foreign
[Music]
I'm here to speak to you today about my
vision for better government
this is about a
type of government which is accountable
is able to demonstrate how all decisions
are made how all actions are taken how
all policy and services and everything
that government administers is done in a
way that is demonstrably lawful
demonstrably
um
aligned to the Mandate and accountable
actions of that government I want to see
a government that is participatory where
the people the public the communities
the families can not just
um deal with policies and services once
they're launched but can be an active
contributor to or even razor of new
ideas of new policies of new things that
they need to live well and can
participate not just in the design of
such policies or services but in the
governance and oversight I'd love to see
a government which is more Humane which
brings our whole person to the job which
brings human measures of success
Community measures of success which
brings values into the way that
everything is done in the public service
how can you have Humane outcomes if you
don't have a Humane workplace and Humane
policy making and Humane ways of working
I want to see a public service and a
government which is adaptive which is
not just sit and forget but is
constantly monitoring for and
understanding and receiving feedback
from the community on the impact of
change so that then we can truly take an
iterative approach that is based on
evidence based on reality based on the
actual effects it's having on people so
that we can constantly guide ourselves
towards a better North Star
and I want to say of course Public
Services which are accessible I want to
see a situation where everyone knows
doesn't just have access according to
their needs and but also has access to
their rights
um where everyone has access to Justice
everyone has access to everything they
need to live well
part of this is about all of us
recognizing that you will always get
something which is lower than your
expectation so if you start with a low
expectation guess what you're going to
get something even lower than that if we
start to raise our expectations of
government of each other of public
servants
if we start from a why isn't it the best
possible action the best possible way of
working when you raise your expectations
you have a chance of changing towards
that raised expectation
when you start from a low expectation
you're going to get what you expected so
I'd like to encourage you all to start
to raise your expectations of government
of public sector and of society so that
we don't end up with a situation where
cynicism desire defines the North Star
policy is this weird word it means
literally pretty much anything for some
people it is a couple of dot points on a
napkin for some people it is the promise
that the government of the day said for
some people it's legislation and the
simple answer is actually all of these
things
the one thing that I would suggest
particularly to a crowd like this is
that policy is like the operating system
for our society
it sort of determines the rules
determines what's available to you it
determines the support mechanisms it is
it's a series of libraries that we all
draw upon every day even if we don't
notice it when we drive our own cars
when we drive public transport when we
use the education system when we use the
health system even just
um you know catching up in a location
like this there are many many policies
that affect us every day and and that's
why it's so critical we get involved in
it so this is my very cheeky way of
describing what policy looks like so for
those
um who can't see the screen I've got a
cartoon here and it basically starts
with the policy team are celebrating
they're saying another great job this is
the current state of how policy is made
in Australia generally and in a lot of
countries great job we got it out the
door now on to the next policy and in
the back of their minds the policy team
are thinking wow I wish we had time to
explore policy Futures to understand
what this looks like even to see what it
means in implementation
but the policy instructions are thrown
into a black hole the black hole of
policy intent and from that black hole
from which purpose is stripped out those
instructions are received by someone to
implement it it might be someone in a
company that has to consume a regulation
it might be someone in a social services
organization that needs to build a new
Public Service it need it might it could
be by anyone who has to consume these
rules who are sort of saying what does
this mean well unfortunately the team
that wrote it on to the next thing and
so let's just do our best so there's a
lot of guessing
and the people actually affected by bad
policy
literally it can ruin people's lives as
we've seen in recent months through
various rural commissions
so what we end up having is this black
hole policy impact
no one knows what the actual impact of
policy is and the people that are
generally measuring policy are looking
to measure the things that they expected
not looking for the unintended
consequences so it can be devastating
and of course eventually you get an
evaluation either an evaluation team or
a royal commission which will eventually
say
there was low intent policy intent or
indeed if there was high policy
realization there was harm created I
wish they'd evaluated Turner but of
course that's always the answer let's
just evaluate sooner and the problem is
that an evaluation is a point in time
but it's not feeding into constant
