Thoughts on Voluntariness

Voluntariness, the requirement that consent be “freely given” is probably the hardest issue in consent, at least from a conceptual point of view. In this post, I’m going to try and summarise the key points of the debate, as I see them. At it’s heart, though, it’s an issue that fairly quickly expands to encompass issues like social power, moral philosophy, and theory of mind. In practice we need to find a reasonable way through. This isn’t an empirical question; it’s a question of values – basically, it’s politics.

Defining “will”

I struggled with where to put this section, so I’m putting it here; think of it as useful background.

Research in consent is hard because it represents the point at which people’s beliefs, attitudes and values crystallise into a behaviour: accept, or decline; use or walk away. The problem is basically that, for well-documented and reasonably well understood reasons of bounded rationality, there is often a far from perfect link between attitudes, beliefs and behaviour. There is huge scope in human decision making for behaviour in the moment to ignore even deeply held convictions and our own sense of longer-term wellbeing.

The observed disconnection betweens attitudes, beliefs and actual behaviour is usually referred to as the privacy paradox. In many ways, the privacy paradox would be better referred to as the consent paradox, as it only applies in scenarios where processing is contingent on the data subject’s decision. There are two basic interpretations of the observed incongruence between behaviour and attitudes – that the mechanisms for eliciting our attitudes are somehow misleading us into behaviour that we don’t really mean, or that the means of eliciting our attitudes are somehow getting us to say things that we don’t really mean. My personal feeling is that both are probably true to some degree (which is also to say that both are also wrong to some degree).

In any case, I contend that the goal of consent design should be to make attitude and behaviour consistent with one one another, because then the nasty problem of which one is ‘truer’ conveniently goes away. The goal is not strictly to avoid data subjects feeling like they’ve made the ‘wrong’ decision, but that they feel like they made a ‘good’ decision in the circumstances.

Voluntariness on a Spectrum

For the sake of expediency, we have something of a spectrum when it comes to views on voluntariness. At one end is the “any inducement undermines voluntariness” position, in which any kind of reward of payoff from giving consent renders it involuntary. (I think the recent ICO draft might come a little bit too close to this end of the scale.) At the other end is the position that any non-compulsory action must be voluntary (compulsion probably defined legally, or with respect to some other actor that has significant power over the individual). This latter argument is, essentially, that the “null action” of walking away always exists.

There are problems at both ends of this spectrum that make adoption of either extreme undesirable. Let’s consider them in turn.

Any reward from giving consent renders it involuntary”

The obvious problem here is that classical economics predicts people won’t accept a cost without a benefit that’s more valuable to them. Why would anybody consent to something if they get nothing in return? Even accepting that classical economics is flawed, it strikes me as ethically problematic to ask people to consent to something that delivers no benefit. There is undoubtedly risk to a data subject in giving consent to any data collection or processing, and arguably any consent given in return for nothing would be based on the subject not understanding that.

Any non-compulsory action must be voluntary”

I don’t have much sympathy for this end of the spectrum, either. Opportunity cost – the value that you miss out on from not taking an action – is an important economic principle and probably the main argument against this position. If everyone else obtains £100 of annual value from giving consent, then you’re comparatively worse off by £100. Not using Facebook, for instance, would entail a large social cost to many users.

The upshot of a spectrum with two undesirable ends is probably that we need to be looking at the middle of it. As a rule of thumb, we might define voluntariness as requiring that any cost from not consenting is relatively small (easily borne by the subject).


As an inconvenient counterpoint to what I just said, I’d like to introduce a thought experiment.

In December 2014, a surgeon screwed my elbow back together following a fairly nasty compound fracture resulting from exercise. This procedure was done with my consent, but in a scenario where my options were contextually limited. The risk of undergoing olecranon surgery is that the ulnar nerve, which controls the hand, might be damaged. I retained the option of just going home, but that would involve a huge opportunity cost of still having a very badly fractured olecranon.

How does this example work with the rule of thumb framed above? The cost of not consenting was huge; potentially losing significant use of my left arm. Nonetheless, I would not not contend that the surgery I underwent was involuntary.

I had, of course, already incurred the direct cost of not consenting a couple of days before. This example is purely about the opportunity cost of not consenting. A surgeon was not threatening to break my elbow if I didn’t consent to something else.

We might also consider that the surgeon themselves had nothing to gain from performing the surgery; that it is ethically relevant what the motives of the other party are. Except that that isn’t really true; the (NHS) surgeon only has a job because people consent to having operations, and in other health systems the value exchange is even clearer – you (or your insurance) pays the surgeon directly.

Using the notion of economic cost doesn’t seem to work in all circumstances, then; nor does questioning the motives of the other party. Neither asking for consent, or giving it, is entirely selfless. We could reframe the previous statement, and say that consent is the basis for finding a value exchange that is agreeable, and beneficial to both parties.

