conference Data Technology

Your algorithmic future: weapons of maths creation and destruction

Science Fiction writer William Gibson said “The future is already here, it’s just not widely distributed.” When you look around you can see the truth of that statement. Most of the technologies that will influence us over the next few decades already exists. In many ways it feels like we’re living in parts of that future. We can 3-D print replacement jaws for people. And 3D printing was invented over 30 years ago. In NDRC, where I work, we have companies working on embedded sensors for post operative bleed detection, and working on helping kids with focusing and ADHD problems through neuro-feedback game play. [1]  In many ways technology is enriching our lives. In reality the title of this piece is less ‘Our Algorithmic Future’ than ‘Our Algorithmic Present’.

As a technophile that’s very exciting. I have a deep and abiding love of science and the wonderful possibility of technology. I grew up reading Isaac Asimov (his science and his fiction), Arthur C Clarke and Carl Sagan. And watching Star Trek, Tomorrow’s World and other optimistic visions of technology and the future.

At the same time there is a darker side to technology. Paul Erlich said “To err is human, to really foul things up requires a computer.” It’s not hard to find examples. California released 450 high-risk, violent prisoners, on an unsuspecting public in 2011, due to a mistake in its computer programming. ‘We-connect’ an app based vibrator which captures the date and time of each use and the selected vibration settings, and transmits the data — along with the users’ personal email address — to its servers in Canada “Unbeknownst to its customers” a number of whom are now suing the company.[2]

And most dark of all is the case of the firing of elementary school teacher Sarah Wysocki by Washington DC Public schools. The school system used “VAR”, a Value Added statistical tool to measure a teacher’s direct contribution to students test results. Despite being highly regarded in classroom observations the low score from the algorithm led to her being fired. There was no recourse or appeal. And no way to really understand the working of VAR as they are copyrighted and cannot be viewed.[3]

Computer Says No There is this abstract notion of what the computer said or what the data tells us. Much as the complex gibberish that underlay the risk models of economists and financial services companies in the run wasn’t questions (because maths) the issue here isn’t the algorithms as much as people and their magical thinking.


I came across this quote from IPPN Director Sean Cottrell, in his address to 1,000 primary school Principals at Citywest Hotel in 2011.[4]  He commented

‘Every calf, cow and bull in the State is registered by the Department of Agriculture & Food in the interests of food traceability. Why isn’t the same tracking technology in place to capture the health, education and care needs of every child?’

Well intentioned as it might be, this shows a poor understanding of cows, a worse understanding technology and dreadful misunderstanding of children and their needs. I find this thinking deeply disturbing, and profoundly creepy so I decided to unpack it a little.

This is how we track cows
And this is how we start that process by tracking calves
And I wondered is this how he’d like to track children? (H/T to @Rowan_Manahan for that last image)

Then I realised that we are already tracking children.
KidsOnly its not the Primary Principles Network that doing it, it is private companies doing the tracking and tagging. It is Google and Facebook and Snapchat, with some interesting results and some profound ethical questions. We now know that Instagram photos can reveal predictive markers of depression and that Facebook can influence mood, and peoples purchasing habits.[5]

Our algorithm present is composed of both data and algorithms. We have had an exponential growth of processing capability over the last number of years, which has enabled some really amazing developments in technology. Neural Networks emerged first in the 1950s dimmed in the late 1960’s, reemerged in the 1980s and has taken off like wildfire in the last few years.The Neural Network explosion is down to the power, cheapness and availability of GPU’s, together with improvements in the algorithms themselves. And Neural Networks are really really good at some kinds of pattern analysis. We are getting to a point where they are helping radiologists spot overlooked small breast cancers. [6]

