private sector: opportunities Data users Intermediaries Data providers
- 16. Private sector: opportunities Data users Intermediaries Data providers Six kinds of data collaborative: ● Trusted intermediary ● Prize or challenge ● Research partnership ● Intelligence products ● APIs ● Corporate data cooperatives
- 17. Artificial intelligence Use Discovery & cleaning Information extraction and data creation
- 18. Open data and AI: critical friends? ● Creation and publication of open data can address gaps in ‘training sets’ used by machine learning; ● Models with open data inputs and outputs can support work to audit AI bias ● Open data can present alternative approaches to AI
- 20. Researchers: rebooting the research agenda ● Document history ● Compare open/non-open models ● Quantitative evidence and natural experiments Funders: mainstreaming & movement building ● Continue investment in core open data movement and infrastructure ● Integrate open data approaches into sectoral programmes ● Focus funding on (open) data literacy Policy-makers: ↟ embedding ↡ gatekeeping ● Explore the potential open data elements of any data-related project ● Maintain space for permissionless innovation & civic engagement ● Openness as a golden thread in data policy Practitioners: thinking politically, ↟ inclusion ● Engage with the politics of data ● Prioritise inclusion and equity ● Provide renewed leadership for openness @stateofopendata
- 21. Recommendations for DevCA ● Focus on problem solving ● Know your history ● Data is a team sport ● Consider sustainability ● Treat openness as a golden thread @stateofopendata
Notas del editor
- The State of Open Data book has been an 18 month long project: bringing together 66 authors to provide 37 short chapters reviewing the current state of open data in different settings, and from a range of perspectives. We have been seeking to critically review the current state of the open data movement, and assessing progress of open data in supporting social, economic development and democratic development around the world, the book looks back over the last decade, and looks forward to challenges currently confronting open data advocacy and practice.
- State of Open Data is a project of the Open Data for Development network.
- As of yesterday - we’ve ‘soft launched’ the book, making it available to download, to buy, and as a set of online chapters at stateofopendata.od4d.net It’s an open access resource, free to share and use. In the online version of each chapter you will find key points, a timeline of selected events, and links to further reading.
- In the book itself, we use five lenses to explore the state of open data. The historical lens runs across the book, and in each other case we have dedicated sections - looking at open data by region, sector, cross-cutting issues and how particular stakeholders are engaging with open data.
- In this talk, I’ve been asked to draw on three particular themes from the book: Artificial Intelligence Data Literacy & the role of the Private Sector We have specific chapters on each of these - with the Data Literacy chapter courtesy of Mariel Garcia Montes and Dirk Slater, and the Private Sector chapter a collaboration between Joel Gurin, Carla Bonina and Stefaan Verhulst. I’m also going to slip in a bit of our data infrastructure chapter, by Leigh Dodds and Peter Wells. However, as well as being distinct chapters, these issues run through many of the other chapters on sectors and regions….
- …. where authors touch upon the different ways in which civil society or private sector involvement is being supported, and the opportunities or challenges related to data literacy and data infrastructure.
- So, as we explore these issues, let’s start with some history. The book is after all, histories and horizons. I want to take you back to the mid-2000s, where we find both private sector, and artificial intelligence researchers calling for more access to data. These calls linked up with calls from civic tech movements for access to specific government datasets - and more-or-less, the open data movement was born. Around 2009 it broke through, and many governments launched data portals and data policies. Although as Maurice McNaughton writes in the Latin America and Caribbean chapter of State of Open Data, “open data in the Caribbean is still seeking to establish a scalable and sustainable platform”, this region has also seen a growth in open data availabilty.
- But - fast forward to today, and although we see use of open data in AI and the private sector - the scale of use falls short of many people’ expectations. We can talk about numerous examples of open data in use, from AI-driven corruption monitoring tools in Brazil, to business-driven Smart City platforms in Ukraine - but there does appear to be a gap between promise and what we have realised. In particular, the current wave of Artificial Intelligence research is less about the semantic web and structured data in ways that were envisaged in the last 2000s, but is much more driven b machine-learning models trained to find signal in the noise of unstructured data - or working with proprietary datasets from social media giants. Our private sector chapter concludes that although “Large and small businesses use open data for business operations and to create new products and services.” “Open data remains largely an untapped resource with much more work needed to support small and medium-sized enterprises (SMEs) to realise the benefits it can bring to drive business innovation.” Two reasons for this gap emerge: Data infrastructure Data literacy
- Open data infrastructure involves the reliable provision of datasets that are solid enough to build a business on. It involves standards, identifiers, registers - as well as governance processes to make sure data is kept up to date, and quality assured. When open data forms part of a trusted data infrastructure, it may be more likely to be used for building a business, or innovating. When businesses (and civil society) know they can get involved in discussions about how a dataset is maintained, they are more likely to rely on it, and even contribute to keeping it up to date. There is a clear message here for all stakeholders: if you want the full benefits of open data in 2019 - you can’t just throw a dataset up onto a website, you need to consider questions of standards and sustainability. In DevCA there is a real opportunity to think about, discuss and describe the data infrastructures you need…. And collaborative on creating them.
