Dr Molly Grace, NERC Knowledge Exchange Fellow in the Oxford University Department of Zoology, discusses the potential impact of IUCN Green Species List, a framework for a standard way of measuring conservation success. A project that she and the team at the Interdisciplinary Centre for Conservation Science played a key role in developing.
What is the goal of species conservation? Many would say that it is to prevent extinctions. However, while this is a necessary first step, conservationists have long recognized that it should not be the end goal. Once a species is stabilised, we can then turn our attention to the business of recovery - trying to restore species as functional parts of the ecosystems from which humans have displaced them. However, to do this, there must be a rigorous and objective way to measure recovery.
Imagine this scenario: A species is teetering on the brink of extinction. In fact, it has been classified on the IUCN Red List of Threatened Species (the global standard for measuring extinction risk) as Critically Endangered. You rally a global team of scientists, conservation planners, and land managers to put their heads together and figure out how to save this species. This team works relentlessly to bring this species back from the edge, and little by little, the species improves. After years, or even decades, of work, the team achieves its goal— the species is no longer considered threatened with a risk of extinction! However, no one is celebrating—in fact, the mood has become decidedly sombre.
There is a simple reason for this apparent paradox, due to limited conservation budgets, species which are classified as being at risk of extinction are preferentially awarded funding. While this makes sense at a wide scale - of course we should be working hardest to save the species which face an imminent risk of vanishing from the planet - it poses a problem for species who have benefited from concerted conservation actions and are no longer in the “danger zone.” Once the threatened classification vanishes, often so does funding. Without continued protections, species may slip back into the threatened category, nullifying the effect of decades of work. Thus, there is a perverse incentive to stay in the exclusive “highly endangered” club - at least on paper. But this prevents us from celebrating the huge difference that conservation can make.
With the creation of the IUCN Green List of Species, we hope to reverse this perverse incentive to downplay conservation success. The Green List, still in development, will assess species recovery and how conservation actions have contributed to species recovery. It will also calculate the dependence of the species on continued conservation, by estimating what would happen if these efforts stopped. This can be used as an argument for continued conservation funding. With the Green List working in tandem, we can stop thinking of Red List “downlisting”— moving from a high category of extinction risk, to a lower one—as a demotion which disincentivises funding, but rather see it for what it truly is: a promotion which should be celebrated. The framework would be applicable across all forms of life on the planet: aquatic and terrestrial species, plant, animal, and fungal species, narrow endemics to wide-ranging species, you name it.
In our recent paper, we presented this framework, which will potentially measure recovery and work in tandem with the assessment of extinction risk (IUCN Red List) to tell the story of a species. For example, a species that is in no danger of disappearing from the planet (Red List assessment) might nonetheless be absent from many parts of the world in which it was previously found, and so cannot be considered fully recovered (Green List assessment). The local loss of a species can have cascading effects on the rest of the ecosystem.
The Green List of Species also assesses the impact that conservation efforts have had, and could have in the future. For example, the charismatic saiga antelope (Saiga tartarica), found throughout Central Asia, is currently considered “Critically Endangered” on the Red List. However, our Green List assessment shows that in the absence of past conservation efforts, many more populations would be extinct or in worse shape today. We also show that with continued conservation, the saiga's future prospects are bright—a low risk of extinction, reestablishment of populations where they are locally extinct, and some functional populations.
We hope that the Green List of Species will help to encourage and incentivise more ambitious conservation goals, moving beyond triage at the edge of extinction.
What are the new 'one-stop shops' for less obvious cancer symptoms, and how is this service being developed and evaluated in Oxfordshire? GP and Clinical Researcher Dr Brian D Nicholson, from the Nuffield Department of Primary Care Health Sciences, is part of the team who developed the region’s pilot site, one of ten across the country, and explains why understanding non-specific symptoms is important.
Last week the New Scientist reported 'around half of people with cancer have vague or non-specific symptoms, such as loss of appetite or weight. As a result, they can end up being referred to several specialists before receiving a diagnosis.'
The Telegraph added 'new "one-stop shops" to speed up cancer diagnosis are being trialled across the country for the first time. GPs can refer patients suffering from "vague" symptoms… to undergo multiple tests for different cancers.'