There is also a very big problem with algorithms. The problem of the Black Box. The proprietary nature of many algorithms and data sets mean that only certain people can look at these algorithms. Worse we are building systems in a way where we don’t necessarily understand the internal workings and rules of these systems very well at all.
Black boxes look like this. In many systems we see some of the input and the output. But most is not only hidden its not understood. In a classic machine learning model. We feed in data and apply certain initial algorithms. Then we use it prediction or classification. But we need to be careful of the consequences. As Cathy O’Neill cleverly put it Donal Trump is an object lesson in Bad Machine Learning. Iterate on how crowd reacts to what he says and over optimise for the output – Classic problem of Machine Learning trained on bad data set. We need to think about what the systems we’re building are optimising for. [7]

George Box said that “All models are wrong but some are useful.” Korzybski put it more simply “The Map is not the territory.” And its important to remember that an algorithm is a model. And much as the human mind creates fallible biased models we can also construct fallible computer models. Cathy O’Neill put it bluntly that “A model is no more than a formal opinion embedded in code.” The challenge is that the models are more often than not created by young white males from an upper middle class or upper class background. It is not that human brains are perfect model makers but we spend a long time attempting to build social processes to cope with these biases. The scientific method itself is one of the most powerful tools we’ve invented to overcome these biases.

As we unleash them on education, (Sarah), Policing (pre-crime in chicago) and health and hiring we need to be aware of the challenges they pose. Suman Deb Roy has pointed out

Algorithmic systems are not a settled science, and fitting it blindly to human bias can leave inequality unchallenged and unexposed.  Machines cannot avoid using data.  But we cannot allow them to discriminate against consumers and citizens. We have to find a path where software biases and unfair impact is comprehended not just in hindsight. This is a new kind of bug. And this time, punting it as ‘an undocumented feature’ could ruin everything. [8]

Bernard Marr illustrates this with an example

Hiring algorithms. More and more companies are turning to computerized learning systems to filter and hire job applicants, especially for lower wage, service sector jobs. These algorithms may be putting jobs out of reach for some applicants, even though they are qualified and want to work. For example, some of these algorithms have found that, statistically, people with shorter commutes are more likely to stay in a job longer, so the application asks, “How long is your commute?” Applicants who have longer commutes, less reliable transportation (using public transportation instead of their own car, for example) or who haven’t been at their address for very long will be scored lower for the job. Statistically, these considerations may all be accurate, but are they fair? [9]

There is an old saying in tech: “GIGO: Garbage In Garbage Out” the risk now it that this will will become BIBO “Bias in and BIAS out”

As we gather vast amounts of data the potential for problems increase. There can be unusual downstream consequences also the opportunity to create perverse incentives. We are embedding sensors in cars, and looking the idea that safer driver will be given better rates. The challenge is that personalised insurance breaks the concept of shared risk pools, and can drive dysfunctional behaviour. Goodhart said “When a measure becomes a target, it ceases to be a good measure.” We had a significant recent Irish example with crime statistics where the CSO pointed out problems with both the Under-recording by police of crime and the downgrading of a number of reported crimes. [10]

At one level I see our future as a choice between, Iron Man – technology to augment, or Iron Maiden – technology controlled by a few that inflicts damage on the many. Technology to augment or to constrict . Technology  changes that threaten the self also offer ways to strengthen the self, if used wisely and well.


It is clear that technology does not self-police. We could cut off the use of phones in cars using technology – so it can’t be used while driving but the companies doing so currently choose not to do so

In Europe we have our own bill of rights – a charter of fundamental rights enshrined in the Lisbon treaty and it guarantees “Everyone has the right of access to data which has been collected concerning him or her, and the right to have it rectified.”  This right has been used to challenge the export of data from the EU to the US under the Schrems decision of the European Court of Justice. [11]

My belief is that we need to extend these rights in the algorithmic era. We need to create a “Charter of Algorithmic Rights” For our algorithmic age. Not a Magna Carta  which really just enabled the lords against the king without much for the the peasants. We need algorithmic rights, of the people, by the people and for the people.

