Good to see how various strands combine here, apart from the topic which is governance of smart cities. The immediate trigger for Peter Bihr is Toronto’s smart city plan, on his radar as he was recently in Canada. We both were to visit Peter Rukavina’s unconference. He references how back in 2011 we already touched upon most of the key ingredients, at the Cognitive Cities conference in Berlin, which he organised, and where I spoke. And he mentions doing a fellowship on this very topic for the Edgeryders, my favourite community in Europe for these type of issues, and which I try to support where I can.

Read How to plan & govern a smart city? (The Waving Cat)

What does governance mean in a so-called smart city context. What is it that’s being governed and how, and maybe most importantly, by whom?

This week I provided a training session on how open data can play a role in public governance integrity and in fighting corruption. The Hague Academy is hosting a group of 11 participants from a wide variety of countries (Nepal, Uganda, Nigeria, Colombia, Northern Iraq, Northern Macedonia, Jordania, Indonesia) for a two week training course. My colleague Paul and I were invited to do a half day session on open data. Where Paul explained the status quo of open data in the Netherlands, I talked about my international experiences, and what that tells me concerning open data in the fight against corruption.

This is the basic outline of what I talked about:

I started off with noting that data these days is a geopolitical issue, making it a strategic good for any organisation.
Then after defining what open data is (pro-actively published, no tech, financial or legal barrierers to re-use), I mentioned what it does: allow access to all (the clue is in the word open), bring in new stakeholders, and allow those stakeholders to act differently. These aspects create impact in different areas, economic activity, civic activity, better and cheaper public service, and transparency.

If you know these impacts occur, you can set out to cause it to happen. Around an issue you can aim to activate stakeholders by providing them with data, for instance to stimulate economic activity. This makes open data a policy instrument, and a cheap one compared to regulation and financing.

But in many instances if you set out to achieve one type of impact, you are likely to also see other types of impact.
This is important because it allows you to find the right type of intrinsic motivation for an entity to publish their data, while knowing it allows other types of impact that you’re interested in as well. Such as planning increased transparency by mapping the government funds flowing into a neighbourhood, and then seeing citizens taking over a community center as a non-profit, reducing the strain on the city government’s budget, and creating additional jobs by providing training to other groups to do the same. Or flipped around, if a government is averse to transparency they may be tempted by the economic potential of certain data being open, and cause transparency as a side effect.

In terms of integrity and anti-corruption, I find I make a distinction between three types of data.

There’s the basic ‘daylight’ data, that may immediately show misconduct. Think of the UK MP’s expenses scandal in 2009. Or the current ‘Shell Papers‘ project by Dutch media, which is about shedding daylight on ties between the multinational and government.

Then there’s data that in itself doesn’t show misconduct, but in combination with others sources allows people agency. E.g. in researching connections, such as combining procurement data with ultimate beneficial ownership of companies winning contracts, and reverse searching the data to find what else they won from government tenders. Or opening up court statistics, verdicts, and court performance reports, in order to allow players in the judicial system to reduce differences between courts, thus increasing judicial system quality and reducing uncertainty for businesses.

The third type of data is data that can be used to spot patterns, or spot (absence of) impact. Think of a situation where the ministry of education allocates budgets to schools, and sends the money through a regional organisation, and where the schools receive their funding from that regional organisation. Now the ministry knows what the budget is and what they sent, but not if that sum arrived. The school knows what it received but not what was budgeted. When both release the data it may show there are differences or not. Of interest here is it cuts out the ability of a middleman to control communication flows, and a bottleneck becomes visible. Opening up a chain like that makes issues visible not because the actions of a corrupt actor show up in the data, but because an expected thing isn’t happening. This means data that doesn’t directly show corruption can be used to detect it. Budget versus delivery and impact comparison is the basic type. None of this is under the direct influence of those involved in corruption, because the data is about steps before and after them in the process. Steps whose actors likely don’t feel threatened by opening that data up.