iteration is it in the tech sector we
all shifted years ago now into
continuous Improvement for systems for
products for services
but there's no equivalent yet in policy
so what we've got is a situation where
there's a Gap a gap between policy and
delivery
um and it is a big gap and just to give
you an example of um the sort of stories
I remember going to a regulatory
conference a Regulators conference and a
regulate tour got up and said there's
nothing wrong with our policy our policy
was perfect it was the implementation
that was the problem
and I got up straight after this person
said has one of your besmirched
implementation people I could have told
you that when you did regulation for
heart monitors which is the regulation
he was talking about you know anyone
with a technical band could have told
you that open wi-fi was a bad idea
um the fact is that every piece of
policy every regulation every
legislation is being implemented in
technology today
which means if you don't have
multi-disciplinary teams if you don't
have technologists together with data
together with social activists together
with um you know highly diverse
perspectives and experiences in the room
when you're developing policy then
chances are first of all it'll probably
miss the mark and chances are it will
probably not be fit for purpose and
chances are it will probably create
unintended
consequences because you're developing
an isolation from those who can feed
into it and of course the Blindfolded
baton passing between policy and
delivery means that the people doing
delivery can't feed back into policy so
you end up with a stagnant artifact very
quickly this is my dream again my future
we're on the way to getting there but
policy options and Futures are
continuously being explored continuously
being raised not just from the policy
team or from the government of the day
but from the public
here's a change I've noticed a bug why
can't we actually have bug fixing of um
of policy of services Etc from the
public
multi-disciplinary disciplinary policy
development where people and policy
teams and implementation teams are
co-designing not just the implementation
but the policy intent the instructions
the success criteria and where you're
actually monitoring and baselining not
just the policy intent but the human
impact from the start
and then you can be constantly
monitoring that proactively managing
that and everyone working from the same
policy infrastructure where you have
policy as code so a policy twin a
digital twin of policy combined with
impact monitoring combined with actual
escalation so when things go wrong which
they will you notice and you can do
something about it and people don't need
to have their lives ruined
so changing things starts with
reimagining good we currently have a
highly manual process where policies are
ideated quite often in political
officers a policy announcement is made
usually in the absence of any actual
evidence then someone goes and develops
the how can we create options to meet
this policy announcements legislative
drafting is very slow and very manual
and and I've literally read legislation
where it's got a triple negative over
the course of 10 pages and years later I
spoke to that drafter and said
why and his answer was fascinating to me
he said I was just having a bad year
like
the fact is
come to write these things is so far
from good it's not even funny so the
Parliamentary process will always be
slow and a lot of debates and you know
we can't really stop politicians being
politicians but publishing is fast but
once you have a new piece of legislation
or regulation art
unless you're the sort of person that
goes looking for and subscribing to
those changes who knows you've got to
discover it you've got to interpret it
no one tells you when you've got to
write only when you got it wrong so you
get Myriad implementations Myriad
um interpretations and implementations
um and um and there's no Assurance of
whether it's the right thing
I see again a situation where policy or
ideation if you actually had a policy
twin where anyone including the
political officers could actually model
and try things out you can actually
draft new regulation legislation as
human and machine readable
simultaneously not one then codify that
into the other but actually implementing
at the same time a reference
implementation so you can feed the
insights of that back into the human
version and then you get something which
is actually good
imagine if um at the point in time where
a new piece of legislation regulation
was passed in the parliament the API was
up like that imagine if at the point of
Consulting on that prior to it even
going to Parliament you could actually
have a reference implementation and test
it there's so much you can do when you
actually start shifting to a
digital and human approach to drafting
these rules and we can start to
dramatically improve the quality and the
integrity and the consistency of how
they're applied
so there's lots of bits to this this is
my mind map it's quite overwhelming I'm
so sorry you're starting to see the
brain of peer culminated over seven
years so sorry
um but here are sort of the enablers for
that better pathway right so I'm not
going to go through all all of these
today in fact I'm only going to touch on
two today but just really briefly if you
want to get persistent impact monitoring
informed and continuous policy reform
based on those impacts you need to be
monitoring you need to have publicly
available modeling tools you need to a
human impact measurements what do you
actually measure what does quality of
