Friday, October 23, 2015

Promiscuously partisan bureaucracies

by Nick Charney RSS / cpsrenewalFacebook / cpsrenewalLinkedIn / Nick Charneytwitter / nickcharneygovloop / nickcharneyGoogle+ / nickcharney

The title isn't mine, it comes from a piece that ran in Australia's Canberra Times back in 2012 that explains late Peter Aucoin's final essay which warns against what he dubbed the "new political governance". Here's a snippet:
"Aucoin observed further: ''In the environment of [new political governance], moreover, ministers, sometimes explicitly, usually implicitly, expect those public servants who are seen and heard in countless public forums to support government policy; that is, to go beyond mere description and explanation. The expectation is not that they engage in the non-partisan political process, for example, at elections or political rallies. Rather, it is that they be promiscuously or serially partisan …'' 
He observes that ''impartiality remains the official doctrine … Yet, breaches are commonplace. The typical response to … instances of public servants crossing the line of impartiality in support of the government is to view the matter … as an aberration [Canada], or … simply part of the reality [Britain] … or as belonging to that grey area between what is and what is not acceptable [Australia]."

Both the article and the full paper are worth reading, especially in the context of the current transition.

I'll just leave it at that.

Wednesday, October 14, 2015

Innovation is Information

by Kent Aitken RSS / cpsrenewalFacebook / cpsrenewalLinkedIn / Kent Aitkentwitter / kentdaitkengovloop / KentAitken

In August, the Clerk of the Privy Council delivered a speech titled “A National Dialogue On Policy Innovation.” Elsewhere, #policyinnovation is one of the most used hashtags by Canadian public servants. It’s somewhat of a hot topic right now. But what is "policy innovation" in the first place? 

For starters, it could refer to "new and interesting ways of developing policy." Or, "new and interesting policy." (See: On Prioritizing Policy Innovation) We tend to use both versions almost interchangeably, but this post tilts towards the former usage. I’ve heard the term used to refer to crowdsourcing and challenge prizes, deep dives into technological and social trends, improvements to government services, behavioural economics, and much more.

But within that nebulous concept, I think there's a central core to the entire idea that may be a useful way to think about how we gather and understand evidence, and how we make and implement decisions. It's all about information.

More options means more precise application

To back up slightly, let’s consider another arc of innovation that is both an analogy and a predecessor, that of telecommunications. We’ve gone from letter-writing to printing presses, telegraphs, telephones, the internet, and now to low-cost ubiquitous mobile connections. Every combination of one-to-one, one-to-a-select-few, one-to-many, public forums, with every combination of attributed or anonymous, for every combination of formats, all at a vanishingly small cost.

But here's the key: at one point, to communicate long-distance you had one option: handwriting a letter. Later, you had two: handwriting a letter, or paying to have something reproduced many times on a printing press. You didn't have to rely on a letter when it wasn't the best option. As more and more options became available, you could match your communications goal more precisely to different ways to achieve it.

Likewise, now we have a wider range of policy development approaches and policy instruments, which means there’s a greater chance that we can match the right approach to the right situation. We have a wider range of options partially because we get inventive over time, but far more so because policy development and implementation often is communication and so we’re simply piggybacking on telecommunications advances.

The information

Which isn’t much of an insight, I recognize. Yes, the internet opens up options for how government does things. But if we start to think of policy innovation as communication, instead of as enabled by communication, it starts to shed light on what we’re really trying to accomplish, and where “innovative” approaches fit in more “traditional” approaches. Using the terms in quotations lightly.

Basically, the approaches that get pegged as "policy innovation" often boil down to two key actions:

  • transferring information between people
  • arranging information for people

It’s the crux of crowdsourcing, policy or service jams, innovation labs, open data, design thinking, challenge prizes, and citizen engagement approaches like consultations, townhalls, and social media chats. Someone has information that policymakers can use: ideas, problems, slogans, lived experience, or academic expertise (see: The Policy Innovator's Dilemma). Then it’s a matter of finding the best way to access it, which is a question of format. You just have to learn the formats. Similarly, once you've crossed the threshold and learned a new telecommunications approach (case in point might be parents and grandparents on Facebook), it becomes part of a passive mental algorithm that takes a need or goal and instantly knows how best to accomplish it.

