How in love with AI are you?

AI is a problematic term at the moment. There is an awful lot of conflation between true existential/ubiquitous computing/end of the world AI on the one hand, and a nerd in a basement programming a decision tree in R on the other.

Which makes for some nice headlines. But isn’t really helpful to people who are trying to work out how to make the most (and best) of the new world of data opportunities.

So to help me, at least, I have devised something I call the LaundryCulture Continuum. It helps me to understand how comfortable you are with data and analytics.

(Because I realise that the hashtag #LaundryCulture might confuse, I’ve also coined the alternative #StrossMBanks Spectrum).

So here are the ends of the Continuum, and a great opportunity to promote two of my favourite writers.

In the beautiful, elegant and restrained corner, sits The Culture. Truly super-intelligent AI minds look on benevolently at us lesser mortals, in a post-scarcity economy. This is the corner of the AI zealots.


In the squamish corner sits The Laundry, protecting us from eldricht horrors that can be summoned by any incompetent with a maths degree and a laptop. This is the home of the AI haters.


Where do people sit? Well it’s pretty clear that Elon Musk sits towards The Culture end of the Continuum. Naming his SpaceX landing barges Just Read The Instructions and Of Course I Still Love You is a pretty big clue.

The Guardian/Observer nexus is hovering nearer The Laundry, judging by most of its recent output.

Others are more difficult… But if I’m talking to you about AI, or even humble data mining, I would like to know where you stand…

In defence of algorithms

I was going to write a blog about how algorithms* can be fair. But if 2016 was the year in which politics went crazy and decided that foreigners were the source of all problems, it looks like 2017 has already decided that the real problem is that foreigners are being assisted by evil algorithms.

So let’s be clear. In the current climate people who believe that data can make the world a better place need to stand up and say so. We can’t let misinformation and ludditism wreck the opportunities for the world going forwards.

And there is a world of misinformation!

For example, there is currently a huge amount of noise about algorithmic fairness (Nesta here , The Guardian here et al). I’ve already blogged a number of times about this (1, 2, 3), but decided (given the noise) that it was time to gather my thoughts together.


(Most of) Robocop’s prime directives (Image from Robocop 1987)

tldr: Don’t believe the hype, and don’t rule out things that are fairer than what happens at the moment.

Three key concepts

So here are some concepts that I would suggest we bear in mind:

  1. The real world is mainly made up of non-algorithmic decisions, and we know that these are frequently racist, sexist, and generally unfair.
  2. Intersectionality is rife, and in data terms this means multicolinearity. All over the place.
  3. No one has a particularly good definition of what fairness might look like. Even lawyers (although there are a number of laws about disproportionate impact even then it gets tricky).

On the other side, what are the campaigners for algorithmic fairness demanding? And what are their claims?

Claim 1: if you feed an algorithm racist data it will become racist.

At the most simple level yes. But (unlike in at least one claim) it takes more than a single racist image for this to happen. In fact I would suggest that generally speaking machine learning is not good at spotting weak cases: this is the challenge of the ‘black swan’. If you present a single racist example then ML will almost certainly ignore it. In fact, if racism is in the minority in your examples, then it will probably be downplayed further by the algorithm: the algorithm will be less racist than reality.

If there are more racist cases than non-racist cases then either you have made a terrible data selection decision (possible), or the real problem is with society, not with the algorithm. Focus on fixing society first.

Claim 2: algorithmic unfairness is worse/more prevalent than human unfairness

Algorithmic unfairness is a first world problem. It’s even smaller scale than that. It’s primarily a minority concern even in the first world. Yes, there are examples in the courts in the USA, and in policing. But if you think that the problems of algorithms are the most challenging ones that face the poor and BAME in the judicial system then you haven’t been paying attention.

Claim 3: to solve the problem people should disclose the algorithm that is used

Um, this gets technical. Firstly, what do you mean by the algorithm? I can easily show you the code used to build a model. It’s probably taken from CRAN or Github anyway. But the actual model? Well if I’ve used a sophisticated technique, a neural network or random forrest etc, it’s probably not going to be sensibly interpretable.

So what do you mean? Share the data? For people data you are going to run headlong into data protection issues. For other data you are going to hit the fact that it will probably be a trade secret.

So why not just do what we do with human decisions? We examine the actual effect. At this point learned judges (and juries, but bear in mind Bayes) can determine if the outcome was illegal.