life look like and of course that's
going to get very diverse when you start
taking different knowledge systems into
account
actual public engagement and policy my
favorite participatory policy story is
from Taiwan where Uber did their usual
backdoor Brute Force try to get you know
regulation to get them to do whatever
they want
um but the way the government there
works is thank you for your suggestion
we're going to ask people
and then they put out a how would you
feel about Uber coming into Taiwan how
do you feel this is something we're
really terrible at in um in the west uh
we sort of start with what do you think
and the problem is what you think is
people get married to their ideas very
hard it's very hard to shift an idea but
shifting a heart is actually a little
bit different how do you feel draws out
people's values and concerns and hopes
well I feel like it could be a good
opportunity to make some money I I'm
worried that
um you know someone's going to hurt me
and then there's not going to be any way
you know any recount recourse for that
it draws out everyone's hopes and fears
and then you can start to draw out well
what does that mean and then you then
what they did is invited the general
public to participate in live
legislative drafting
live streamed live legislative drafting
how cool does that sound right
um and then and they said to Uber you're
welcome to come or not we don't mind
um and so rather than it being done in
back you know back rooms by experts and
lobbyists it is actually done in the
open this is again a dream
so public engagement
um my favorite
um piece of legislation in the world is
um again total nerd here
um is the right to an explanation is it
well utilized or no no but in New
Zealand in the foi equivalent over the
oia they have a thing called section 23
every citizen every resident in New
Zealand has a legal right to an
explanation about any decision made
about them by the government what a
beautiful piece of law that would
certainly change things quite
substantially here
um human and unique policy and human
indicators you need a public repository
people can share stuff you need
escalation mechanisms test Suites
baselining policy as code and continuous
monitoring so I'm only going to look at
these two together so don't worry we're
going to break this right down but I
wanted to give you if you like the whole
picture and why they um pretty much all
of it does end up coming back to a
Reliance upon policy as code
my origin story in this started at um
oztrack austrack is the
um one of the Australian Regulators of
the financial sector and they look after
things like anti-money laundering and
counterterrorism financing regulation
and it was really interesting going
there because a bunch of problems became
really evident really quickly and that's
why I got onto this whole rules code
Journey one of which was everyone's into
you know has to go and read and
interpret and a lot of these
principles-based regulations people are
like oh what does that mean I don't know
let's try did I get this right I'm not
going to tell you oh we're just going to
find you when you get it wrong so
there's a lot of angst and a lot of
stress
so the seven problems I identified were
first of all we just need better rules
we need rules that are more easy to
implement model Etc because that
translation Gap I spoke about before so
I joke a lot much to my ex-lawyer
husband's annoyance
um I joke a lot that we are currently
using lawyers as modems
we're using lawyers to translate between
analog and digital to sort of say what
does this legislation mean and how
should we implement it we need to adapt
to digital there's a lot of rules like
the example about the heart monitor
where they're just not making the
assumption that things are going to be
done in technology so they don't have
any Geeks in the room they don't have
any technologists informing the um the
policy design so we need to create the
understanding that everything that we're
building is going to go into a digital
system at some point so we need to take
that into account
a lot of regulations assume it's shaping
a human behavior but of course that's
increasingly not the case and a machine
doesn't care about a Legal Financial or
criminal pressure so if you're building
a regulation to shape human behavior and
then the core the main consumer of that
rule is actually machine then you're
probably not going to get your outcome
um a lot of people just building
rebuilding the status quo with shiny new
things they're just speeding things up
and running off a cliff
and there's a lot of disincentives so
they might have a key goal but they're
secondary goals become the primary goal
very quickly and and then they miss the
outcome so adults track one of the key
goals is to strengthen the financial
system against abuse but then the
secondary goals were to detect and to
disrupt Financial crime fine
all of the measures of success when I
was there this is a while ago now were
about how many criminals did we arrest
and how many
um you know non-compliance financial
institutions did we fine and the problem
is that both neither of those are
naturally incentivized to strengthen the
system against abuse both of them are
you know naturally incentivized to have
a weak system so you can actually arrest
more and and find more
so perverse incentives are a problem but
number six is Trust of course government
is a um is not something that should ask
for trust that needs to earn trust and
there's all of that context where people
are saying well you know why people have