CrashSimply put we need airbags for the algorithmic age. For decades cars have safer for men than women because the standard crash test dummy tests on male size standard and biases the development of safety towards the average male. As I said, technology is not self policing. [12]




We are going to have to create better tools. We need to be able to detect, and correct bias and to audit and ensure fairness over a simple move to efficiency. Or else we are tying things together in unforeseeable ways that can have profound consequences at the individual and societal level. Tools such as Value in Design and Thought experiments help. But we need to go much further.


Kate Crawford writing in Nature says

“A social-systems analysis could similarly ask whether and when people affected by AI systems get to ask questions about how such systems work. Financial advisers have been historically limited in the ways they can deploy machine learning because clients expect them to unpack and explain all decisions. Yet so far, individuals who are already subjected to determinations resulting from AI have no analogous power.” [13]


While this is necessary I don’t believe it’s sufficient. We need a “Charter of Algorithmic Rights“. While looking to the opportunities they can afford we need to recognise the biases and limitation of technology.  What appears to be augmentation may not really be the case. It may restrict and rule rather than enable.



We need to ensure that are tools are creative and reflect the diversity of human experience.

– (C) BBC / BBC Studios – Photographer: Ben Blackall

We are better managing them than being managed by them in our algorithmic future.




[1] The companies mentioned are Enterasense and Cortechs.

[2] Computer errors allow violent California prisoners to be released unsupervised can be found here and the story on the app based vibrator is here.

[3] One link to the Sarah Wysocki story is here for more details read Cathy O’Neills excellent book “Weapons of Math Destruction” or take a look at Cathy’s blog.

[4] Original Link was Tweeted by Simon McGarr. The piece is here

[5] How an Algorithm Learned to Identify Depressed Individuals by Studying Their Instagram Photos  and  Everything we know about Facebooks mood manipulation

[6]  Neural Nets  may be so good because they map onto some fundamental principles of physics

[7] Trump as a bad Machine Learning Algorithm

[8] Genesis of the Data Drive Bug

[9] Bernard Marr The 5 Scariest Ways Big Data is Used Today

[10] What is the new Central Statistics Office report on Garda data and why does it matter?
and CSO (2016)

[11]DRI welcomes landmark data privacy judgement and Schrems v. Data Protection Commissioner

[12] Why Carmakers Always Insisted on Male Crash-Test Dummies

[13] There is a blind spot in AI research Kate Crawford& Ryan Calo


10 Ways that the Websummit is like Disneyland

I took the kids to Disneyland last year. I was reminded of that experience today. (I skipped that last few Websummits and it’s grown a wee bit since the first one in Bewley’s Hotel in Oct 2009 and the ones in 2010/2011). More or less tongue in cheek

  1. Lots of money spent on AI and figuring out how to manage queues still means “there are lots of queues.”
  1. The food is overpriced. (Websummit have better overpriced food but it’s still well overpriced). Like Disney bring in your own food (especially if you’re a struggling startup) or go outside for food. Base Pizza will run you €11.50 for best pizza in Dublin and a drink. Or soup in Insomnia even cheaper.
  1. You’ll spend a lot of time on your feet walking from attraction to attraction. Its not quite Walt Disney Studios / Disneyland back and forth but its not far off. 
  1. The good talks/rides are too short.  You’re just starting to enjoy them when they’re over…….
  1. Three days is enough. There’s only so much you can take. While it can be great fun eventually you’re in need of something more substantial. And the same goes for Websummit.
  1. The best map of the venue is on paper.
  1. You need to pace yourself. It goes from pre-summit breakfast events, through the summit itself to lots and lots of parties. In Disney it was get in early, take a break in the middle of the day and come back refreshed. Some variation of this is probably a good summit plan too.
  1. Mickey Mouse makes an appearance. OK no Mouse there was someone with a Unicorn hat.
  1. The kids get very excited about the whole thing. And the adults get very exhausted but they go anyway. (That’s a marketing win for both Disney and Websummit ) 
  1. You have to smile at the fireworks. This year they’re spectacular.


