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).

Conclusions

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.

 

 

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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

Immanuel_Kant_(painted_portrait)

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…

HellCycle

 

 

 

Doing Good With Data: the case for the ethical Data Scientist

This post is designed to accompany my presentation to the Teradata Partners User Group, but hopefully some of the links will prove useful even if you couldn’t get to the presentation itself.

Needless to say, the most important part – the Pledge – is right at the bottom.  Feel free to skip to it if you like!

Law

The law (as it relates to data – well actually pretty much all law) is complex, highly jurisdictional, and most importantly of all at least 10 years behind reality.  Neither Judy or I are lawyers, but hopefully these links provide some general background:

One of the first legal agreements was the OECD’s position on data transfers between countries. It dates from the early 70s, when flares were hot and digital watches were the dream of a young Douglas Adams: http://itlaw.wikia.com/wiki/Guidelines_on_the_Protection_of_Privacy_and_Transborder_Flows_of_Personal_Data

Much later the EU released the snappily titled EU 95/46/EC – better known as the Data Directive. The joy is that each country can implement it differently, resulting in confusion.  There are currently proposals out for consultation on updating it too: http://en.wikipedia.org/wiki/Data_Protection_Directive

Of course the EU and the US occasionally come to different decisions, and for a brief discussion of some of the major differences between them you can try this: http://www.privireal.org/content/dp/usa.php

Don’t do evil

Google’s famous take on the hypocratic oath can be simplified as ‘don’t do evil’. As we say in the presentation, this is necessary, but scarcely enough.  It also has the disadvantage of being passive. In it’s expanded form it’s available here: http://investor.google.com/corporate/code-of-conduct.html

Doing Good With Data

Now for the fun bits!  For information on the UN Global Pulse initiative: http://www.unglobalpulse.org/ 

Data 4 Development – the Orange France Telecom initiative in Ivory Coast: http://www.d4d.orange.com/home

If you have a bent for European socialised medecine, then the NHS hack days are here: http://nhshackday.com/

DataKind

And our favourite – with a big thanks to Jake Porway and Craig Barowsky – is DataKind: http://datakind.org/ You can also follow @DataKind

To find out more about the UK charities mentioned check out Place2Be https://www.place2be.org.uk/ and KeyFund http://www.keyfund.org.uk/

Please take the time to register with DataKind, and keep your eyes open for other opportunities.  We hope that DataKind will be open for business in the UK too soon!

The Pledge

Please go and look at the Pledge, and if you think you can, then sign up.  If you have one of our printed cards, take it, sign it and put it on your cube wall (or your refrigerator – wherever it will remind you of your commitment). But sign the online one too.  And one you’ve done that, let the world know! Get them to sign up. If you want a Word copy of the printable one just drop me a line.

http://www.causes.com/actions/1694321