happy to share data with the social
media or or um you know big Tech or
whatever and the simple answer
realistically is because those entities
don't have the state Monopoly on
violence like it's fairly simple answer
they can't come and arrest you or take
your children they can do some pretty
awful things but they don't directly
have the powers and stressors the
government does
so and when you lose trust you lose
stability in the whole society so it's a
really critical problem to try and solve
and finally policy impact so I'm
actually helping kick off a research
project at the moment to look at what
the unintended human impacts of public
policy are starting with Robo debt but
also looking at working parents and all
kinds of other social policies to try to
actually say see you can measure and see
when there's an adverse impact on a
community and you should be able to
understand that flag it and escalate it
and mitigate it without having to wait
for a commission
somebody jump in rules this code then
I'm going to give you some examples
what is what sort of rules are we
talking about
um
a lot of legislation and regulation
policy is made up of two types of things
there's prescriptive rules and judgment
based
the prescriptive rules are the sort of
things you want consistently applied so
those are the ones that fit this really
well so things that are eligibility
criteria calculation information things
that are about
um
um you know over ten thousand dollars
needs this action his or this obligation
based on this trigger then they're a
judgment-based ones so you might meet
all of the prescriptive rules for
instance to get citizenship but then the
Judgment based is that you have to pass
a good character test which obviously
changes over time and is is very
judgment based so rules as code really
fits prescriptive rules really well it
shouldn't be used or I don't believe it
should be used for prescriptive rules
for judgment-based rules and that
certainly has played out in all the
places that we've applied it
so it fits really well in Service
delivery it fits really well in social
policy and Taxation policy in a lot of
the the quite prescriptive rules and
regulations that you deal with all the
time
um I've spoken about a little bit the
why
um I have a couple of favorite tools I'm
going to talk about open fiscal in a
second but there's another good one
called blocks that you can check out all
of this is available
what is not rack though this isn't a
source of constant argument
um there are definitely people who are
like oh just you know just create XML
structured content doesn't make it
machine understandable if it's not
machine
executable then um and of course using
the word executable and policy in the
same sentence is deeply uncomfortable
um but structured content is not rules
as code because if a machine can't parse
it then then it's not actually machine
it's not rules as code it's just rules
as structure uh it's not automated law
so anyone who's trying to and there's
plenty of tools that like this who are
like oh it's all right just push your
your rules into our system it'll just
automatically translate them or it'll
just interpret them
um that's not rules as code because of
course you've still got a black box of
decision making it's not a traditional
rules engine traditional rules engine
have business logic and the rules mashed
up which means that any change is very
very complex as anyone that's dealt with
rules engines knows it's not an AI it's
not a website it's been surprising to me
how many people don't and particularly
when you start working with policy teams
they don't understand technology they
don't understand the difference between
a website and rules as code
um and because lots of people are trying
to sell into this place at the moment
into this space at the moment they're
sort of like oh well someone just sold
me this tool last week it's like yeah
but it's still a black box still can't
see the rules still can't trace the
decision back to the actual legislation
and see if it's actually lawful
um so
excuse me for a second okay all right
I'm done okay there's lots of current
use cases I'm going to show you some
examples in a second rather going on but
step one for actually codifying rules is
just understanding it read the
legislation do some concept modeling so
that if and it's been fascinating with
completely brand new regulations like
I've been involved in drafting when you
say look we're not even going to try to
draft it at all yet don't start writing
it we won't start coding anything yet
let's just step back and do a concept
diagram let's just look at what this is
supposed to achieve and even just going
through concept modeling has been able
to flush out a whole bunch of
assumptions use cases oh but if what if
you know test cases that would would
break it so that's been really really
helpful getting into then saying well
what would the human readable version
look like and what might a machine
readable version look like starting to
draw that out gets um actually then
really helpful so you have a what they
call isomorphic drafting
then there's lots of opportunities to do
modeling and all kinds of other tools
I'm actually going to show you a bit of
a demo now
so yeah read the legislation identify
the logic create some test cases and
then start codifying in your favorite
tool so here are some examples I want to
give you
in New Zealand there's a thing called
the rates rebate act rates rebate is
ostensibly very simple if you are a
owner occupier
the the policy was built to try to
encourage