- However, perhaps the more important gap identified across many chapters in State of Open Data relates to data literacy. That is, the capacity and skill to critically create, read, work with, analyze, and argue with data. And, as Mariel and Dirk argue in their chapter, Data Literacy is not just about individual skills, but instead, as they put it ‘Data is a team sport’: it takes collaboration between people with a mix of skills and perspectives. For collaborations emerging at DevCA, that calls for thinking about who to bring into a team, and what division of labour to pursue: as well as how to learn from each other about different aspects of working with data. Crucially, in looking at Data Literacy, the State of Open Data project identified a massive under-supply of open data literacy support. As fantastic as programms like School of Data are, we can count the number of people who have been exposed to them so far in the hundreds and thousands, when we need to be able to see this learning available to, and taken up by, millions. Funders have under-invested in data literacy: and programme design has not given enough though to pedagogy and different learning styles.
- Mariel and Dirk set out what makes for good data literacy building. They talk about capacity building that is long-term, based on applied and social learning, collaborative, and crucially, justice-focussed. Events like Developing the Caribbean are a great example of collaborative and social learning… and I hope for many this will form part of a longer-term learning journey. On the question of social justice, Mariel and Dirk explain that “A focus should be placed on the value of openness in fighting inequality versus focusing solely on the value of data analysis.” Open data literacy is distinct from just data literacy. Open data literacy involves empowering people to ask questions about the data, and about it’s impacts - and to consider issues of inclusion and equity alongside issues of utility.
- So - with this in mind, let’s turn to the private sector. If the private sector has good data literacy, and can rely on solid data infrastructure - how can it engage with open data? Joel, Carla and Stefaan identify business opportunities in three areas: the private sector as data users, as intermediaries, and as data providers.
- Evidence from the Open Data Impact Map suggests businesses are using open data across a wide range of sectors. Although back in 2010 or 2011, use of open data might have meant consumer apps like Google Maps - many of the uses of open data for the private sector today are more behind the scenes. Open data is used to optimise supply chains, or understand the environmental impact of decision making. It might be used in B2B or B2G services.
- Private sector firms are also acting as data intermediaries. Big firms like Thomson Reuters, data platform providers like Junar, or new forms of company like OpenCorporates, which has a unique governance structure that allows it to provide a trusted open data infrastructure of corporate data, whilst also selling premium services.
- Lastly, firms are increasingly acting as open data providers. This can simply be by publishing their own data, or may come through participating in what GovLab have called ‘Data collaboratives’. Here - companies may not want to release their data as entirely open data, but they may work with others to identify extracts of the data or products based on it that can be released openly.
- And what role does AI play? Well, it can have a similar role. We can use AI technologies to consume and analyse data, to make it easier for others to use by cleaning up data problems, or we can use AI to produce new structured and open datasets from semi-structured information.
- But we also need to be aware of the limitations of AI. Concerns about bias from AI are widely reported: and open data has an important role to play in addressing these concerns. The creation and publication of open data can address gaps in the ‘training sets’ used by machine learning methods. A commitment to running models with open data inputs and outputs can support work to audit bias in machine-learning training and operation. And open data can present alternative approaches to AI. By it’s nature, AI can lead to centralisation of power, whereas sometimes problems are best solved with small-data - and keeping data close to people. Across the book, we find that open data has helped professionals to hone their ‘data intuition’, and we need to build on this. Data literacy building needs to also include literacy about machine-learning, and knowing when AI is the right tools for the job, and when it is not. It’s notable, for example, the Seranat D’amor, the corruption tracking app in Brazil, only applies AI to detect possible issues with elected representatives expenses - it then refers it’s suspicions to humans to check and decide what action to take.
- So - let’s look ahead. The book is after all called Histories and Horizons. In the books concluding chapter we have recommendations for four different groups:
- Researchers, Funders, Policy Makers and Practitioners. I’m not going to go through all these recommendations now - though can pick up on them in any questions.
- I wanted though to think about our take-away messages for civic and entrepreneurial hackers, about to spend a few days creating new innovations. Focus on problem solving - be led by the problems, not the datasets. When we are data literate, we can see new data infrastructures created. Know your history. Every dataset has a history. Every problem has a context. We found the historical lense of state of open data invaluable: although all too often we had to turn to the Internet Archive to find reports and write-ups from even just a few years ago. However, I’ll bet that for any issue you approach - people have been thinking about the data dimensions of it for at least a decade or more. That’s not a negative: as we find in chapters on Aid and Humanitarian data, a decade is about the time it takes for open data initiatives to truly mature. The important thing is to look at what is already there to build upon, rather than re-invesnting from scratch. Data is a team sport. I don’t need to say much more about this - other than to encourage you to think about the diversity of the teams working on data with you, or in your organisations. Consider the sustainability model (or business model) for any innovation. As companies like OpenCorporates have found, it’s possible to combine commercial sustainability with a committment to openness. This won’t work for all datasets and sectors though: different approaches are needed depending on the market and ecosystems around a particular dataset or problm space. Lastly, treat openness as a golden thread - not just an input for innovation, but also part of the intermediate stages, and the output of what you do. Openness has proven a powerful tool over the last decade to allow us to better understand the way data is changing our world. It has helped engage a wider group of people in data discussions. As attention shifts to ‘smart cities’ or ‘AI’, we must not lose sight of the importance of openness, but instead must continue to use it as a tool to address power imbalances, and keep generative space and opportunities open.
- With that, let me remind you of the address where you will find all the content of the book - and - I’m not sure if we have time for questions - but you can certainly keep in touch with us on Twitter via @stateofopendata.