AI OK — what happened next?

Sam Villis
Feb 22, 2018 · 5 min read

A few weeks ago I wrote a little blog about queries I had after a request came my way from a team wanting to use an AI tool. You can read more about it here:

I thought it might be helpful to blog about what happened next, or rather, what I did next to answer the question.

I hope it demonstrates why working in an open, networked way, can help to solve problems and bring about solutions. So…

What’s our next step?

1. When in doubt, blog it out

Blogging helps me to work through the problem space and order my thoughts. It helps me to acknowledge the limitations of my knowledge and to work out what questions I need to ask to find out more.

It also helps me to work out how I can tell a story, make it understood by other people, and hopefully to ask for help or people’s views in the right way. It also gives me a resource I can share (so that I don’t have to tell the same story many times over!)

It also means that I have something to go back to and reference if I need to.

So getting my thoughts all into one place was key for me to get this moving.

2. Call in reinforcements

Blogging is the first port of call for some of my closest peer support network; by writing a blog I know that people who I trust will be likely to read it and help to set me on the right path; critiquing my thoughts, or offering their knowledge and experiences.

Sharing blogs further using Twitter helps me to widen my net.

It means it’s more likely to be seen by people on the edges of my network who might know more than me, know someone who knows someone and help me find the right direction.

As it happens, people did get in touch. Ryan linked me to a few resources and helped me to consider my risk aversion and question where it had come from — I really appreciated that. Ryan also linked me to the government AI Community of Practice.

I realised that I knew one of the organisers, Stefan, via One Team Gov.

We hadn’t really spoken before, but knowing that Stefan had similar interests to me gave me confidence to reach out, so I got in touch. Which brings me to…

3. Multiply

Take any introductions which are offered to you. Seek out the people whose names come up in conversation.

Stefan suggested I speak to Thom who works on Data Ethics Standards at GDS.

I would never have known to speak to him if I hadn’t listened to Stefan — this recommendation ultimately lead me to my solution; The Data Ethics Standard [1].

4. Jump

If there’s an opportunity to find out more, go for it, even if it pushes you out of your comfort zone. You’ll only know if it is going to be useful by participating.

In this story I ended up attending the AI Community of Practice meeting. I can’t imagine how I would have even known about it let alone actually attend unless Ryan and Stefan hadn’t encouraged me.

But going along to things and listening is, in my opinion, the best way to learn anything.

Ah, go on 3PO.

So what, Sam?

I’ve now made contact with enough people who are working in this area to be able to advise my stakeholder properly.

The advice is still concentrating on the usual stuff; focussing on user needs, running a discovery to understand all the options — the AI is just one possible approach among many that we could take forward.

The difference though, is that if they do want to seriously consider that approach, thanks to the Data Ethics Framework we have a good start for understanding the risks and questions that we would need to ask.

It means that my team can advise more clearly about what needs to be done. A massive win.

And why am I telling you this? Well because I could have sat at my desk and read everything I could possibly find on AI (I did read a fair bit) but that would have only got me so far.

If you can take anything from this story it’s: work with people, build your network [2], value people, listen to them and they will carry you along.

Resources:

If you’re here because you want to know more about AI and not just about how my brain works, I’ve collated all of the great resources that I’ve been sent by the brilliant people who have helped me with this. Enjoy:


Thank you to:

Richard McLean, Ryan Dunn, Stefan Janusz, Thom Townsend, Joe Broadway, Dan Barrett.


[1] The team are working on an updated version of the Data Science Ethical Framework. The original framework can be found here:

The update is still being tested and is not yet published and ready for use. Look out for it!

[2] Yes I know this is a yucky phrase, but in the absence of knowing a better one I’m using it here. Networking is too linked with shallow transaction, that is completely the opposite of what I mean.


TL;DR — people. They’re kinda brilliant.

Sam Villis

Written by

Working to #FixThePlumbing at MHCLG, Prev: Standards Assurance at GDS and Cabinet Office. Proud to be @OneTeamGov. Weeknotes/Design/illustration/things/records

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