Talk of policy innovation tends to go hand-in-hand with the idea that policy issues increasingly cross jurisdictional or societal boundaries, and are a part of an increasingly complex environment (see: Complexity is a Measurement Problem or On Wicked Problems). Which is where arranging information becomes invaluable.

Let's say  you get ten informed stakeholders of a given policy question in a room, and ask each for their concerns. They each reveal a different way of looking at the issue, revealings its complexity and pointing out legitimate pitfalls for policy options. The problem is that by the time the tenth stakeholder spoke you forgot the concerns of the first five, so it's impossible to understand all ten in context. It's Miller's Law: human beings can only hold seven things, plus or minus two, in our working memory. Which is where techniques like journey mapping, system mapping, and sticky noting everything are crucial for policy. They're the policy landscape equivalent of doing long division on paper so you can remember everything in play - what we might call mental scaffolding

Many approaches include both transferring and arranging information. For instance, a public consultation might include a call for ideas with a voting mechanism that creates a ranking, signaling importance. Some deliberation platforms include argument mapping systems that use algorithms to arrange the discussions for participants, almost like Amazon bringing complementary products to the forefront. ("Are you outraged at your government about X? Many people outraged about X are also outraged about Y, perhaps you should consider lambasting them on that topic too.")

In other cases, governments can (and should) map out what they already know about a given policy issue to get it out of working memory and focus on change drivers and relationships between forces. This will become increasingly important if we truly want to get out of siloed policy-making, find hard-to-see connections between once-distinct policy areas, and genuinely understand entire systems. Our governance model was built for a world we falsely believed was simpler than it was, and within that we're running into our own cognitive limits. We literally cannot hold all the elements of a complex policy issue in our heads without some kind of mental scaffolding, be it tools, other people, or paper.


Two notes on metadata, or information about information (an example would be how DSLR cameras automatically include date stamps, aperture, shutter speed, iso, and more information in image files).

First, some approaches that get lumped in with policy innovation don't fit perfectly with the transferring and arranging information categories. Behavioural economics, for instance (and its service delivery cousin of user testing), seems more like creating new information through research. But viewed from a policy lens, I'd suggest it's actually more like metadata.

Let's say government wants to maximize the rate of tax returns, so tweaks the language on letters to taxpayers to see what framing resonates with people. Here's the UK example:

"...replacing the sentence “Nine out of 10 people in the UK pay their tax on time” with “The great majority of people in [the taxpayer’s local area] pay their tax on time” increased the proportion of people who paid their income tax before the deadline."

The core policy instrument here is a law, and the letter sent to taxpayers is supporting education about the importance of filing tax returns. In this case, the information is in the letter. The behavioural economics piece is metadata about that information: how many, and which, people acted upon the information they received. It's still really about transferring information between people, which puts tools like behavioural economics and data analytics in this common framework and may help practitioners navigate between possible approaches.

Second, there's a meta-level to the idea of transferring and arranging information that changes the value of different approaches and formats. We might call it "conspicuous innovation" or "conspicuous engagement." Basically, the transfer and arrangement of information is not the only goal achieved by these approaches - someone emailing a policymaker a vital piece of information for a policy question is worth less than that same person posting it publicly during an official consultation. The metadata for that piece of publicly posted information includes the number of views from other people, the signals about government's attitude towards governance and transparency, and the future value to others. 

So what?

The "policy innovation" toolkit centers around two actions: transferring information between people and arranging information for people. Past this common core, it's often a question of forums and formats (increasingly, but not uniquely, about how we transfer information from non-governmental actors) (with exceptions, of course). So what?

One, I think it's worthwhile to examine what binds the idea of policy innovation together, to refine our working concept of the term.

Two, I think thinking in these terms highlights what we're actually trying to accomplish through these approaches, and might make it easier to choose between them.

Three, putting them in a historical context puts the perceived risk in context. I mean two things here: first, that policy innovation is very similar to our personal experience with telecommunications advances: more options allows more niche approaches, and eventually they become routine. Second, that if some of these approaches are at a fundamental level analogous to things government has been doing for ages, they seem less daunting. For instance, there are dozens of consultations ongoing at at any given time. It's just a different way of transferring information between people and policymakers.