Cancer Research UK’s director of early diagnosis, Sara Hiom, was quoted: 'We’re confident that these ten pilot centres will give us a much better understanding of what’s needed to speed up the diagnosis and treatment of people with less obvious symptoms, improve their experience of care and, ultimately, survival.'
The public response was mixed, ranging from outrage over another postcode lottery of NHS resource allocation, to reminders about the initiative’s nature as a pilot which is subject to evaluation before being rolled out nationally.
I’m part of a team of GPs and health researchers at the University of Oxford’s Nuffield Department of Primary Care Health Sciences (NDPCHS) who conduct research into non-specific cancer symptoms and developed the “one-stop shop” pilot site for Oxfordshire. Known as the Suspected CANcer (SCAN) pathway, we’ve developed this in close collaboration with clinicians, researchers, commissioners, and heath care professionals from the Oxfordshire Clinical Commissioning Group and Oxford University Hospitals NHS Foundation Trust (OUH).
Our site is one of ten diagnostic centre pilot sites set-up by local health care teams as part of the Accelerate Coordinate Evaluate (ACE) programme, funded by NHS England, Cancer Research UK and Macmillan.
Why non-specific symptoms?
Specific cancer symptoms point to cancer of a specific body location. For example, coughing up blood can be linked with lung cancer, while difficulty swallowing can be linked with cancer of the food pipe (the oesophagus). Specific symptoms give a clear signal to GPs about where in the body to perform tests for cancer, and the NHS provides GPs with rapid access to tests.
Non-specific cancer symptoms can be more challenging for GPs and include tiredness, loss of appetite, tummy pain, feeling generally unwell and weight loss. They are more common and are linked with several non-cancer (benign), long-term and short-term conditions seen by many GPs. What is more challenging is that non-specific symptoms can also be linked with cancer of more than one part of the body, making it more difficult for GPs to choose the most appropriate investigation to perform first.
The NHS, until now, has not been set-up to provide GPs with access to rapid tests to urgently investigate these non-specific symptoms across different parts of the body.
Weight loss, for example, is linked with ten different types of cancer and is the second most common symptom of colorectal, lung, pancreatic and kidney cancer. Our latest research on weight-loss shows that in the over 60s, the risk of cancer in patients presenting with weight loss is higher than previously thought (BJGP Link), so systems that fast-track investigations of unexplained weight loss to either diagnose or rule-out cancer earlier are urgently needed.
Ten centres, ten different approaches
The newly-launched cancer diagnostic initiative has been developed to evaluate what sort of 'one-stop shop' could be effective at diagnosing cancer in the NHS, building on similar clinics used in Denmark. This has taken a lot of time and careful organisation as each of the ten pilot sites have developed a unique “one-stop shop” approach that can be provided in their local areas. To learn the most about what makes an effective “one-stop shop”, the pilot sites were chosen in urban and rural locations, richer and poorer areas, they use different combinations of health professionals and local services, accept a range of non-specific symptoms, and use a variety of different tests.
Oxford’s one-stop-shop for cancer diagnosis
Our local pilot, SCAN, allows all GPs in Oxfordshire to refer patients aged 40 years or over if there is no other urgent referral pathway available, and if they are concerned about cancer or serious disease following a face-to-face consultation for a range of non-specific symptoms. These include: unexplained weight loss, severe unexplained fatigue, persistent nausea or appetite loss, new atypical pain, or unexplained results from a laboratory test. GPs may also refer if they have clinical suspicion of cancer or serious disease, or a 'gut feeling’ that their patient warrants investigation.
All patients referred to SCAN undergo a broad panel of laboratory blood and faecal tests and low-dose Computed Tomography (CT) imaging of the chest abdomen and pelvis. Depending on the results, they may then be referred to the SCAN clinic, where clinicians with expertise in evaluating non-specific symptoms evaluate them in more depth – they’ll be guided through the process by a specialist radiographer, known as the SCAN navigator. Depending on the results of the initial tests, the patient may otherwise get a rapid referral to another cancer pathway, or for a different serious disease.
