Opinion
Should Facebook have experimented on 689,000 users and tried to make them sad?
In the experiment, users were split into three groups and posts which contained either positive or negative words were screened from the users' news feed. One of the groups acted as a control and had random posts screened from their feeds. They then counted the percentage of emotion words that the test subjects used in their own posts.
The results showed that there was a very small, but statistically significant result. People who had fewer positive posts shown to them reduced their own use of positive words by 0.1% and increased their use of negative words by 0.04%. Conversely, people who had fewer negative posts shown to them increased their use of positive words by 0.06% and decreased their use of negative words by 0.07%.
The emotional responses shown by the unwitting participants in the study are nothing compared to the sense that Facebook, as a private company, has taken another step too far in the use of its network and created mistrust and resentment in its user community.
Although the experiment may not have breached any of Facebook’s user agreements, it is clear that informed consent was not obtained from the participants of the research. The study itself allegedly received approval by the Institutional Review Boards at the researchers' universities. According to the article’s editor Susan Fiske, this was given on the basis that “Facebook apparently manipulates people’s News Feeds all of the time”.
Professor Fiske, a psychologist at Princeton University who reviewed the paper said that she was “creeped out” by the nature of the research. Despite this, she believed that the regulations had been followed and there wasn’t any reason the paper should not be published.
The ethics of good research
We don’t know the full nature of the ethical clearance that was given to the researchers from their respective universities and so it is hard to comment fully on the nature of the approval they were given for the research to go ahead. If this was indeed on the basis of Facebook’s agreement with its users, then it would be fair to say that this was a very liberal interpretation of informed consent.
Facebook’s Data Use Policy only says that it has the right to use information it receives for research. It does not make explicit that this involves actually carrying out experiments designed to manipulate emotions in their customers, especially not negative ones.
Federal US guidelines on human research provided within the “Common Rule” are quite clear about what is and isn’t acceptable in this type of research. It includes details of how informed consent must be obtained and the information, including the risks and benefits, of them being involved. They must also be allowed to opt out of the research. Although Institutional Review Boards are required for organisations conducting research funded by or on behalf the Government, private companies are also signatories to the regulations.
The fact that the researchers and Facebook did not ask for consent suggests that they knew that there would be a backlash when it became public and that it would be easier to deal with this after the fact.
Right now, the researchers involved are not allowed to answer questions on the research and this is being handled by Facebook itself.
What did the research itself prove?
It is not at all clear that the research actually did say very much concerning the transfer of emotional states via emotional contagion as it stated. The measurement of the frequency of emotion words in very short status updates is clearly not a measure of the overall emotional state of the writer.
Even if it were, the results of the experiment found differences of 1 word in a 1,000 in the number of emotional words used between the experimental and control groups. Remember that this is the number of positive or negative words used not the total number of words written. At the level of the individual, these differences are meaningless and hardly a demonstration of “emotional contagion”.
Big Data brings with it the naive assumption that more data is better when it comes to statistical analysis. The problem is, however, that it actually introduces all sorts of anomalies especially when dealing with extremeley small differences that appear in one single measure at scale.
There may yet be another twist to this story. Given that it would be particularly strange that a prestigious journal would publish what seems to be quite weak research, perhaps this is all part of a bigger experiment to see how society reacts, especially on Facebook, to the idea that Facebook believes that its customers are actually just test subjects to be examined at will.
David Glance does not work for, consult to, own shares in or receive funding from any company or organisation that would benefit from this article, and has no relevant affiliations.
This article was originally published on The Conversation.
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Google's Larry Page wants to 'save 100,000 lives' by analyzing your healthcare data
Talking up the power of big data is a real trend at the moment and Google founder Larry Page took it to new levels this week by proclaiming that 100,000 lives could be saved next year alone if we did more to open up healthcare information.
Google, likely the biggest data owner outside the NSA, is evidently carving a place for itself in the big data vs life and death debate but Page might have been a little more modest, given that Google’s massive Flu Trends programme ultimately proved unreliable. Big data isn’t some magic weapon that can solve all our problems and whether Page wants to admit it or not, it won’t save thousands of lives in the near future.