Thank you to Blaise Hebert and Nick Charney for super interesting conversations on this topic.

Also, two recent posts from Melissa that are good general fodder here: What Innovation Feels Like, Part 1: Fear; and Part 2: Lack of Trust

Friday, October 9, 2015

Cognitive Government, Artificial Intelligence and the Future of Collaboration

by Nick Charney RSS / cpsrenewalFacebook / cpsrenewalLinkedIn / Nick Charneytwitter / nickcharneygovloop / nickcharneyGoogle+ / nickcharney

"By 2020, the cognitive technologies–machine learning, natural language processing, speech recognition, and robotics–start to augment the government workforce and improve the quality and efficiency of government systems. These technologies bring forth a range of applications in government including law enforcement, transportation, healthcare, and even fraud detection. More importantly humans remain “in the loop” not only to develop, customize, and train the systems; but also oversee, guide, and improve them." - Gov2020 Deloitte
Cognitive government (as described above) is one of the mega shifts identified in Deloitte's Gov2020 research project. Its something that Bill Eggers and Paul MacMillan elaborated on for Nesta's blog back in May, arguing that cognitive government requires open functionality, applied learning and adaptive-rule making and asking:
"How can governments shorten their learning curve to more effectively adapt to the technological changes that surround them?"*
Eggers and MacMillan seem to think that advancements in Artificial Intelligence (AI), Cognitive Systems and Machine-to-Machine learning holds the key to achieving fundamental changes to the architecture of government; and the more I think about it the more I tend to agree.

Enter Artificial Intelligence (AI)

To date, we've largely thought about our information systems as static repositories of information rather than a dynamic AI. This type of thinking has informed procurement decisions, operational decisions, and shaped the very flow of information in and thus knowledge contained within our organizations. But what if our records and document management systems were built around a dynamic AI rather than a static repository?

Cognitive computing is supposed to accelerate and enhance human expertise; capture the expertise of top performers; improve decision making; and scale easily alongside both the information supply and demand. Our current information systems do none of these things, instead they rely on the user to classify, query, judge the validity of information and bring it to bear.

What if we had cognitive systems that are able to put the entire corpus of a department's information base into context and provide confidence-weighted responses, supporting evidence and map any related actors, polices, regulations, and services?

What if the system continued to learn as new information was inputted? If it could gauge and determine the validity of a given information resource based on who produced it, who was consulted, how quickly or far it moved through the system, and whether it was ultimately approved or set aside. What if we took a more enterprise wide approach and linked departmental AIs together?

Imagining the future of collaboration

What if – to take a real world example – we had approached the web renewal initiative with an AI (rather than Content Management System) in mind?

Imagine all of the government's information in a single window that learns not only from how the civil service organizes its information, policies, services, and regulations but also how citizens search for, consume and interact with them online. The AI could automatically prioritize related information for users as they search, highlight seasonal information as it is a priority (e.g. tax filing, voting, etc), and remove redundant, outdated and trivial content (ROT).

This would take co-creation and collaboration to a whole new level as the AI brings governments and citizens together in a way that is otherwise impossible to achieve. We may not think of this as co-creation or collaboration in the contemporary sense of the word but it surely entails at least some small but important part of the future of both.

*Caveat: Melissa picked up on this point in her wildly popular, "A government that learns by design"; it is worth reading if you have not yet done so.

Friday, October 2, 2015

What Innovation Feels Like (Part 2: Lack of Trust)

by Melissa Tullio RSS / cpsrenewalFacebook / cpsrenewaltwitter / creativegov

Public servants go through a rigorous process to become full-time, permanent employees. I won't get into detail, but it usually requires a lengthy HR process with at least two to three stages of vetting to find the right person for the job. And then, if you’re one of The Chosen, you’re on the inside. Yay, you; your knowledge and expertise were tested against dozens, sometimes hundreds, of other candidates, and now that you're inside, you can get to work and start making use of all that creativity, energy, and expertise you've been specifically chosen to provide. Right?