This last bit is what national governments can use to lower corruption. Not necessarily by catching bad actors and seeking punishment, but by reducing opportunity and make it slowly disappear. Other governments bet heavily on e-government for similar reasons. E-gov measures reduce the number of face to face interaction with civil servants, and thus cut out potential points of bribery. This however only addresses low level corruption, and doesn’t attack systemic corruption.

Then I switched gears a bit and talked about the difference between the perspective from inside a country, and across borders. Corruption and malfeasance crosses borders, but governments are bound to their own jurisdiction. Projects that cut across borders and allow governments a more holistic view on a subject can be useful. IATI, bringing transparency to the entire aid sector is a good example. Also because all actors both have something to lose and to gain by opening data, and it works because those interests and reservations are overlapping in a way that makes the benefits for an actor outweigh the risk of sharing. A project like Open Corporates is another example of how aggregating public data across jurisdictions makes an enormous difference. A very different type of cross border data is earth observation data which can surface illegal deforestation, human rights abuses, war crimes and more. The EU e.g. releases all their satellite data, which allows a peek into countries that might not want to publish anything about it. Then there are the Panama Paper‘s type of leaks that result in national level stories, and the type of ‘open source intelligence’ projects that Bellingcat does. This is outside normal government capabilities to do, and outside their control to prevent or hinder, yet often results in outcomes that are useful to national governments.

All these things depend on data existing around an issue. This may not be the case. Gathering data as collective action can be an intervention in itself.

In order to obtain data out of government knowing the applicable regulations and legal framework very well is important. As well as having that detailed knowledge spread out to non-expert circles. If an agency say a certain regulation doesn’t apply to them, or they can’t release something because of privacy or secrecy concerns, it is necessary to be able to know for yourself if that rings true. Often these things are used to stonewall requests. I’ve worked in a country where privacy protection is often cited as a reason to not share data between government entities. This may well be laudable intent, but in that country privacy laws only pertains to companies, not government. Or an example where the official secrets act is used often to stonewall requests, but it only list a dozen specific types of data it covers, and leaves all other decisions at the discretion of the data owner.

Knowledge of regulations however can also be used against you. An outdated official secrets acts that clashes with more modern rules on information freedom, can be used to tackle a political adversary by reporting a breach of one law, though the act in question was motivated by another law. And then there is the sad and disturbing case of the murder of journalist Jan Kuciak and his fiancée Martina Kušnírová, where his colleagues suspect it was because his FOIA requests were leaked to people who realised he was investigating them.

Towards the end I looked more at what types of data is of primary interest in the context of integrity and anti-corruption. On that list are things like procurement, tenders and awarded contracts, spending (which also means it needs list of government entities), ownership (companies, buildings, which means additional need for addresses, maps), judicial verdicts / consolidated regulations and government decisions.

But in absence of some of that, other data sets might serve as proxies for it. Data less obviously tied to transparency, that still would have echoes of the impact of mismanagement for instance. If maintenance is budgeted but not properly executed and the data on road works is missing, data on road incidents, traffic jams, traffic intensity and flow might tell the story as well. Proxies often are outside the scope of control of misbehaving actors which is an added benefit. Data you need may also be available elsewhere. Many countries share data with e.g. the OECD under international obligations, that isn’t necessarily released inside the country. But it can be obtained from those international organisations. World Bank similarly publishes a lot that may be less easy to obtain inside the country it describes

In summary when thinking about using open data for anti-corruption, it is important to think in terms of the three things that make open data a policy instrument: issues, connected stakeholders, and relevant data.
From this you can explore what is needed to make an issue visible, and who is needed to do that. What action is needed to reduce an issue, and who. What is needed to measure result, and who would do that.
None of this is a silver bullet for corruption, and it can’t be. Corruption has many causes, and regularly serves a purpose too. But open data does play a role in chipping away at it in different places simultaneously. It also allows you to switch focus from one specific situation to another, and every result may lead to additional ones.

Following this we had lively discussion, which continued over lunch.