There’s a good take on day 1 the Websummit by Karlin Lillington.


conference People personal

A sense of #Úllconf

I have copious notes to write up on Ùll. Pages and pages. Eventually

The images below are a sensory fragments of a  “A family wedding without the family rows.”

It is to paraphrase someone in the corridor “Enough technology to qualify as a business event, but to call it a technology event undersells the scale of what it does”.

And yes. The 5K was completed (evidence below).
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conference People

#Cong14 Thoughts and Reflections (Or Everything you wanted to know about Congregation but were afraid to ask)

My mind is still buzzing from #Cong14. More ideas per square minute than most events would have in a week.

It was a day and more of conversation and serendipity. People I’ve known online and off. Some of the connections have deep roots. Sean McGrath who’s blog I first paid attention to over a decade ago, but had never met and Bernie Goldbach who I first met when the crackle of dial up modems was how I got online.

Initial plans to drive down to Galway on Friday morning were changed by Client meetings. There was a pitstop in Galway and the needful pilgrimage to Charlie Byrnes before a final fogbound trip out to Cong in the dark.  I met  Rurai Kavanagh  Gianno Catalfamo and the man behind Congregation Eoin Kennedy for a quick drink, and a brief tour of Cong. Later that evening I chatted with Fiona Ash and Amanda Webb with a roaring fire, the Late Late Toy Show and conversation flowing.

Saturday was #Cong14 proper.  50 plus people registered in Ryans and were assigned to huddles for the the day.  This was where the key problem of Congregation presented itself. Too much good stuff across and too many good people. As chance would have it I spent three of my four huddles in ‘The Quite Cailin’.

Each of the huddles needs a post in itself. The idea was that over an hour two people would present their papers and the group would discuss in an open unconference type format. Our first huddle started with Maryrose Lyons talk “We need to talk about porn” and that is all we did for for the next hour. To the extent we hijacked the idea of a second talk and kept the discussion going.  Maryrose’s paper and talk gave me a lot of material to think about. She’s blogged a followup post about it

Things I learned in that first session include

  • Kids now first seeing porn at 11/12 v’s 17/18 20 yrs ago “like leaving a bag of heroin around the house and not talking about it”
  • Doctors treating erectile dysfunction used mostly treat men in their 40’s. Now treating more 18 year olds than 40 year olds
  • Porn is a neural and a cultural issue not a moral issue
  • Historical repressive Irish culture and not talking about sex meets 21st century technology is a danger
  • There are obvious links to online misogyny and abuse of women that comes with porn culture
  • Two studies in UK in 2011 and 2013 on “the sexualisation of culture” because of the concern over it
  • Snapchat and other things making porn a paying mechanism for 3rd level students

The thought that jumped into my head is – is porn and boys a parallel with girls and fashion and body image?  As someone else pointed out in both cases “it fucks with their heads”

There was a lot of deep ideas and sharing that came from everyone in the group on this topic. The only problem and it was the general problem of the day is that we didn’t get to talk about more of the topics.

My second huddle of the day was back in the Quite Cailin and summed up in this photo

  In our second talk Sean McGrath told us that the Cloud was a terrible thing to waste on content.

Starting with the idea that How do we get rid of the divide between business people and IT people those who can program? and expanding on the notion that at one level. Since Algol in 1960 everything in Computers in syntactic sugar. How do we reframe things to let billions participate ? Sean went on to point out that Excel was in many ways one thing that went beyond syntactic sugar and put power in peoples rather than programmers hands.

I learned of the phrase of a “Personal Event Network” and was reminded of Zapier and If This Then That in the description of “I want to be able to draw it and then run it, most solutions to the problem are attacking it the wrong way”

The core notion of if then else… and iteration are all computers need (for Turing complete programming environment) echoed this idea.  Its not to get rid of computer programming. Its to supplement it.

The thoughts went deep. And the question of serendipity came up as Sean’s learning of Python came due to a book being misfiled and a Barney doll. Something Bernie has expanded on.