In Practice

In practice, determining whether a consent signal passes the test set out above will be incredibly difficult. Not least because it’s hard to quantify the value obtained by each party, especially if they don’t even know it themselves, and particularly since external factors – or inherent risk – can result in an expected payoff never actually emerging. Few would contend that entering the national lottery is involuntary, even though the average payoff is less than the cost of entering and the potential payoff is – for the overwhelming majority – never realised.

A more tractable alternative would be to consider voluntariness through the eyes of people who are giving consent; if they feel the exchange is fair, and were happy to provide consent, we might conclude that it’s voluntary. This takes the complex context into account naturally, without inviting endless speculation and “what if..” scenarios. It’s also a dimension that we’re experimenting with in our consent metric (“consentfulness”) research.

Equally practically, we need consent to work in – broadly – the way that classical economics says it should. In essence, consent should be a mechanism that supports consumer choice and a functioning market in personal data driven services. It’s the only real means of data subjects directly exerting pressure on data processing practices. There are two ways that consent can fail – both, ultimately, disempower citizens and leave us in a consent-less scenario.

First, consent can fail in the way it traditionally has failed – where it becomes weakened to the point that it’s essentially meaningless, and doesn’t actually signify understanding or choice on the part of the data subject.

But consent could also fail because it becomes too hard to obtain. If the practical implications of gaining consent become too onerous, if the legal risk becomes too high, then the hard reality is that organisations will have to find ways to weaken it, or gradually expand their other legal bases to avoid ever having to rely on consent. Consent is the only legal basis that necessarily gives explicit consideration to the wishes of the data subject. It is valuable as a mechanism for consumer preference, and it is valuable in and of itself as a means to enact our personal agency. We must avoid a situation where reasonable-but-imperfect consent is discarded in pursuit of perfect-but-ultimately unobtainable consent.

Pareto may be applicable here; for the time being I think we have to be prepared to settle for 80% of consent, for 20% of the cost, in order to make it practically deployable.


Ultimately, whether consent is voluntary or not is – like all things in consent – hugely contextual. An interaction that is voluntary to one person, may be involuntary to another. At scale, an interaction that provides voluntary consent in one setting or with one demographic may not delivery voluntary consent in another setting or with a different demographic. The consentfulness of an interaction – whether defined in terms of understanding, or voluntariness, or both – is never just a function of the consent mechanism, or the service being offered, or the organisation itself, but of the complex contextual interaction between the consentee and the person requesting their consent.

The challenge here is, realistically, not to get it right in every case, but to put in place interactions, procedures and practices that get it right as often as possible. As ever, this is not just a case of complying with the law, but of building trustworthy relationships with the people whose data is being held and processed. I’ve come up with a few likely implications of the consent rules, and the complexities that they entail regarding voluntariness:

1. Monopoly providers probably need to act differently to start-ups

The opportunity cost associated with being excluded from a monopoly (or defacto monopoly) service is likely to be very different to that of being unwilling to consent to a smaller service that none of your friends use. Supporting, through a basic service, those users who don’t consent will become a cost of success. “We have to make some processing voluntary because we’re a defacto monopoly provider” is kind of a nice problem to have.

2. Trying to understand necessity is probably a poor strategy

I have a feeling that the concept of necessity is too nebulous, and that it’s too hard to constrain what “necessary” really means. Economic necessity is no less real to most organisations than technical necessity. Equally, defining whether a particular processing activity is a cost or a benefit to the end user is fraught with difficulty; some users do like the idea of targeted advertising, receiving coupons, and hearing about new features via their email inbox. One person’s unwanted side-effect is another’s reason for saying yes in the first place. Moving towards an understanding of consent that’s based on managing surprise, and empirically evaluating how well people’s expectations match up with reality is probably a more tractable approach.

3. Voluntariness will often be in the eye of the consentee

This sort of follows from the above. In the absence of being able to measure and constrain the relative payoff (or costs) to the parties involved in consent we should defer to something more empirical. We’re experimenting with a voluntariness dimension in our work on consent metrics; asking people simply if they felt they were “happy” to give consent feels like a tractable way to identify scenarios where consent isn’t being given voluntarily for some reason.

4. Consent is not a solution to all social ills

It’s probably not practical to try and render other social issues like inequality irrelevant by the way that we define consent. A choice between paying for a service or seeing targeted adverts is, to my mind, a positive step forwards that delivers choice, despite the fact that a sizable minority in our communities can’t afford such expense. I don’t think consent is a good vehicle for addressing systemic economic inequality, there are better policy areas for tackling that problem; scoping it do so feels like the path to unworkable consent requirements in return for no actual impact on the underlying inequality. (Which is, absolutely, a problem that needs to be tackled.)