Evaluating effectiveness, first and foremost
Importantly this initiative is collecting data, with patients’ consent, to evaluate each “one-stop shop” comprehensively. As more patients referred to these pilot sites grows and we collect more data, we will be able to determine what an effective service for patients with non-specific symptoms could look like in the NHS. Once the model has been refined, these one-stop shops will be expanded and made available right across the NHS so that all patients have the opportunity to access rapid investigation via their GP to speed up the diagnosis and treatment of their non-specific symptoms .
In the final part of our women in AI series, Dr Vidya Narayanan, a researcher at the Oxford Internet Institute and post-doctoral researcher on the Computational Propaganda Research Project, discusses her work understanding the effects of technology and social media on political processes in the United States and in the UK.
What is AI (In your own personal view)?
In my view, Artificial Intelligence is the ability of computer programs to make independent decisions with little or no human intervention and to adapt to new situations.
AI has been a subject for intense speculation and rigorous academic study since the 1950s, when Alan Turing asked if machines could think. While the theory of AI has seen continuous development since then, it’s only in the past decade, with our access to vast stores of data and the advent of graphics processing units that can process this data in parallel, that applications of AI seem finally ready to step out of the pages of science fiction books and have a profound impact on our everyday lives.
What are the biggest AI misconceptions that you have encountered?
As with any relatively new and powerful technology, AI too has the power to split opinion among technocrats, policy makers and the general public. To me, it feels as though we still lack the evidence to categorise specific notions about AI as misconceptions. There is little consensus among academicians and other AI researchers on a timeline for the development of Artificial General Intelligence (AGI) – a level of intelligence that allows a computer to handle any intellectual task that a human can. The onus is on us, as academicians, to continually assess the state of art of AI and communicate these findings in an accessible manner to the general public and build an informed consensus about AI among peopl
What do you think can be done to encourage more women into AI and what has your own personal experience been in the field?
This is a very important question and one that concerns me vitally as both as a woman and as a mother. The place to start is at school and work towards creating an environment where girls can interact with technology in a peer group setting. It’s vital to encourage them to think that they can be both consumers as well as developers of technology. I have been very fortunate to have worked with very supportive colleagues and mentors and have had an extremely positive experience working as a woman in AI. On a personal level, I would like to support endeavours to create such conducive work environments for women across the globe, particularly in technology.
What drew you towards a career in AI?
Back in the day when I was a graduate student at Pennsylvania State University in the US, the research team were working on decision-making problems in distributed systems that couldn’t be solved by conventional optimisation techniques. The paradigm of multi-agent systems that use reinforcement learning techniques to make decisions in dynamic and uncertain environments were among the various techniques we considered. This was my introduction to Artificial Intelligence and I moved to the UK to pursue a Phd in Computer Science in the Intelligence, Agents and Multimedia lab at Southampton, which was doing pioneering work in the area. Since then, I have been acutely aware of the immense potential of AI to kick start a new technological revolution and change our lives.
As a scientist, I wished to play an active part in creating some of these methods and this drew me towards a career in AI. More recently, I have been motivated by a need to use AI for social good and harness its capacity to solve some of the most pressing problems in the world, including equitable sharing of resources across the globe, examining the impact of social media interactions on democratic processes, the effect of private companies acquiring vast amounts of personal data and the potential for this to be misused by political campaigners - particularly in fragile democracies.
What research are you most proud of?
I returned to academia in November 2017 after a career break to care for my young children. Since then I have joined the Computational Propaganda project, exploring the role of social networks in spreading fake news and influencing electoral processes around the world.
My colleagues and I have been studying the effects of technology and social media on political processes both in the US and in the UK. In particular we have looked at bot activity on Twitter during the Brexit referendum and the spread of junk news among audience groups on both Twitter and Facebook. This is a fascinating area that brings together the disciplines of Political Science, Sociology and Computer Science, to strengthen democratic processes. I’m very motivated to extend this study by creating and using state of the art technologies to study political polarisation, junk news spread on social media platforms and misinformation campaigns by state and non-state actors to influence elections around the world.
What are the biggest challenges facing the field?
The biggest challenges for the field are to address the risks of AI which are very well documented. For example; disruption to jobs, wealth creation for a few individuals widening the social and economic divides and the issue of most of the innovation in AI being driven by private companies. I also think there is a need for policymakers to regulate the development of AI, so that we can make algorithms accountable and rid automated decision-making systems of inherent biases against sub-populations. We need to really harness the power of AI to create egalitarian societies around the world.