Big promises
Saving lives by analysing healthcare data has become a major human ambition, but to say this is a tricky task would be an enormous understatement.
In the UK, the government has just produced a consultation on introducing regulations for protecting this kind of information alongside care.data, a huge scheme aiming to make health records available to researchers and others who could work with it.
Given the ongoing care.data debacle, this is a broadly sensible document and a promising start for consultation. In particular, it identifies different levels of data. Data that could be used to identify an individual person should not be shared in the same way as other types of data.
But, like Page, the UK government is also presenting a false vision for big data. It has said review after review have found that a failure to share information between healthcare workers has led to child deaths. It’s an emotive admission but rather beside the point in the big data perspective.
It is indeed entirely credible that many tragic failures within the NHS might have been prevented by someone sharing the right information with the right person. Sharing is essential, but when the NHS talks about sharing, it means linking and sharing large medical databases between organisations. Surely no case review has ever claimed that the mere existence of a larger database of information would have got the right knowledge to the right person.
Medical data sharing may be a good thing in many ways, but unfortunately there is no clear case yet that it prevents child deaths and other tragedies. It is only big data, not magic. Preventing child deaths appears to be brought in as emotional blackmail, expected to trump the valid concerns over the NHS' big data plans.
Big disappointments
The fact is, we are not as advanced as we would like to believe. This month, 60 years after Alan Turing died, his test for recognising “true” artificial intelligence made the news again. One in three human test subjects mistook a computer programme called Eugene Goostman for a 13-year-old Ukrainian boy. But Eugene didn’t really pass the test. The programme was simply good at playing the game and relied heavily on the fact that a 13-year-old probably wouldn’t know the answers to many of the questions.
The programme fell back on the same tactics used some 42 years ago by Parry, a programme that tricked people into thinking it was a paranoid schizophrenic, and the even earlier Eliza programme which had proved hard to distinguish from a real Rogerian therapist. So much for progress.
The research field of artificial intelligence – or more modestly, machine learning – has been active for 60 years and passing the Turing test is its original Holy Grail. And many of the brightest minds in computer science have worked in this area. Computing power has been increasing exponentially over that time and the web provides a massive amount of samples of human communication to learn from. The fact that we have made such slow progress despite all these developments shows just how hard it is to turn vast amounts of data into human intelligence.
Be wary of big claims
This should teach us to be wary of anyone who makes bold claims about the potential of big data. Google Flu Trends sought to derive information about the spread of illness by gathering data when people searched for terms like “flu”. But we’ve seen time and time again that machines don’t understand humans and can’t mimic real human qualities.
A prime example can be found outside healthcare. It’s now broadly accepted that in the course of its surveillance programmes, the NSA had obtained information that might have prevented 9-11, but failed to join the dots.
Edward Snowden’s revelations made it clear that the NSA and GCHQ are collecting large “haystacks” of communications data. The intelligence services have made various claims that the analysis of this prevented serious terrorist attacks, but these claims have not stood up to detailed scrutiny. Given the amount of computing power the NSA possessed, even before the internet age, it must have been applying machine learning techniques to its bulk data for at least 30 years. Still, no evidence has been presented of any significant needles being found as a result – at least not any that is available to the public.
This all goes to show that using machine learning to process vast amounts of data, such as the information held in healthcare databases, won’t save lives alone. The kind of human insight needed to put the information to proper use still can’t be replicated by computers, even after decades of trying.
Doctors need to be able to ask the right questions and use their unique human qualities to make life changing decisions for their patients. Similarly, researchers still need to formulate their hypotheses and ask the medical databases targeted questions. They are not machines, and we should be grateful for that.
Eerke Boiten is a senior lecturer in the School of Computing at the University of Kent, and Director of the University's interdisciplinary Centre for Cyber Security Research. He receives funding from EPSRC for the CryptoForma Network of Excellence on Cryptography and Formal Methods. He is a member of BCS and board member of its specialist group on Formal Aspects of Computer Science. He is also a director (governor) of The John of Gaunt School, a Community Academy.