And in terms of creation? Can we stop bad algorithms from being created? Probably not. But we can do what we do with humans: make sure that the people teaching them are qualified and understand how to make sensible decisions. That’s where people like the Royal Statistical Society can come in…

Final thoughts

People will say “you’re ignoring real world examples of racism/sexism in algorithms”. Yes, I am. Plenty of people are commenting on those, and yes, they need fixing. But very very rarely do people compare the algorithm with the pre-existing approach. That is terrible practice. Don’t give human bias a free pass.

And most of those examples have been because of (frankly) beginners mistakes. Or misuse. None of which are especially unique to the world of ML.

So let’s stand up for algorithms, but at the same time remember that we need to do our best to make them fair when we deploy them, so that they can go on beating humans.


* no, I really can’t be bothered to get into an argument about what is, and what is not an algorithm. Let’s just accept this as shorthand for anything like predictive analytics, stats, AI etc…



Sexist algorithms

Can an algorithm* be sexist? Or racist? In my last post I said no, and ended up in a debate about it. Partly that was about semantics, what parts of the process we call an algorithm, where personal ethical responsibility lies, and so on.

Rather than heading down that rabbit hole, I thought it would be interesting to go further into the ethics of algorithmic use…  Please remember – I’m not a philosopher, and I’m offering this for discussion. But having said that, let’s go!

The model

To explore the idea, let’s do a thought experiment based on a parsimonious linear model from the O’Reilly Data Science Salary Survey (and you should really read that anyway!)

So, here it is:

70577 intercept
 +1467 age (per year above 18; e.g., 28 is +14,670)
 –8026 gender=Female
 +6536 industry=Software (incl. security, cloud services)
–15196 industry=Education
 -3468 company size: <500
  +401 company size: 2500+
–15196 industry=Education
+32003 upper management (director, VP, CxO)
 +7427 PhD
+15608 California
+12089 Northeast US
  –924 Canada
–20989 Latin America
–23292 Europe (except UK/I)
–25517 Asia

The model was built from data supplied by data scientists across the world, and is in USD.  As the authors state:

“We created a basic, parsimonious linear model using the lasso with R2 of 0.382.  Most features were excluded from the model as insignificant”

Let’s explore potential uses for the model, and see if, in each case, the algorithm behaves in a sexist way.  Note: it’s the same model! And the same data.

Use case 1: How are data scientists paid?

In this case we’re really interested in what the model is telling us about society (or rather the portion of society that incorporates data scientists).

This tells us a number of interesting things: older people get paid more, California is a great place, and women get paid less.

–8026 gender=Female

This isn’t good.

Back to the authors:

“Just as in the 2014 survey results, the model points to a huge discrepancy of earnings by gender, with women earning $8,026 less than men in the same locations at the same types of companies. Its magnitude is lower than last year’s coefficient of $13,000, although this may be attributed to the differences in the models (the lasso has a dampening effect on variables to prevent over-fitting), so it is hard to say whether this is any real improvement.”

The model has discovered something (or, more probably, confirmed something we had a strong suspicion about).  It has noticed, and represented, a bias in the data.

Use case 2: How much should I expect to be paid?

This use case seems fairly benign.  I take the model, and add my data. Or that of someone else (or data that I wish I had!).

I can imagine that if I moved to California I might be able to command an additional $15000. Which would be nice.

Use case 3: How much should I pay someone?

On the other hand, this use case doesn’t seem so good. I’m using the model to reinforce the bad practice it has uncovered.  In some legal systems this might actually be illegal, as if I take the advice of the model I will be discriminating against women (I’m not a lawyer, but don’t take legal advice on this: just don’t do it).

Even if you aren’t aware of the formula, if you rely on this model to support your decisions, then you are in the same ethical position, which raises an interesting challenge in terms of ethics. The defence “I was just following the algorithm” is probably about as convincing as “I was just following orders”.  You have a duty to investigate.

But imagine the model was a random forest. Or a deep neural network. How could a layperson be expected to understand what was happening deep within the code? Or for that matter, how could an expert know?

The solution, of course, is to think carefully about the model, adjust the data inputs (let’s take gender out), and measure the output against test data. That last one is really important, because in the real world there are lots of proxies…

Use case 4: What salary level would a candidate accept?

And now we’re into really murky water. Imagine I’m a consultant, and I’m employed to advise an HR department. They’ve decided to make someone an offer of $X and they ask me “do you think they will accept it?”.