What motivates you most in your research?
I enjoy the challenge of using mathematical techniques, computer science and data sets to find solutions to real life problems. I’m acutely conscious of the fact that while in some parts of the world we are on the brink a ‘Fourth Industrial revolution’, there are others who haven’t benefited even from the first industrial revolution and lack access to food, water and electricity. My primary motivation is to build AI powered applications that address these issues by developing fundamental advances in the theory of AI as a computer scientist at Oxford University.
Who inspires you?
There are a number of people who inspire me: Ada Lovelace, Bertrand Russell, Martin Luther King and Alan Turing. I also loved the film Hidden Figures; the true story of Katherine Jonson, Dorothy Vaughan and Mary Jackson, three brilliant African-American women that worked at NASA and played a key role in the space race, getting John Glenn to orbit the Earth. It has a brilliant cast (Janelle Monae, Taraji P. Henson and Octavia Spencer) and is incredibly inspirational – particularly for women in STEM.
FIND OUT MORE ABOUT THE COMPUTATIONAL PROPAGANDA RESEARCH PROJECT
Professors Peter Brown and Rafal Bogacz in the Nuffield Department of Clinical Neurosciences describe their research team’s discovery that a certain ‘hold your horses’ function in decision-making occurs in an extremely brief window of time, and involves bursts of a specific type of activity in a brain centre known as the subthalamic nucleus.
Are you a decider or a ditherer? When making decisions, we not only have to decide what to choose, but also how much time to spend making the decision. How long should we spend collecting relevant information to inform our choice?
Imagine, for example, that you are choosing which meal to pick up during a lunch break. Dwelling over this decision might mean that you miss out on valuable time that could be spent chatting with friends, whereas quickly choosing a menu option without proper thought might mean that you overlook a better alternative.
It was already thought that the subthalamic nucleus might play an important role in balancing the opposing demands of speed and accuracy during decision-making. Scientists suspected that it helped us delay decisions for the optimum amount of time, to enable the best choice to be made in any given situation. But our own research reveals that this part of the brain gets involved in adjusting these ‘decision thresholds’ at a very particular and brief moment during the process of deliberation.
The aim of our new study was to probe the mechanisms by which the subthalamic nucleus influences decision-making. We were able to do this using deep brain stimulation in Parkinson’s patients (an intervention which has been shown to be very successful in alleviating some of the movement symptoms related to this condition).
The research team asked ten patients to decide whether a cloud of moving dots appeared to move to the left or to the right on a computer screen. The percentage of dots moving coherently to one direction was either high or low, and participants were instructed to respond as fast or as accurately as possible. If it was difficult to determine the answer (i.e. the percentage of dots moving coherently in one direction was low), the response time was longer.
Participants responded more quickly when deep brain stimulation was applied during the difficult tasks. But this effect was confined to an incredibly brief moment during the time that people were trying to decide how to respond. Remarkably, if stimulation was applied later than 500 milliseconds after the task started, it had no influence at all on response time, even though most responses during difficult tasks were made later than 500 milliseconds into the task.
This result implies that deep brain stimulation interfered with a very particular time-limited process of setting the decision threshold to the required level according to task difficulty. This supports existing hypotheses that the decision threshold is set according to the difficulty of the task in a single abrupt change, and depending on information gathered in an initial period. This raises the possibility that it is this specific time-related mechanism that is dependent on the subthalamic nucleus.
Our observations add to the converging evidence that decision thresholds are adjusted through dynamic modulations of cortico-basal ganglia networks.
Words by Jacqueline Pumphrey, Peter Brown and Rafal Bogacz, NDCN.
Dr Paula Boddington is a research associate at Oxford’s Department of Computer Science, specialising in developing codes of ethics for artificial intelligence.
What drives you in your field?
I find the philosophical and ethical questions posed by developments in artificial intelligence fascinating.
There are visions of the development of AI that press us to ask questions about the limits and basis of our values – if AI radically changes the nature of work, for instance, perhaps even abolishing it for many, we have to reappraise what we do and don’t value about work, which raises questions about why we value any activity. Such questions about extending human intelligence and agency with AI are in fact, honing in on the most fundamental questions of philosophy, about the nature of human beings, about our place in the world and our ultimate sources of value. For me, it’s like finding a philosophical Shangri La to be working in this field.