um particularly retirees to stay in
their homes for longer right but no of
course no one was measuring whether that
policy intent was being met whole
different point it's a really simple
piece of legislation as you can see my
favorite bit though is here the actual
calculation
all right payer who was a great payable
residential
is entitled to so much of the rates of
people for that rating year in respect
to the property as represents two-thirds
of the amount by which those rates
exceed 160 reduced by
one dollar for every eight dollars
by which the rate payers income for the
preceding tax year exceeded that amount
the last mentioned amount being
increased by 500 in respect to each
person
who's dependent of the rate payer the
comments on the right thing okay so you
can see even simple regulation gets
quite interestingly annoying
that however because I'm a maths nerd as
well just turned out was a quadratic
equation which is cool so I was able to
just codify that fairly simple sorry
no
um so what we did with that was the
actual
um
coded version we used a tool called open
fisca I love open fisca it is
complicated to get to know but the
reason I like it so much is it doesn't
just provide rules as code for services
it provides the basis of being able to
do a whole of population modeling of
change which we've done now in three
countries where here's a policy if I
make this change to the policy or to the
amount or to the variables or to the
eligibility or to the rates
what impact will that have on the whole
population we've been it's been such a
good tool for exactly that purpose so it
sort of breaks it all down each one of
these is a variable which is then you
know broken down in each of the
variables and the act of that actual
calculation then is right here rates
total minus initial contribution
minus divided by divided by 3 times by 8
plus allowable income yay
um what that then so the sorry I
shouldn't yep no I'm going to run out of
time if I keep going so
um each one of the variables is defined
this is another key thing that I love
about this particular tool is you can
have all of the years
so you can um rather than just having
the current legislation you can keep the
rates over the years you can keep the
history of the change to the regulation
or to the legislation so you can see oh
look at how people are affected today I
can run the same demographics against
people 20 years ago and I can see the
impact look I can now see trends of
who's getting more and less money over
time I can now see Trends about
um who's being affected the size of the
cohort the affected by a policy based on
the eligibility criteria Etc so this
ability to actually maintain history is
actually quite helpful
um and then uh then you build test
Suites this has been the most helpful
thing for engaging with policy people
because if we can say wouldn't give them
this exact thing but if we can say look
here's an input here's the output that
we think we should get is this right
they go and then do their manual
calculation they say okay yes we can
assure that these 10 test cases are
right great so now every time we run the
code and we show them look your 10 test
cases did come out as true are you
comfortable and they're like oh actually
yeah that makes me feel a bit more
comfortable so test it really helps to
get the policy people engaged but it
also helps to flush out with sometimes
where their assumptions are not right
that's then used by a bunch of different
services including this little rates
calculator so you can say how much rates
anyone from New Zealand want to yell at
their rates
cool 3 400 a year perfect how many
dependents same person
same person zero perfect thank you next
question so this has already done a call
to our open fiscal backend because one
of the things we found is a lot of
superannuates don't know how much they
earn exactly and it's actually quite
um undignified for them to engage in the
process so being able to say look if you
earn less than this amount you're going
to get the full amount perfect okay you
get the full estimated amount if you
earn more than this amount you're going
to get zero because we had a lot of
people who were going through all the
pain of applying for this particular
benefit of this particular rebate to
then find out they were eligible for a
dollar fifty kind of thing or somewhere
in the middle
um I won't I won't throw you into it
I'll just oops I'll just pick a number
somewhere between there 43 000.
and and then it says okay that's how
much you will likely get so this
actually just having that calculator
available meant that thousands thousands
of retirees who were it was such a
stressful
um process and they were getting almost
nothing out of the process um could sort
of say oh a this is worth applying for
or it's not and suddenly they could make
a choice in their life this is about
trying to reverse that helpfulness of
government rather than we're going to
make it super hard and if you finally
jump through our Hoops you might get
something it's more about how can I help
you figure out everything that you are
entitled to so as a quick follow-up to
that given I'm almost running out of
time a little project in New Zealand
that a few of us recently did we took a
bunch of benefits and we said well why
don't we just encode all of them and all
of these are available in open Fisker as
well
and we said okay
um I am this I am 24 year 23 years old I
am currently without work I'm this this
is this I have let's say none for the
situation
[Music]
okay that'll do ah I forget postcodes in
New Zealand someone help me out
58 19.