5. Sometimes data subjects will be in hard positions

Like someone with a broken arm, sometimes data subjects need a service that is being provided. The test for voluntariness will be whether they’re happy with the agreed exchange, or whether they feel like they’ve been exploited as a result of the position. It’s also ethically important to consider whether the person asking for consent had a role in putting them in that situation – a surgeon who breaks someone’s arm cannot be said to have sought voluntary consent upon offering surgery to correct it.

ICO fines 11 charities for abusing donors’ personal data

The ICO today announced that 11 charities are being fined for abusing their donors’ personal data over a number of years, among them some high-profile names such as Great Ormond Street and Oxfam.

ICO have a good overview of who they fined, and why, but I wanted to touch on the bigger picture briefly. Fining charities has some fairly obvious ethical implications; these aren’t organisations that are out to make a profit, they’re there for a social good, and the money that will pay these fines was donated by people who expected their money to fund these charities’ work.

On the other hand, the people who gave up their personal data along with their money have a set of fairly common sense rights enshrined in law, which derive from our Article 8 right to respect for private and family life. On balance, the argument that charities should be immune to financial penalties seems to be an argument that the ends justify the means, and so I can’t say that I find it all that persuasive – as much as I admire the work that these organisations do.

Longer term, the ongoing damage to these charities is likely to be reputational rather than financial. Many donors will, rightly in my opinion, feel aggrieved that their support was rewarded with secretive (and ultimately illegal) background checks and cross-referencing. As always, complying with data protection isn’t just about the law, it’s about the trust between individuals and the organisations that they engage with. Working within the guidelines set by data protection law doesn’t just avoid financial penalties, it’s a fantastic step towards building sustainable long-term trustworthy relationships.

On balance, we should probably be welcoming today’s action. Partly for protecting the rights of donors, but mostly because in the long term these trust-breaking practices will make it harder for any charity to build positive relationships with their supports.

At the University of Southampton’s Meaningful Consent Project, we’re trying to understand trusted, consentful relationships – and helping to design the tools and infrastructure that will help build them .  For a real-life example of consent management infrastructure, check out

RasPi Boot Service

Some quick notes about starting things at boot on the RasPi, because someone asked:

Basically, you need to register your program as a system service, which can then be started at boot (and, controlled using the normal service x start/stop/restart commands).

1. Create a .service file, eg /home/pi/path/to/myprogram/myprogram.service

Description=My New Service





2. I pointed the start/stop commands to shell scripts ( / – so create those

cd /home/pi/piot/pi/ # Change to program's working directory
(python & echo $! > /tmp/ & # This is a double fork - it lets the command keep running in the background after the bash shell itself exits
# The echo puts the pid of the process into a temp file so we can kill it later
// I haven't tested this...
kill `cat /tmp/`
unlink /tmp/

3. Register the service to start at boot

systemctl enable /path/to/myprogram.service

NB: kill sends a SIGTERM to the python process. This signal can be caught in order to trigger a graceful shutdown (or even ignored). If it’s not caught, the process will just be ended.

“Off you go!”: Why I don’t think we should be forcing kids to run

I don’t normally blog about anything other than consent/privacy/data protection these days; but there is another side to my PhD – wellbeing, health and exercise.  Broadly, these two apparently disjoint areas are joined together by a desire to understand how we design to accommodate complex human values and lives, and build technology that respects that diversity.  This post is, mostly, though, about experiences far predating my PhD research.

I just read (by which I mean gave a cursorily skimmed) an article on the Guardian debating whether school kids should be made to get a daily mile of running or walking in their routine.  The idea of running a mile fills me with horror.  I detest running.  It’s painful, boring and, frankly, the outside is never the right temperature.  These are pretty much the same reasons I have always hated running.  I hated other sports, too – Football, hockey, rugby; possibly because, to some extent, they involve running in themselves.  I still don’t really like competitive sports – what fun is there in being practically the slowest and falling over all the time; or standing in a field in the middle of winter; or trying to put on f***ing shin pads and football boots?

One particular low point came on school camp in Bude, in year 9.  I had tried the morning run on the first day; decided it was too painful, and presented the teachers with the prewritten excuse note I’d got mum to write.  They accepted it, somewhat grudgingly. (“Why can’t you do the swim?” they asked, although I think they knew that if they’d made me try and swim in the sea pool at 9am in the morning I would have drowned.)  Later in the week, we were taken, sans-teachers, to play ‘games’ on the beach.  One of which (peculiarly for something called a ‘game’) basically involved running up and down the beach.  I gave it a go, and, to my credit, managed a couple of beach-laps.  Then, as was typical, decided that the pain in my leg was probably not worth it.  So I stopped.

“Why aren’t you running?” asked the instructor.
“My leg hurts,”
“That’s a weak excuse,” he replied, “off you go.”

I was, basically, a pretty good pupil, and not one to disobey.  In my whole secondary school career, I had probably less than 20 debits (almost exclusively for not getting my homework diary signed –  I know, WTF?).  On reflection, my decision to basically ignore the c*** and walk off in the opposite direction to sit by myself, in floods of tears (because actually, telling a kid that isn’t lying that they are lying is a really crappy thing to do) was something of a watershed.