(During the session I mentioned a wide range of specific examples I encountered or are familiar with, which I largely left out here)

Stop corruption
image by Naberacka, license CC-BY-SA

Kicks Condor dives deeply into my info-strategy postings and impressively read them all as the whole they form (with my post on feed reading by social distance as starting point). It’s a rather generous gift of engagement and attention. Lots of different things to respond to, neurons firing, and tangents to explore. Some elements with a first reaction.

Knowing people is tricky. You can know someone really well at work for a decade, then you visit their home and realize how little you really know them.

Indeed, when I think of ‘knowing someone’ in the context of information strategies, I always do so as ‘knowing someone within a specific context’. Sort of what Jimmy Wales said about Wikipedia editors a long time ago: “I don’t need to know who you are“, (i.e. full name and identity, full background), but I do need to know who you are on Wikipedia (ihe pattern of edits, consistency in behaviour, style of interaction). As Wikipedia, which is much less a crowdsourced thing than an editorial community, is the context that counts for him. Time is another factor that I feel is important, it is hard to maintain a false or limited persona consistently over a long time. So blogs that go back years are likely to show a pretty good picture of someone, even if the author aims to stick to a narrow band of interests. My own blog is a case in point of that. (I once landed a project where at first the client was hesitant, doubting whether what I said was really me or just what they wanted to hear. After a few meetings everything was suddenly in order. “I’ve read your blog archives over the weekend and now know you’ll bring the right attitude to our issue”) When couch surfing was a novel thing, I made having been blogging for at least a year or two a precondition to use our couch.

I wonder if ‘knowing someone’ drives ‘social distance’—or if ‘desire to know someone’ defines ‘social distance’. […] So I think it’s instinctual. If you feel a closeness, it’s there. It’s more about cultivating that closeness.

This sounds right to me. It’s my perceived social distance or closeness, so it’s my singular perspective, a one way estimate. It’s not an estimation nor measure of relationship, more one of felt kinship from one side, indeed intuitive as you say. Instinct and intuition, hopefully fed with a diet of ok info, is our internal black box algorithm. Cultivating closeness seems a worthwhile aim, especially when the internet allows you to do so with others than those that just happened to be in the same geographic spot you were born into. Escaping the village you grew up in to the big city is the age old way for both discovery and actively choosing who you want to get closer to. Blogs are my online city, or rather my self-selected personal global village.

I’m not sure what to think about this. “Neutral isn’t useful.” What about Wikipedia? What about neighborhood events? These all feel like they can help—act as discovery points even.

Is the problem that ‘news’ doesn’t have an apparent aim? Like an algorithm’s workings can be inscrutable, perhaps the motives of a ‘neutral’ source are in question? There is the thought that nothing is neutral. I don’t know what to think or believe on this topic. I tend to think that there is an axis where neutral is good and another axis where neutral is immoral.

Responding to this is a multi-headed beast, as there’s a range of layers and angles involved. Again a lot of this is context. Let me try and unpick a few things.

First, it goes back to the point before it, that filters in a network (yours, mine) that overlap create feedback loops that lift patterns above the noise. News, as pretending to be neutral reporting of things happening, breaks that. Because there won’t be any potential overlap between me and the news channel as filters, no feedback loops. And because it purports to lift something from the background noise as signal without an inkling as to why or because of what it does so. Filtering needs signifying of stories. Why are you sharing this with me? Your perception of something’s significance is my potential signal.

There is a distinction between news (breaking: something happened!) and (investigative) journalism (let’s explore why this is, or how this came to be). Journalism is much closer to storytelling. Your blogging is close to storytelling. Stories are vehicles of human meaning and signification. I do follow journalists. (Journalism to survive likely needs to let go of ‘news’. News is a format, one that no longer serves journalism.)