that's all right so long as it's a legit
one then that'll be fine so being able
to then say okay what you've told us and
what you're eligible for and and roughly
how much that's worth for you
these and particularly the accommodation
supplement it is almost impossible to
figure out what your accommodation
supplement is in New Zealand it is
because it's based on where you live on
your amount on your dependence and a
whole bunch of stuff that people find
very hard to figure out so this is
already being used a little project done
by a bunch of just a little citizen
group a bunch of um largely volunteers
working with citizen advice Bureau to
build something so people could just
figure out what they're entitled to and
very cheekily
how can I question a decision to help
people say ah and here's an email
template if you want to challenge a
decision
[Applause]
yeah I'm not planting grenades at all um
so
um there's lots of examples I think
you're already starting to get a bit of
a feel for this in Canada we did a
policy Difference Engine where we were
able to show if you made this change to
this regulation here are all the people
that will do better and all the people
that will do worse
it's so important to be able to make
those kinds of calls and make those cut
and help make those kinds of informed
decisions so and there's lots and lots
of papers I've got them all linked so to
jump back to my slides briefly and then
we'll do some questions
[Music]
um
what you end up with is better rules as
a process this idea of isomorphically
drafting taking a test driven approach
to drafting new policy new regulation
and new legislation specifically
then being able to provide a reference
implementation publicly API dot
legislation.gov.eu dot New South Wales
dot Etc is the dream because anything
that is less than an API is probably not
sufficient
um the whole point is to draw a line in
the sand so that we can start getting
better rules so that we can start
holding up all of our legislation and
regulation in a way which is consumable
actionable appealable understandable by
people more broadly
um and I'm gonna jump past painful
principles but this is a key one as well
AI
there is of course a lot of automation
happening in government and it is
terrifying
um even as a person who works in
government almost all the time it is it
is terrifying so for me digital
legislation and regulation is I
genuinely believe a precondition for
lawful AI because either you have a
system where your software is consuming
the rules which would be good because
there's a whole bunch of you know
rules-based AI That's 30 years old that
works perfectly well it doesn't have to
be a machine learning based AI but even
if you use machine learning based being
able to test the output and the input of
your system against the law is really
important so that it's you know lawful
um the other thing is so I've written
the whole paper about how to create
trustworthy and legitimate use of AI in
government particularly around ADM and
um and the other part of that is rule of
law like people don't think about
administrative law enough which should
explain your decision but rule of law
also holds that you should get a
consistent outcome you know all people
should get the same consistent outcome
and if you've got a machine just
changing the output over time because
it's low learning but it's not a person
accountable for that shift then that can
also get quite dangerous as well so
Human Services need human measures human
Futures and human participation
participatory governance I've given you
the Taiwan example but there's also a
huge amount of opportunity around
drawing on indigenous knowledge systems
so that we're not just planning for the
next financial quarter or the next
electoral cycle but we're actually
trying to plan 50 and 100 years out so
we get values based Futures we're
working towards and the last I'll say is
my epiphanies of over this time seven
years of epiphanies boiled down to one
slide here you go
rules and business logic need to have an
amical separation
every one of you that's worked with
rules engine how many people have worked
with rules engines
not many some of you've been spared that
pain so rules engines are so hard
because someone over here says here's
all the rules in the business
requirements and the business analyst
goes ah here's what it all kind of means
and then someone codes it all up and
then there's a change
and then you keep having to jump through
the cycle of the change coming out
having to refactor all of your business
logic and then re-implement into your
rules if you actually separate your
business logic and keep the business
logic in your applications and just have
the rules as good just rules as code
then just the legislationist code that
you can consume by all these different
applications then suddenly you have a
lot more consistency of How It's applied
but also easy of Mana ease of management
when change happens
I think legislation and regulation is
part of the digital public
infrastructure that we need there is a
lot particularly with any you know
emerging Trend a lot of snake oil sales
people so worth keeping an eye out for
and a lot of people who are not snake or
sales people they're just driven by
their own thing there's a lot of natural
learning people who love this space
because they get to apply all their
skills but if you just build a tool that
just keeps
um parsing badly drafted regulation
legislation in the first place you're
actually propping up a system that
itself needs to change
there is a strong Coalition of the
Willing globally worth getting involved
in and anything short of API that
legislation the government is not enough
people who are like oh technology
doesn't really matter you just need to
have a good you know
um
um you just need to have a good concept
model architecture
so I've got a whole bunch of links here
A whole bunch of materials whole bunch
of case studies I'm going to stop and
take some questions I hope that's been