In retrospect, forcing me to do activities I hated was bad for my self-esteem and bad for fostering any sense of enjoyment in physical activity.  It led me to the conclusion that, fundamentally, exercise is awful, with no redeeming features – at least for me.  It encouraged a sense of helplessness in the face of physical activity, and a belief that I just couldn’t enjoy any of it.

These days, though, I do at least two exercise classes a week and have a fairly substantial collection of weights in my living room.  These are things that I enjoy, and that I look forward to.   It is common knowledge among many of my friends that Step Aerobics is the absolute highlight of my week.  I like the music, I like that it isn’t competitive, that it engages my mind so I have something to think about other than the discomfort.  I like that I can do it entirely for me and not some twat who’s shouting at me to do it.  What’s more, I’m actually pretty good at them!  V-step – YES. Reverse turn – YES. 2-minute plank – YES!  It’s hard to shake the feeling that I’m doing them in spite of my earlier experiences, though.  In spite of the fact that, instead of setting me up on a path towards an active life, the choice of activities and the way they were pushed in successive schools taught me lessons that I’ve had to unlearn, like “I hate exercise” or “I can’t exercise”.

Through my research, I’ve spoken at length to numerous interview participants who have shared, often very candidly, their own journeys around physical exercise.  Some of these people have always been active, others have come to it later and used it as a way to turn around stressful and unhealthy lives.  What is most striking, though, speaking to these people, is the diversity and dynamicity of their reasons for engaging in physical activity; and the often complicated stories about how they found an activity that really fits with what they care about.  I’ve spoken to nobody, nobody, for whom physical exercise is just about getting physical exercise.  It is not true that – beyond a reductive physical sense – any exercise is good exercise.  The “right” exercise is the one that makes you want to do it again, that fits in with the rest of your life in terms of logistics and goals.  Few, if any, teenagers will go for a run today because it might avert heart disease in 40 years time.  Plenty of people will go for a run because it’s a chance to socialise, to listen to music or to explore the countryside, though.

A single, reductive, approach like “run a mile” is the complete opposite of the rich serendipitous journeys that lead most people to finding those activities that are right for them.  It is actively unhelpful because, for those people who don’t like running, it too often translates into a blunt rejection of all exercise, and a missed opportunity to find something that will engage them.

If we’re serious about getting an active population, we need to help people discover the activities that work for them; and that should start with schools.



In about 2002, after a decade of resenting numerous otherwise reasonable teachers for making me run, I was largely vindicated in my consistent opposition by medical proof that “my leg hurts” was not a weak excuse, but the actual bona-fide result soft-tissue problems in my right leg and foot.  I still struggle to get my right heel flat on the floor; and my calf muscle is noticeably smaller.  For a long time, because of my experiences trying, I thought it stopped from me doing serious exercise.  I can do aerobics, step aerobics, total tone and weight lifting, though (albeit slightly wonkily).

On surveillance by machines

Last Thursday I attended a workshop on consent where (among other things) Andrew McStay of Bangor University was presenting some of his work on people’s reactions to “Empathic” media; specifically adverts that are able to measure human responses and adjust accordingly.  Understandably, there is significant interest in this from the marketing industry.

This sort of surveillance raises a few interesting issues; in the context of consent it raises the question of how relevant consent is outside of Data Protection and Privacy which is where we typically think about it.  Sensing the emotional state of an unknown person who passes by an advertisement is unlikely to be covered by data protection legislation, since the data is unlikely to be personally identifiable.  Still, though, we might consider it to be something that should require the subject’s approval.  As I alluded to in my ongoing series of posts about technology and empowerment, control over personal data processing seems to be just the start of a more general question of control over technology.  At the moment, most of our technology is concerned with processing data and so data is where the control problems have manifested themselves.  The IoT, and advances such as empathic media, start to demonstrate how individuals might want control over technology that goes beyond just controlling what we currently define as personal data.

The second issue, that I want to focus on here, is the extent to which being observed by an machine (in this case an advert on a bus stop) is the same as being observed by another human being.  As another participant at the workshop pointed out, sales people have always responded to the emotions of the consumer;  you can try to upsell to a happy buyer, or back off if the customer is getting annoyed or angry.  This is a legitimate point;  few of us would feel uncomfortable at a sales person knowing how we feel – that the other person has a sense of empathy is implicit in most human interaction.  Personally, I can’t say that I’m so comfortable with a machine that attempts to do the same.  I’ve been thinking about what the difference is; why am I uncomfortable with a machine sensing how I feel but not a sales assistant?

In short, what’s the difference between a human observer and a miscellaneous electronic widget?

