Tim has spent the last 20 years working at the intersection of technology, participation and governance as both a researcher and practitioner. From piloting digital tools bringing youth voice into local decisions, to developing data standards the enable community scrutiny of billions of dollars of public spending, or writing about the political dynamics of open data initiatives, his work has explored how shared social challenges need participatory, collaborative and collective responses.
Tim was lead for the World Wide Web Foundation’s Open Data in Developing Countries research network (2013 - 2015), and led development of the Open Data Barometer. He was co-editor of The State of Open Data: Histories and Horizons (2019), and founding director of the Global Data Barometer project. From 2015 - 2018 Tim was a co-founder and director of Open Data Services Co-operative, a worker owned team providing the technical backing to initiatives including 360Giving, the Open Contracting Data Standard and OpenOwnership.
Tim is a former fellow of the Harvard Berkman Klein Center for Internet and Society, and a senior fellow of the Datasphere Initiative. He is a graduate of the Oxford Internet Institute (Social Science of the Internet), and Oriel College, Oxford (Politics, Philosophy and Economics).
He lives in the People’s Republic of Stroud where he is involved in various Green politics.
How should the toolkit of open government be applied to the governance of data and AI? That’s the question we set out to ask with our design lab workshop on the fringes of the Open Government Partnership (OGP) summit earlier this week.
The answer we arrived at: we need policy commitments that move beyond transparency alone, to centre the informed voice of citizens and affected communities in deliberating on and setting out the social licence for data and AI systems to operate, and in monitoring their procurement, implementation and impacts.
At the Data Justice Conference in Cardiff a few weeks ago we ran the first public play test of a card game designed to support conversations about collective and participatory data governance.
It’s the first iteration of the output from our participation design lab process exploring game design both as a method for researching methods to involve communities in data governance, and as a way of generating resources that might help inspire and embed new ways of working, particularly within private sector contexts.
This is the second post in a series produced as part of the analysis for the Measuring Data Values Around the World project.
We have previously scoped out how existing primary data collected from the Global Data Barometer might map to the Data Values framework. As a multi-dimensional composite index, the Global Data Barometer is based on both primary and secondary data sources.
In this post, we consider if there are elements of data values measurement which could be addressed by drawing on existing secondary indicators or by incorporating additional secondary data sources. These could feed into future iterations of the GDB, or be used in Data Values measurement products, tools or analysis based partially on the GDB.
As part of our project exploring how the Global Data Barometer might be used to provide insights and metrics for measurement against the Data Values framework, I’ve been looking into how Large Language Models (LLMs) like ChatGPT might impact upon the methodology of expert survey studies like the Barometer.
This post contains some initial notes from this exploration.
The GovLab and new non-profit The Datatank have launched a conversation about the job specification for a re-imagined data stewardship role. They argue that ‘Data Stewards’ are a much-needed role in both public and private organisations, operating to maximise re-use of data in the public interest, and are inviting feedback on a revised Data Steward job specification.
In this post I look at the feedback that a collective and participatory data governance frame might offer.
On Monday 6th March, participants from 12 organisations working on issues related to poverty gathered for a workshop in London to dream, imagine and envision potential directions for an ‘insight infrastructure’ to support action on poverty, social and economic inequality.
This was the second workshop in an appreciative inquiry series. The first‘Discovery’ session is documented here.
In this post, we summarise some of the key themes explored in the workshop, and document the ideas and suggestions made that will feed into the next workshop, which will focus on the design of stakeholder engagement, and governance proposals for insight infrastructure.
This week we had the first of our workshop sessions to explore the potential ecosystem around, and stakeholder engagement in, Joseph Rowntree Foundation (JRF) plans to develop an Insight Infrastructure on poverty in the UK.
Below I’ve written up an initial synthesis of the session.
Over last three weeks at the end of July 2022 I was an observer of the NHS AI Lab Public Dialogue on data stewardship: a process involving around 50 members of the public meeting for 12 hours (across four sessions) to share their ‘thoughts, aspirations, hopes and concerns’ about how access to healthcare data for AI purposes should be managed. A report of the dialogue was published by the organisers (Open Data Institute, Imperial College Health Partners and Ipsos), and the NHS AI Lab (who co-funded the dialogue along with Sciencewise) intend to use the findings to inform the Terms of Reference for a research competition titled ‘Participatory Fund for Patient-Driven AI Ethics Research’.
This write-up contains my notes as an independent observer of the dialogue, and member of the project’s Stakeholder group.