informative and useful thank you
thank you so much I'm sorry if I rushed
you to a towards me and that was just
absolutely fantastic do we have any
questions to start us off
results
there's one down here I think Russell
are you heading towards anyone Russell
or have we got one here
it's all right I can
I've got two actually we'll start with
you
um fantastic talk I'm trying to work out
how do you engage governments that
aren't already on the bandwagon like
you've shown some examples with the
rates calculator and whatnot there's
evidently some engagement with
government to actually use this process
to get there so how how do you bootstrap
that like if you are interested in
having better government how do you get
them to do this cool so I'll give you
three strategies just real quick
um
because policy and delivery are
physically separated between a lot of
departments if you're dealing with an
implementation agency rules is code is a
great way for them to save money so for
an implementation agency
the cost of change for traditional rules
models is rules engines is
extraordinarily high they feel the pain
of poorly drafted policy they feel the
pain of when you said this what did you
mean because by the way how I interpret
that could affect thousands or hundreds
of thousands of people right so for
implementation agencies saying
regardless of how it's drafted you could
run your legislation as code as a way to
actually minimize your own complexity to
simplify your own Service delivery to
improve change management and to
actually test the outcomes of your
systems against the law to make sure
it's lawful before it you know pumps out
to a person for a policy agency they're
only starting to realize that they're
part of the problem
so it's a different type of conversation
but that conversation is already
starting to happen my personal hack that
I'm going to try to do this year and I
mean that in the Civic hack way is I'm
working with the person who's one of the
authors of the Australian policy
handbook and the Australian policy cycle
to try to rewrite that for instance to
actually make this a normal process
um so working with policy agencies and
showing them look if you isomorphically
draft you can take a test driven
approach why not have a couple of people
that have consume this Rule and what I
found is every policy team that has
experienced having someone in the room
that's going to naturally consume the
rule and then getting a better policy
getting something which is implemented
closer to what they imagined in the
first place every time they experience
that they they want to change so that's
how to deal with implementation that's
how I do with policy agencies with the
Parliamentary Council Office which is
the key one to change the problem is
they don't have any Tech capability
so a little call out we need great
techie people with Innovative minds and
open source mentalities to work in
government come play please
that will be my third strategy come come
and join the workforce and help
thank you
cool thank you once again g'day
um
Dr Andrew Lee
member for fenno in Canberra
um really likes randomized controlled
trials yeah and I'm wondering where that
fits in are they the tests are they the
the the
feedback that go to policy makers is
that part of the plan so the when you
take a better rules process in the first
instance and there are some agencies who
have started doing more let's call it
design LED approach to policy
um so
people that just want to do a b testing
the challenge is that you may you can
maybe test your way to a spoon from a
rock over a thousand iterations right
but if you actually ask a person what
they're trying to do and they say and
trying to drink some soup you might give
them a spoon to start so I think that a
b testing and randomized testing has a
role to play but it shouldn't be the
start of the process if you see what I
mean like the start of the process
should be that concept modeling that
what are we trying to achieve and what
are the different ways that we might
achieve that it might be a grant it
might be a piece of Regulation it might
be a service it might be tweaking
something that already exists but it
certainly should involve the people
affected by that policy to be part of
that ideation process before they even
commit to one way or the other as it
were
um so but yeah then actually monitoring
for impact is really critical the other
problem with um randomized testing and a
b testing and policy is if it's
something as critical as Social Services
what happens to the bees as it were so
so some of this has to be done in in um
not just collaboration with people but
in micro testing before you push it out
just because of the absolute critical
nature of some of some of the things
that government provides
so participatory
design LED from the start
and and then micro testing before things
are rolled out I think are probably
really key
well it's a few
thanks
um so I just want to start by saying I
love this stuff so you had me at hello
um but I do have a question
um from the inside it seems that uh the
the biggest barrier we have to overcome
in getting all of these things to come
together and and produce these great
outcomes is that that none of the
players trust each other and I'm
wondering if there are things that
you've seen in practice that can help
say the the policy
the policy experts and the the people
who have to do the implementation and
the people who lived experience and the
broader Community who aren't personally
going to be impacted but have a real
interest in seeing this work well in
society how do we how can we start to
build that trust between them so that
they can come to the table and co-design
for sure
um I I remember in Canada I was talking
at a policy I was talking to some policy
lead people and we were talking about
running an event called policy
transformation and they're a bit
bit funny about it and I and they
finally said well Peter I just want to