Visibility: Humans are, at least in comparison to modern technology, easily recognisable and actually pretty big.  What’s more, human eyes are necessarily co-located with human brains and human bodies.  Being surveilled directly by a human is, in practical terms, easier to avoid than being surveilled via a tiny piece of technology.  You’re simply more likely to know about the presence of another person, and therefore able to opt-out of their presence if desired.  What’s more, it’s hard for a human to avoid this.  No matter how hard they try, humans will never be able to hide as easily as a CCTV camera can.

Persistence: Humans don’t record information in the same way as a machine can.  Even when people have good memories, we don’t give eye witness testimony the same weight as we give, say, CCTV images.  We readily accept that human accounts can be mistaken or fabricated in a way that the high-fidelity accounts that technology creates typically aren’t.

Transfer: There’s a two-to-one (at most) relationship between human eyes and human brains.  There’s no possibility of sharing what I see (or have seen) with another human being, short of physically getting them into the same place as me.  Compare this to technology, where a video stream is easily copied, broadcast, recorded, replayed and shared.

Of course, each of these things could be achieved technologically.  We can easily build devices that are visible, make no persistent record (or even insert deliberate errors to make their accounts somewhat unreliable) and which don’t share the sensed data with other people or devices.  None of these things can be guaranteed to the same extent that they can with human beings, though.

Being surveilled, analysed and tracked by technology is qualitatively different to being surveilled, analysed and tracked by actual people precisely because technology has capabilities beyond those of humans and because there is no easy way to verify exactly which capabilities a given widget has.

We’re all unreliable liars stuck inside our own heads; and those are nice properties to have in someone that is watching and analysing you, because in some way they put limits on how the information can be used and where it will end up.  I don’t have to trust you to be those things, I know they’re true because you’re human, like me.

‘Smart’ Things: Making disempowerment physical?

This is the second in a series of posts about the crisis of intelligibility and empowerment in modern technology. If you’ve not read the first post, “Technology Indistinguishable from Magic,” that might be a good place to start.

The Internet of Things (IoT) is set to continue as the Hottest Thing in Tech ™ in 2016, and is receiving huge attention from industry and bodies such as the UK’s Digital Catapult. There is clear promise in the idea of using established communications technology (TCP/IP) and infrastructure to control and orchestrate previously disconnected objects, or to enable entirely new classes of device such as smart dust.

Of course, the IoT goes beyond just replacing existing control mechanisms like physical knobs and buttons with an API that can be accessed over the network. IoT taps into big data, machine learning and other state-of-the-art computer science techniques to bring devices that can operate with less user input. Taken together, these features of the IoT have the potential to transform our environments from the dumb analogue world we wander around in today into “smart”, interconnected digital systems that respond to our presence, the task we’re working on, the time of day or even our mood. Of course, the IoT also taps into less desirable “features” of today’s software engineering and business practices such as centralisation, obsolescence and privacy-as-an-afterthought. In keeping with the theme of this series, there is the very real possibility that the IoT-enabled environments that are beginning to emerge will disempower the unfortunate humans that are set – voluntarily or otherwise – to inhabit them.

One of the first mainstream IoT devices was the Nest – the smart thermostat (now owned by Google) that promises to lower bills by learning your routine and adjusting your heating appropriately. On the face of it, this is a great idea. There are clear benefits, from a user-experience and environmental perspective, of such a device. Even with issues such as a reliance on internet connectivity (and availability of the central Nest service) aside, there could be a serious intelligibility cost to this type of smart device, though; can users introspect why the heating is in a particular state, and correct that if required? Existing research, such as that by Yang and Newman (, suggests that users have trouble understanding why the Nest has set a particular temperature, or how to control the learning of the Nest effectively. That one of their participants described the Nest as “arrogant” sounds like a strong indicator that the device is disempowering them in some way, by imposing a ‘will’ all of its own.

The IoT has a data element; by embedding sensors in the environment around us the IoT will have the ability to collect a wide range of data and attribute that to individuals, or at least small groups of individuals. In many ways, techniques such as Privacy by Design (mandated by the GDPR) will help to achieve this goal, although only if we have solid understanding of how users reason about these devices, and how to present explanations and choices in a way that makes sense to them.

Potentially more novel, though, is the way in which the IoT transforms algorithmic decision making from something that has consequences for our personal data to something that has consequences in our physical environment. Having already been hugely disempowered with regard to their personal data, will the IoT spread this disempowerment into users’ physical environments, too? In my opinion, the answer to this questions seems to be an almost certain Yes, unless we learn from our mistakes in the personal data context and apply the lessons to the smart devices that the IoT promises to surround us with.

In a general sense, the learning functions of the Nest move us from a declarative interaction model (setting the desired temperature) to an inferential one, in which there is no longer a clear and easily articulated link between a user action (setting the desired temperature) and the actions taken by the system. There can be little doubt that, when users are prevented from understanding why something has taken place, or how they can correct it in future, they become less able enact their own desires. Crucially, this needn’t be by design. In fact, the intelligibility problems apparent in the Nest seem to be in spite of the desire of engineers to help users better control their thermal comfort and energy bills.