The experience on the inside is very different than what you expect. When you try to provide that expertise, you’re beaten down by process, by politics (big-P), or by conflicting personalities (little-p). You thought you had something to offer the world, to make things a little bit better for people you’re serving, but by the time you’re able to get something you create through the processes, it doesn’t at all resemble the passion and energy you put into it.

When good people are crushed by process

Most of what we do as public servants resembles the following.
Step 1: Create this [product]. Use your knowledge, expertise, evidence, research, and best advice to create it.

Step 2: Next, slap this template approvals form on top, which requires (minimum) 10 signatures to be approved, and get every single pair of eyeballs listed on that sheet to give you their two cents on it.

Step 3: Use outdated linear proprietary approvals mechanisms to track and incorporate remarks, changes, vague comments, and illegible handwritten markups from all parties involved. Update. Revise. Update. Revise. Co-ordinate/negotiate changes among parties.

Step 4: Lose track of version (due to outdated linear proprietary approvals mechanisms). Backtrack to find original version buried in an email. Re-incorporate all changes you just lost. Repeat.

Step 5: (Miraculously and with much lost sleep) Meet deadline, feeling mangled. Start at the beginning for the next one.
Don't get me wrong; the product we start off with isn't ever perfect, and none of us should be immune to critical feedback, or fresh eyes to take a look at something to make it better. But if you step back and look at the baked-in approvals process, underlying it is an inherent lack of trust (perceived or real, which is basically the same thing to the experts that are hired to provide their best advice).

My Big Idea

Earlier this year, I pitched an idea to change the way we do approvals. It's an interesting play off of one of Kent's posts from the summer – see: Government, Citizens, and Power. What I proposed (without realizing it) would be something like applying the IAP2 spectrum internally to our processes by empowering staff to have a real stake in outcomes and decision making in government.

This approach would start with cross-functional/cross-ministry teams (5-7 max) of senior advisors – those responsible for developing products/policies at the ground level. Some authority would be relinquished from decision makers so that teams are expected to reach all final decisions on the products/policies they developed (ie, ditch the approvals sheet/mechanism all together).

The role of deputy ministers, assistant deputy ministers, and directors would shift: they'd be expected to provide feedback and direction as an advisory committee (which would be decided on before the project begins, meaning leaders from different ministries might be assigned to certain projects, depending on the nature of the work). This advisory body would check in periodically with the project team, and the feedback they provide would be more holistic, bringing in perspectives from all ministries/areas identified as "leads" on the project.

Managers/supervisors would be included on teams as quality control agents, and to ensure advisors have all the information and context they need for any piece of the products/policies. They would also play a facilitating role, determining who teams might need to work with to do their work.

An early sketch of the project flow looked like this (I'd revise this and move DMO (deputy minister's office) and legal to the advisory committee):

I'd also add to this: project teams and advisory committees would include experts (lived experience counts) from outside of government, too.

There's more to explain about how I see this proposed process working in real life (hint: we'd have to change how we do our work, too), but I'll leave it for another day and another blog post.

Let It Go

(#sorrynotsorry for the earworm.)

More and more of our work in government these days depends on all kinds of collaboration: between ministries/departments, and beyond our own level (municipal, provincial, federal) or sector (private, non-profit, community-based/grassroots). The approvals system I described at the beginning of this post does not work any more. It is seriously broken. And what needs to replace it would depend entirely on trust.

Trust means letting go of control, and the current model favours (and rewards) control freaks and micro-management.

Trust means letting go of authority, and the current model presumes that traditional power structures have the final say in everything.

Trust means letting go of fear, putting risk in its place (ie, risk management rather than risk aversion, as a rule), and experimenting with stakeholders instead of believing we have all the answers ourselves.

Richard Pietro, self-proclaimed open government fanboy and all around amazing human being, recently produced the world's first short film on open government, open data, and open source. I'm going to spoil it for you (you should watch the whole thing) and skip to the end. This is what trust feels like.
"You have to let it all go; fear, doubt, disbelief... open your mind."
...and open the processes, starting with the premise that we trust the people we so painstakingly selected for the jobs we've asked them to do.