change the name because policy
transformation sounds like you actually
want to change how we do things
I'm like yes
and so then that led to a different sort
of conversation
um I I think that partly um I think
there's a couple of things first of all
some of this happens naturally in tiny
Pockets so weirdly enough my observation
is that almost every tax department
globally that I've dealt with does a
micro aversion of this they do have
policy people and implementation people
in the same room rapidly iterating
policy regularly so there's a there's a
micro example of that in every
jurisdiction that I've worked in for
sure and several that I've seen where
for whatever reason and it's probably
because of you know people governments
have adapted to be very responsive to
business needs just less so citizens but
you know but we can use the patent to
being able to say look we already do it
well here so why can't we just extend it
to other sorts of portfolios so
amplifying patterns is helpful
starting from the the actual outcome
because everyone's sort of focused on
they have too much work and Source too
stressful but if you say well hold on
what's the impact you're trying to have
what's the shared goal that we have
and if you can identify a shared goal
you have a better chance of people being
able to come together because if even if
they even across disciplines you know
and identifying discipline friction as a
thing you know if you get your data
scientist and your designer in the same
room they will kill each other
um unless you can say we will only get a
better outcome because you're both in
the room and by the way it's not that
each either of you is better or worse
than the other it's that you're
different let's identify that you have
different lexicon different methods
different like different techniques and
let's Embrace that diversity as part of
the strength of getting to where we need
to go identifying discipline friction as
a thing and building that into a culture
that can be multi-disciplinary by choice
um helps build that trust because some
of that trust is like from my
observation anyway as pure discipline
friction
um
and then of course actually starting to
shift to outcomes-based structures so 20
30 years ago as some of you again are
noticed me to know but most of you
probably won't about 30 years ago they
made a big shift to governments all
around the world where they decided to
turn them into businesses so all of the
departments were structurally changed to
have functional segmentation
that structural segmentation means that
every every discipline has a different
cost center and can't talk to each other
unless they can give them some money or
do some cost recovery or cite a cost
center if we can shift the structures of
our departments back to what they were
which was based around outcomes you need
to have an outcomes based cost center
and outcomes-based structure so that you
can hire and maintain the
cross-disciplinary set of skills and
experience that you need to drive that
outcome whereas with everyone just
trying to Blind Baton Pass between their
functions one group writing business
requirements the other group
implementing the first group saying
that's not what I meant well that's what
you wrote well I meant this well why
didn't you say that
um every time you hear the word business
requirements they're probably doing it
wrong
and yet so so we need to identify these
patterns and help people remember that
they've got more in common that they
haven't difference
a good approach we have one minute left
but I think we can fit one more sneaky
question and
just one second for a lovely microphone
for you thank you
um I'm just wondering if you could um
outline or give any examples of once
these um once this legislation or
regulation has been written of um either
citizen groups like you were showing in
New Zealand or other organizations
actually consuming them
of other organizations using them yes
um
so that's starting to happen all around
the world the um so
just from my experience
um so the the New Zealand I'm pretty
pleased with because
um there's lots of
um there's lots of departments starting
to say okay yeah actually this
separation of rules and um of
legislation and Business Systems is
really actually helpful to what we're
trying to do so it's definitely starting
to be adopted in government itself in
New Zealand the group that we pulled
together
um was sort of loosely based on the gov
zero lessons out of Taiwan
um but it is I think the world's first
Citizen Community repository of
legislation as code which is pretty cool
um and so it's continuing to be built
upon they're finding new funding to get
more benefits put in and the goal is to
actually have a community managed
repository which is the next call for
all of you I want to build a community
repository of legislations code here
that was actually the last slide which I
mentioned I get to that was really bad
um I want to build a community
repository here something that we can
use to test new regulation Test new
legislation something we can use to say
oh same as benefit me here's what the
law says you're eligible for and if the
department gives you a different answer
and you say well here's what the law
says I'm eligible for why are you giving
me a different answer and when they say
well you don't have any operational
policy in your tool we can say are you
suggesting your operational policy
conflicts with the law
question mark
um you know you're going to shake up the
power differential otherwise uh
otherwise it you know we all take it too
seriously um so I want to build one of
those in Australia that we can all rely
upon every single Think Tank every
single research institution Gratton here
in Melbourne case in point keeps
building out these models these
legislation and regulations code models
to do their own thing why wouldn't we
have a shared repository why can't we
build policy infrastructure for our
nation so that would be my suggestion
anyone yeah
thank you