Declarative interfaces, despite being “dumb” are fairly easily understood. Broadly, declare your desire and let the system work to achieve and maintain it. In practice, a thermostat can be used in two different ways: Set the desired temperate and leave it alone; or use it as a binary on/off switch for the heating by turning it between the two extremes. A common misuse of a thermostat is to treat it as if it were a sort of thermal volume control – turning it up to increase the thermal output of the boiler and down to decrease it. That is not how most heating systems work, and this strategy is more effortful for the user than just setting the desired temperature and leaving the thermostat to turn the heating on and off as necessary. This points to the fundamental need – well established in HCI – for users to have an accurate model of how the system works in order to debug it. In practice, in the case of a conventional thermostat, even the erroneous model allows users to “debug” the heating; when it’s too hot, turn the dial to the left and when it’s too cold, turn it to the right. The resulting strategy may not be optimal, but the device is intelligible to the point that virtually everyone is empowered to control the temperate of their living room. Few, if any, people would accuse a conventional thermostat of arrogance.

The simplicity of declarative interfaces is precisely why they can lead to sub-optimal results, though. Leaving the intelligence with the human necessarily introduces their own bounded rationality into the operation of the system. Conventional thermostats lead to higher than necessary heating bills because they are constrained by the requirement that the user correctly declare the most appropriate temperature. An appropriate temperature for an occupied house might be determined by comfort, but in an unoccupied house it’s probably determined by the need to prevent the pipes from freezing, or to allow the temperature to be raised to a comfortable level relatively quickly when the occupant returns. It is this sort of “double-loop” learning that smart devices can use to introduce efficiency. Not only can they take into account what temperature the user feels most comfortable at, but also whether or not they’re at home to feel comfortable or not.

Inevitably, though, these devices will be wrong some of the time. Human beings have complex schedules that are influenced by everything from work commitments to their physical or mental health; sitting at home with the flu is an inopportune moment for the smart thermostat to decide 5°C is an appropriate temperature for your home. There are two responses to this problem; build smart devices that are never wrong, or build smart devices that are intelligible and flexible enough for your poor snotty user to correct. My working hypothesis that only the latter is a viable option.

When interacting with a smart device there are two concerns for the user to consider, though; the immediate situation (it’s too cold) and future situations (will this affect the temperate next week, when they’re back at work?). In order confidently use smart devices, users will need to be able to reason about the effects their actions will have both now and in the future in order to pick the best course of action. Ideally, the device will also be flexible enough to accommodate a temporary aberration from the norm, but even if it isn’t then knowing that it will need to be corrected later on will potentially avert another mistake in a few days time. Part of the solution to this challenge is undoubtedly to adopt models that are easily predictable, the other is to offer some means to inspect how a decision was made. Knowing the information that led to the current state will help users to to correct it, and to improve their understanding of how the device reaches decisions. In the case of device that combines multiple inputs, knowing if the temperature has been reduced because of an appointment in your calendar, or because a movement sensor determined that nobody is at home is key to rectifying the situation and preventing it from recurring in future.

This concern needs to be addressed whether it’s a question of how hot someone’s home is, how bright their lights are, whether now is a good time for the robotic hoover to go to work, or whether the front door will open. Ultimately, the goal of the IoT should be to produce devices that make our lives easier; or at least no more difficult. The smartest devices of all will embrace the fact that they’re not really that smart at all. Instead, they’ll give their users the knowledge and control that’s required to take control of their environment and to exert their own agency.

 In this series I’ll expand on the idea of technology as a disempowering force, covering the need to make empowerment part of the standard design vernacular and how we might do that. Subscribe to the RSS feed, or follow @richardgomer on Twitter to make sure you don’t miss the next post!

Technology Indistinguishable from Magic: A crisis of technological disempowerment

This is the first in a series of posts looking at the crisis of intelligibility in modern computer systems, and the threat that this poses to individual empowerment if we don’t get to grips with it.

As expected, the final text of Europe’s new Data Protection Regulation puts more emphasis data subject consent than the previous Directive. There’s a legitimate debate around whether consent is the right approach to data protection, or whether it’s just a distraction from more effective regulation, but in this series of posts I want to explore a broader, but related, and important, but often ignored, issue: The very real problem of technological intelligibility and the risk of technology disempowering everybody.

It should be stated, so I will, that my opinion is shaped hugely by a liberal philosophy; I like the idea of consent, and the EU approach to Data Protection precisely because it gives consumers rights over their personal data, rather than setting absolute limits on precisely what service providers can or can’t do. In Europe, if you consent to your data being processed then it’s pretty much fair game. If you want to find out how it’s being processed, or what is held, or challenge that processing, though, then you (as a human being, not necessarily an EU citizen) have a set of rights to help you do so. This appeals to me because it empowers individuals as intelligent (if not necessarily rational) agents. If you want to sell your genome because an advertising company offers you a vast sum of money for it, you can do so; but if you don’t want an advertising company to process your genome then you have the right to challenge it if they try.