Jazz, failed artists and Frank Herbert “you cannot understand a system by stopping it” were thoughts around the conversation.  I like the idea that we shouldn’t  stop systems to understand them, but rather we need to slow them down to look at them.

The second part of this session was the least well formed of the day. It was my own. My post was a rough draft and its only after the morning sessions and conversation I figured out what I was trying to say.  How can the digital and the social help the analog the the personal and the societal.  As Joe Kearns  deftly pointed out, with every technology we lose something but we should be gaining more than we lose.

Congregation itself is an example of where technology is linking people together in deeper and more important ways.

We had an interesting chat with Michelle of The Quite Cailin just before lunch. The Shop has a fair amount of technology powered from a Raspberry Pi and is looking to be self sustaining in electricity through the use of Solar Panels.

It’s a beautiful space.

Lunch was in Puddleducks Cafe where I learned a lot by listening to Robbie who chaired our morning huddles.  Little connections played back together with a project he’s working on at the moment in my home town.  Our third session of the day had me back in The Quite Cailin. I debated a particular painting when Michelle commented “Art, if its meant for you it’ll find you”.

We talked about the nature of value in the afternoon with Paul Killoran t starting from the concept of Aristotles 4 characteristics of Currency, (that it should be Durable, Divisible, Portable, and have Intrinsic Value). Paper money and breaking gold standard took us away from that intrinsic value. Bitcoin takes us another step away from that concept and Paul tore up a €5 note to demonstrate that the value of money is in our heads.  Money is the worlds largest religion. Its a belief system.  We examined questions of what has intrinsic value with one idea being the only thing that has intrinsic value has time. And we often lose focus on that when we focus too much on money.  Kingsley Aikens made some important points on inherited wealth and talked about  “the lucky sperm club” the heirs that will inherit 30 trillion in the US over next 30 years. 

There was a recommendation for the book “You are not so smart” by David McRaney on human cognitive biases and a very interesting comment from Gianni Catalfamo on the implications of Gödels incompleteness theorem for Bitcoin. That was one of the most intriguing question in a day of intriguing questions. 

The final huddle of the day was in the Rare and Recent Bookshop.  There was no obvious wifi code so I presumed there was none. (The owner later told me that no one had asked him for it.) So for the final huddle of the day I took some notes on the laptop.  Many topics came up. The question of Porn and online behaviour was discussed again as was the dangers in how teenagers can deal with suicidal ideation online. We talked about getting Irish businesses online. 47,000 of them have no online presence.  There is a need to break down barriers. And a large economic imperative with a lot of money leaving the country to companies in the UK. Averil Staunton of Historical Ballinrobe took us through that initiative and we learned of the challenges for technology in rural areas.  We discussed the the sharing of stories and a little about Storyful. 

After the group photo I explored the Rare and Recent Bookshop before a few drinks.

Dinner in Pat Cohan’s gave me an opportunity to catch up with Pauline Sargent and learn more about how some political parties are engaging or not locally.

There were all to brief conversations with too many. And lots of people I didn’t get to talk to at all. I need to dig into Caroline Lawless piece on managing online identity.  I missed the huddles on the issues with the Internet of Things some insights into what may be the best managed twitter account in Ireland Garda Traffic  and whether or not the postman has read my email and many more besides.

Some final thoughts

The social nature of social media, the challenges its poses and that it is all about people, people, people came through again and again in the papers and the  conversations.

If “Hashtags subvert hierarchy” is the new “Hyperlinks subvert hierarchy” then Congregation is a hearty stew of social serendipity takes you places that are important even if its not necessarily what you were expecting.

By serendipity I came across this by Steve Wheeler  writing on Ivan Illich. He quotes Illich

“Most learning is not the result of instruction. It is rather the result of unhampered participation in a meaningful setting.”

Unhampered participation in a meaningful setting.

That is the definition of Congregation.