I could ignore the data I have available: that gender has an impact on salaries in the marketplace. But should I? My Marxist landlord (don’t ask) says: no – it would be perfectly reasonable to ignore the gender aspect, and say “You are offering above/below the typical salary”**. I think it’s more nuanced – I have a clash between professional ethics and societal ethics…

There are, of course, algorithmic ethics to be considered. We’re significantly repurposing the model. It was never built to do this (and, in fact, if you were going to build a model to do this kind of thing it might be very, very different).


It’s interesting to think that the same model can effectively be used in ways that are ethically very, very different. In all cases the model is discovering/uncovering something in the data, and – it could be argued – is embedding that fact. But the impact depends on how it is used, and that suggests to me that claiming the algorithm is sexist is (perhaps) a useful shorthand in some circumstances, but very misleading in others.

And in case we think that this sort of thing is going to go away, it’s worth reading about how police forces are using algorithms to predict misconduct


*Actually to be more correct I mean a trained model…

** His views are personal, and not necessarily a representation of Marxist thought in general.



The ethics of data science (some initial thoughts)

Last night I was lucky enough to attend a dinner hosted by TechUK and the Royal Statistical Society to discuss the ethics of big data. As I’m really not a fan of the term I’ll pretend it was about the ethics of data science.

Needless to say there was a lot of discussion around privacy, the DPA and European Data Directives (although the general feeling was against a legalistic approach), and the very real need for the UK to do something so that we don’t end up having an approach imposed from outside.

People first


Kant: not actually a data scientist, but something to say on ethics

Both Paul Maltby and I were really interested in the idea of a code of conduct for people working in data – a bottom-up approach that could inculcate a data-for-good culture. This is possibly the best time to do this – there are still relatively few people working in data science, and if we can get these people now…

With that in mind, I thought it would be useful to remind myself of the data-for-good pledge that I put together, and (unsuccessfully) launched:

  • I will be Aware of the outcome and impact of my analysis
  • I won’t be Arrogant – and I will avoid hubris: I won’t assume I should, just because I can
  • I will be an Agent for change: use my analytical powers for positive good
  • I will be Awesome: I will reach out to those who need me, and take their cause further than they could imagine

OK, way too much alliteration. But (other than the somewhat West Coast Awesomeness) basically a good start. 

The key thing here is that, as a data scientist, I can’t pretend that it’s just data. What I do has consequences.

Ethics in process

But another way of thinking about it is to consider the actual processes of data science – here adapted loosely from the CRISP-DM methodology.  If we think of things this way, then we can consider ethical issues around each part of the process:

  • Data collection and processing
  • Analysis and algorithms
  • Using and communicating the outputs
  • Measuring the results

Data collection and processing

What are the ethical issues here?  Well ensuring that you collect with permission, or in a way that is transparent, repurposing data (especially important for data exhaust), thinking carefully about biases that may exist, and planning and thinking about end use.

Analysis and algorithms

I’ll be honest – I don’t believe that data science algorithms are racist or sexist. For a couple of reasons: firstly those require free-will (something that a random forest clearly doesn’t have), secondly that would require the algorithm to be able to distinguish between a set of numbers that encoded for (say) gender and another that coded for (say) days of the week. Now the input can contain data that is biased, and the target can be based on behaviours that are themselves racist, but that is a data issue, not an algorithm issue, and rightly belongs in another section.

But the choice of algorithm is important. As is the approach you take to analysis. And (as you can see from the pledge) an awareness that this represents people and that the outcome can have impact… although that leads neatly on to…

Using and communicating the outputs

Once you have your model and your scores, how do you communicate its strengths, and more importantly its weaknesses. How do you make sure that it is being used correctly and ethically? I would urge people to compare things against current processes rather than theoretical ideals.  For example, the output may have a gender bias, but (assuming I can’t actually remove it) is it less sexist than the current system? If so, it’s a step forwards…

I only touched on communication, but really this is a key, key aspect. Let’s assume that most people aren’t really aware of the nature of probability. How can we educate people about the risks and the assumptions in a probabilistic model? How can we make sure that the people who take decisions based on that model (and they probably won’t be data scientists) are aware of the implications?  What if they’re building it into an automated system? Well in that case we need to think about the ethics of:

Measuring the results

And the first question would be, is it ethical to use a model where you don’t effectively measure the results? With controls?

This is surely somewhere where we can learn from both medicine (controls and placebos) and econometrists (natural experiments). But both require us to think through the implications of action and inaction.

Using Data for Evil IV: The Journey Home

If you’re interested in talking through ethics more (and perhaps from a different perspective) then all of this will be a useful background for the presentation that Fran Bennett and I will be giving at Strata in London in early June.  And to whet your appetite, here is the hell-cycle of evil data adoption from last year…