To me, as a liberal (with a big and a little L), individual empowerment – the ability for individuals to choose and to shape their own lives – is of fundamental importance. To me that means challenging state power and social inequality, as well as other factors such as patriarchy and, as I’ll explore through these blog posts, the ways in which technology shapes our opportunity, choices and lives.

I’m a technologist by training, and I do believe that it has the potential to improve the lives of human beings and to create a fairer and more liberal society. I don’t think that is a given, though; technology can obviously be a distraction from problems, an ineffective smokescreen that gives the appearance of doing something without actually helping, or it can actively work against our interests. This critical viewpoint is one that the industry as a whole (and an often sycophantic media) often ignores, and is something I’ve tried to champion while an editor of XRDS magazine. My work as part of the meaningful consent project, and my PhD thesis, has brought me to the conclusion that we, as an industry and a society, may be stumbling blindly towards a future in which the potential of digital technology is missed, and which instead of supporting citizens to reach their potential fundamentally disempowers them.

For a long time, we’ve trumpeted the idea of advanced technology being indistinguishable from a magic as an achievement, as an implicit design goal or something to strive for; the technology industry is largely proud of being ‘magical’. Magic, though, is almost by definition the preserve of a few ‘gifted’ individuals, at best unintelligible to most and at worst completely unavailable to them. Magic is about power, that’s why it makes for exciting stories; and it is about being mysterious, which is why those stories needn’t explain how it actually works. Our most compelling stories about magic inextricably link magical ability to the antagonist – whether it’s as a tool to undermine the autonomy of others (as Sauron attempts in the Lord of the Rings), a justification to subject non-magical others to your whim (as Voldemort in Harry Potter) or a malevolent influence in and of itself (as the Dark Side of the Force in Star Wars). Basically, magic never goes well.

I think that the unintelligibility of magic is what makes it so troublesome. It’s the unintelligibility that makes is both unpredictable and exclusive. If we can’t predict, we can’t shape our environment to our own ends. If something is exclusive then those with access to it have a disproportionate power over everyone else. Magical technology that we don’t understand, cannot predict the consequences of, and do not have the ability to master does not empower us. At best it just is and at worst it shapes our lives as a result of someone else’s intent (or inattention).

Nowhere is this better demonstrated than in the web’s ubiquitous advertising network. Natasa Milic-Frayling (then at Microsoft Research), Eduarda Mendes Rodrigues (then at the University of Porto), m.c. schraefel (my academic supervisor) and I demonstrated the sheer extent of the tracking to which we are subject in our 2013 paper. Using search engines as an entry point into the Web we crawled thousands of pages to uncover the invisible network of advertisers, brokers and content providers that work together to collect information about the web pages we visit and to transfer that data among themselves to deliver advertisements individually tailored to our supposed interests. It takes just 30 clicks before the average web user has a 95% chance of being labelled by all of the top 10 tracking domains. Despite the invisibility of these networks themselves, some of the organisations participating in them are household names; Google, Facebook, Twitter – all are deeply implicated, and all have access to far more data about us than merely what we type into their own websites.

The ability for a website we’ve never visited before to deliver an advertisement tailored to our interests, or to a status update we posted “privately” to Facebook the previous day, even to our household income or our credit rating, is magical. Most of us probably wouldn’t say it was magical in a good way, more at the Voldemort end of the scale than the Gandalf side of things; it is magical nonetheless. In particular, it is unintelligible to most of us, even the websites that benefit from the advertising revenue. In fact, the complexity of the emergent network itself means that the actual extent of the data brokerage is probably beyond the understanding of most of the organisations involved in it.

The unintelligibility of the advertising network makes it virtually impossible to understand what the profiles it has created for each of us contain, or how we can influence them. This does not make for positive or fulfilling experiences; most web users perceive aggressive ad targeting as creepy or downright disturbing. Despite having dedicated years of research to the topic, I am still unable to account for some of the targeting that’s apparent when I browse the web, and I’m still unable to prevent much of it from taking place. We are all hugely disempowered by the existence of this “grey web”; we can’t opt out even if we want to. It is fortunate that, with some notable exceptions, despite the creepiness it is largely not a major threat to most of us.

Still, the grey web is just one way in which most of us have become (or at least feel that) we are pretty disempowered when it comes to exercising control over our personal data. Only yesterday the DVLA sent my driving license renewal letter to my flat in Southampton; not the address that appears on my license (which is registered to my parents’ house) after apparently checking “the last address you gave us with records held by a commercial partner.” How I would correct that record if it were wrong is anybody’s guess – There’s no insight into the magical process that they used and their description seems to provide little clue as to what they actually did or who they asked.