Second, neutral can be useful, but I wrote neutral isn’t useful in a filter, because it either carries no signifcation, or worse that has been purposefully hidden or left out. Wikipedia isn’t neutral, not by a long-shot, and it is extensively curated, the traces of which are all on deliberate display around the eventually neutrally worded content. Factual and neutral are often taken as the same, but they’re different, and I think I prefer factual. Yet we must recognise that a lot of things we call facts are temporary placeholders (the scientific method is more about holding questions than definitive answers), socially constructed agreements, settled upon meaning, and often laden with assumptions and bias. (E.g. I learned in Dutch primary school that Belgium seceded from the Netherlands in 1839, Flemish friends learned Belgium did so in 1830. It took the Netherlands 9 years to reconcile themselves with what happened in 1830, yet that 1839 date was still taught in school as a singular fact 150 years later.)
There is a lot to say for aiming to word things neutrally. And then word the felt emotions and carried meanings with it. Loading wording of things themselves with emotions and dog whistles is the main trait of populistic debate methods. Allowing every response to such emotion to be parried with ‘I did not say that‘ and finger pointing at the emotions triggered within the responder (‘you’re unhinged!‘)

Finally, I think a very on-point remark is hidden in footnote one:

It is very focused on just being a human who is attempting to communicate with other humans—that’s it really.

Thank you for this wording. That’s it. I’ve never worded it this way for myself, but it is very to the point. Our tools are but extensions of ourselves, unless we let them get out of control, let them outgrow us. My views on technology as well as methods is that we must keep it close to humanity, keep driving humanity into it, not abstract it so we become its object, instead of being its purpose. As the complexity in our world is rooted in our humanity as well, I see keeping our tech human as the way to deal with complexity.

While we were with friends on one side of the Atlantic, other friends such as Jon Husband and Lee Bryant met under the Lisbon son at SocialNow, brought there by Ana Neves, to discuss digital leadership. I need to find out if some of the talks were recorded and got posted already. Here’s a brief recap, with lots of familiar names from our early blogging forays taking on the push for agency and real transformation in the emerging space now that some of the luster of the big tech platforms has gone out of style and seen for what it is.

From the recap the term neo generalists is something I will explore, as it was put forward by Kenneth Mikkelsen in the context of increasing people’s agency.

I need to write more extensively about two things that I for now want to link / bookmark here, both coming from Neil Mather.

One is local-first software, an article by Ink and Switch:

In this article we propose “local-first software”: a set of principles for software that enables both collaboration and ownership for users. Local-first ideals include the ability to work offline and collaborate across multiple devices, while also improving the security, privacy, long-term preservation, and user control of data.

This resonates with me on two frequencies, one the notion that tools need to be useful on their own, and more useful when connected across instances, the other that information strategies and agency in my mind correlate with social distance.

The second thing is Neil’s reference to Gevulot. At IndieWebCamp Utrecht one session took place around oversharing and conditional sharing. Gevulot is a device that allows for very precise contextual sharing, in the SF trilogy The Quantum Thief by Finnish author Hannu Rajaniemi (previously mentioned in this blog).

Gevulot is a form of privacy practised in the Oubliette. It involved complex cryptography and the exchange of public and private keys, to ensure that individuals only shared that information or sensory data that they wished to. Gevulot was disabled in agoras.

This resonates again with information strategies and the role of social distance, but also with how I think that our tools need to align with how we humans actually interact such as flexibly and fluently switching between different levels of disclosure for different aspects of our lives in conversation with someone. That link to a posting on what I’d like my tools to do is from 2006, and my description of a ideal reader more recently is still consistent with it over a decade later (albeit from the reading perspective, not the sharing perspective). Gevulot from now is definitely the shorthand I will use for these type of explorations.

The Irish government started planning for Brexit in 2014, a full 2 years before the UK referendum, and lobbied both EU and Cameron to secure a yes vote. In contrast it seems the UK started debating the impact of Brexit on the Irish border in earnest about two weeks before the 29 March cliff-edge. “Easiest deal in history” and all that.

Bookmarked How the Irish backstop emerged as May’s Brexit nemesis (the Guardian)

Ireland was streets ahead of the UK when it came to planning for Britain’s exit