This article was originally published on The Conversation.
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Why haven’t we encountered aliens yet? The answer could be climate change
Enrico Fermi, when asked about intelligent life on other planets, famously replied, “Where are they?” Any civilisation advanced enough to undertake interstellar travel would, he argued, in a brief period of cosmic time, populate its entire galaxy. Yet, we haven’t made any contact with such life. This has become the famous "Fermi Paradox”.
Various explanations for why we don’t see aliens have been proposed – perhaps interstellar travel is impossible or maybe civilisations are always self-destructive. But with every new discovery of a potentially habitable planet, the Fermi Paradox becomes increasingly mysterious. There could be hundreds of millions of potentially habitable worlds in the Milky Way alone.
This impression is only reinforced by the recent discovery of a “Mega-Earth”, a rocky planet 17 times more massive than the Earth but with only a thin atmosphere. Previously, it was thought that worlds this large would hold onto an atmosphere so thick that their surfaces would experience uninhabitable temperatures and pressures. But if this isn’t true, there is a whole new category of potentially habitable real estate in the cosmos.
Finding ET
So why don’t we see advanced civilisations swarming across the universe? One problem may be climate change. It is not that advanced civilisations always destroy themselves by over-heating their biospheres (although that is a possibility). Instead, because stars become brighter as they age, most planets with an initially life-friendly climate will become uninhabitably hot long before intelligent life emerges.
The Earth has had 4 billion years of good weather despite our sun burning a lot more fuel than when Earth was formed. We can estimate the amount of warming this should have produced thanks to the scientific effort to predict the consequences of man-made greenhouse-gas emissions.
These models predict that our planet should warm by a few degrees centigrade for each percentage increase in heating at Earth’s surface. This is roughly the increased heating produced by carbon dioxide at the levels expected for the end of the 21st century. (Incidentally, that is where the IPCC prediction of global warming of around 3°C centigrade comes from.)
Over the past half-billion years, a time period for which we have reasonable records of Earth’s climate, the sun’s surface temperature increased by 4% and terrestrial temperatures should have risen by roughly 10°C. But the geological record shows that, if anything, on average temperatures fell.
Simple extrapolations show that over the whole history of life, temperatures should have risen by almost 100°C. If that were true, early life must have emerged upon a completely frozen planet. Yet, the young Earth had liquid water on its surface. So what’s going on?
Get lucky
The answer is that it us not just the sun that has changed. The Earth also evolved, with the appearance of land plants around 400m years ago changing atmospheric composition and the amount of heat Earth reflects back into space. There has also been geological change with the continental area steadily growing through time as volcanic activity added to the land-mass and this, too, had an effect on the atmosphere and Earth’s reflectivity.
Remarkably, biological and geological evolution have generally produced cooling and this has compensated for the warming effect of our ageing sun. There have been times when compensation was too slow or too fast, and the Earth warmed or cooled, but not once since life first emerged has liquid water completely disappeared from the surface.
Our planet has therefore miraculously moderated climate change for four billion years. This observation led to the development of the Gaia hypothesis that a complex biosphere automatically regulates the environment in its own interests. However, Gaia lacks a credible mechanism and has probably confused cause and effect: a reasonably stable environment is a precondition for a complex biosphere not the other way around.
Other inhabited planets in the universe must also have found ways to prevent global warming. Watery worlds suitable for life will have climates that, like the Earth, are highly sensitive to changing circumstances. The repeated cancelling of star-induced warming by “geobiological” cooling, required to keep such planets habitable, will have needed many coincidences and the vast majority of such planets will have run out of luck long before sentient beings evolved.
However, the universe is immense and a few rare worlds will have had the necessary good fortune. It may just be that Earth is one of those lucky planets – a precious, fragile jewel in space. So, perhaps inevitably, climate change will remain a bane of the continued existence of life on such planets.
Next, read this: Habitable exoplanets are bad news for humanity.
David Waltham is the author of Lucky Planet (https://davidwaltham.com/lucky-planet/).
This article was originally published on The Conversation.
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