In this series of (hopefully weekly) posts I’ll expand on the idea of technology as a disempowering force, covering the need to make empowerment part of the standard design vernacular and how we might do so. Subscribe to the RSS feed, or follow @richardgomer on Twitter to make sure you don’t miss the next post!

Asking about gender in research

STILL A DRAFT, I might tweak it later…

On a couple of occasions lately I’ve had cause to query how gender is being asked about in research studies at the University.  I wanted to make some notes about my thoughts on what is potentially a confusing and difficult area – for scientific and social reasons – that I can point people to when the issue comes up.  I’m not an expert in gender, or in research. So, grab a pinch of salt before reading on.

Screenshot from 2015-12-07 16:02:27Figure 1: The problem

Apparently the University of Southampton does recommend that an “other” option is provided when asking about gender.  I was unable to find the relevant guidance, but have emailed the RGO and will update this post if they can point me to it!

These are just my thoughts, so input from researchers and research participants is welcome, in the comments or by email (r dot gomer at soton dot ac dot uk).

Here’s a typical scenario: “I’m doing a survey.  I want to collect basic demographics about participants for analysis, or just to check how representative my sample is, and I’m going to ask about gender”.  In practice, there are two questions to grapple with here.  1) Should you be asking about gender, and 2) what options should you give to participants?

Aside: Gender vs Sex
A lot of researchers might not have thought about what gender really means* – So here’s a quick note on gender vs sex.  Sex, typically, is a biological (or genetic) concept – It tends to effect things like how tall people are.  Gender is the social construct that (typically) arises from sex.  It’s the set of social expectations about how men and women behave – How they dress, their role in a family, how they behave, or (thankfully less common these days) what jobs they should or shouldn’t do.  Gender arguably has more of an impact on most of us than sex, even if for most people there is a direct mapping from biological sex to the corresponding gender.

* something that seems to be missing from the UK curriculum…


About 0.4% of people identify as a gender other than what they were assigned at birth.  It could be that they identify as the “other” gender, or reject a gender label altogether, feeling themselves neither male nor female.  That’s 1 in 250 people.  Not many, but uncommon either.  In a survey of 1000 people, you can pretty much guarantee that some participants mightn’t think either “male” or “female” is a good description of their gender.

Principle 3 of the Data Protection Act 1998 is that “Personal data shall be adequate, relevant and not excessive in relation to the purpose or purposes for which they are processed.”  In practice, this means that (if your participants can be identified from the data you collect) you have a legal obligation to ensure that you’re only collecting the data that are actually required to conduct your research.  If participants are not identifiable (eg in an anonymous online survey) then this isn’t a legal requirement; but it feels like good research practice not to collect data that isn’t necessary to answer your research questions.

As mentioned above, there are two general reasons you might ask about the gender of your research participants:

1) To ensure (or demonstrate) that your sample is representative, or at least to contextualise the data.
2) Because you expect (and will either look for, or control for) gender effects.

Both sound like valid reasons to collect gender, but have different implications for research (below).  Still, in many studies you might not expect to see gender effects, and if you’re not going to test for them then why bother collecting that data at all?

If you ARE testing for gender effects, then you could consider whether identifying as a binary gender should be part of your study inclusion criteria.  If you know you can’t recruit enough participants that identify as something other than male or female to get a statistically meaningful result, then you should consider whether it is ethical to ask those people to give up their time to take part in your research.  Of course, if gender effects are only one of many analyses (for most studies, this is almost certainly the case), then excluding participants on these grounds is likely to be unjustified (and arguably worse than only providing a binary choice).  Perhaps consider just excluding those participants who choose something other than “male” or “female” from the particular tests that relate to gender identity.

So, in practical terms, what should you ask participants?  For most research, where gender is necessary for descriptive purposes or some minor analysis (ie most research) one of the following is probably a good starting point.  I’ve taken the options phrasing from the EHRC guidance on asking about gender for monitoring purposes.

Free text Three Options Four Options
What is your gender?
 Which of the following describes how you think of yourself?
+ Female
+ Male
+ In another way
 Which of the following describes how you think of yourself?
+ Female
+ Male
+ In another way
+ Prefer not to say
  •  Participants can express their gender in their own terms.
  • The most flexible approach.
  • Immediately quantitative
  • Broadly covers everyone, without the use of a clumsy “Other” option
  • Immediately quantitative
  • Broadly covers everyone, without the use of a clumsy “Other” option
  • No pressure to answer
  • Data needs to be coded, probably by hand.  EFFORT.
  • You still need to assign categories in order to do any quantitative analysis.  Unless you’re going to let those categories emerge from the data, then you might as well specify them directly for participants to choose from.
  •  Some participants might feel pressured to answer.
  • If gender is an important aspect of your research, this might cause you to miss out on data.