What are the biggest challenges facing the field?
My work is focused on the implications of the technology, so the challenges include making sure that the power of AI does not simply amplify problems – such as our existing biases. There’s also a big issue in how we apply AI to problems. AI can be very powerful indeed in narrow areas. Whenever such narrow focus happens, there’s a danger that context will be missed, and that we’ll have found a solution, and so make all our problems fit the solution. It’s like Abraham Maslow said, when you have a hammer, everything begins to look like a nail.
There are certain tasks at which the AI we have now and in the near future can excel, so we must make sure that as we develop particular applications, we don’t find that our picture of the world starts to mould itself to what we can achieve with AI, especially given the hype that periodically surrounds it. That’s one of the reasons why we need as many people as possible involved in developing and applying AI, and thinking creatively about how it can best be used, and what else we need to achieve real benefits.
Why do you think it is important to encourage more women into the field?
Yes it’s important that women and men work in AI, but more than this, it’s important that there are people with diverse experiences and varied opinions and viewpoints in AI for a number of reasons.
We need to develop technology that actually caters to people’s needs, and where in practical applications, human beings will really benefit. Tailoring such tech is complex and it needs really good design sensitive to the context of a myriad of different circumstances.
What research are you most proud of?
I try not to really ‘do’ being proud of things, I’ve always been taught that ‘pride comes before a fall’. But I’m most pleased to be involved in work that might have a practical impact to improve lives for people. For instance, I’m also working right now on a project based in Cardiff University collaborating with a group of medical sociologists and others, on the care of people living with dementia – see storiesofdementia.com. This might seem a million miles away from AI and the impact of the development of new technology, but in fact the philosophical and ethical issues overlap considerably – how do we translate abstract ideas such as respect for persons, and humane, dignified care, into making a concrete difference to the lives of those such as people living with dementia, who have various challenges such as difficulties in communicating?
This work is aimed at producing practical recommendations to improve lives. We’ve just started a project looking at continence care. A world away from the glamour of AI, but essential work. And I see a great opportunity for technology to think about some important and common problems, for example, perhaps with working towards better detection of pain, which is greatly under-treated for those with dementia, or assisting with access to fluids and access to the toilet, which is often a problem in hospital wards. In the end, it’s this kind of careful, detailed ethnographic work that my colleagues in Cardiff are carrying out, which examines what’s really going on and what’s needed, that needs to be married up with developments in tech, in order to produce technology that will really benefit people.
Are there any AI research developments that excite you or that you are particularly interested in?
I’m particularly interested in the possibilities for AI in medicine, such as helping with disease diagnosis and the interpretation of medical images, and also its deployment in applications such as in the use of mobile technologies for health management. With these developments people are increasingly able to monitor and learn about their own health conditions. These are particularly exciting for use in remote areas or where medical staff are in short supply, but also simply for increasing the knowledge and control that individuals have over their own conditions and hence over their own wellbeing.
There are, quite understandably, fears that AI will take away jobs, but in the context of medicine, I think that’s unlikely. Think about how overstretched medical staff are at the moment. Helping them to make faster, more accurate diagnoses, tailored to individuals, will not only help patients, it should, hopefully, help to relieve time pressures and other stressors from doctors, if applied thoughtfully.
The evidence so far seems to indicate that AI works best as an addition to the skills of medical practitioners, not as a replacement for them. With all these developments, however, we need to keep looking very carefully at how we can get the best out of such technologies. For example, the early diagnosis of disease can be a big advantage in some conditions – but not such an advantage in others. In any context, and medicine is a good example of this, information is just information. It’s not knowledge, and it’s certainly not wisdom. That’s where the human skills of medical practitioners will always have a vital role.
What drew you towards a career in science?
Our whole family was always really excited about science. As children, my siblings and I were always glued to the television whenever Tomorrow’s World was on.
I came to dislike school a lot and used to bunk off and go to the library and read philosophy instead. I was really interesting in how the Arts’, social sciences and general STEM worked together.
I’ve always been focused on applying abstract ideas to concrete reality, and having an understanding of, say, the science behind developments in genomics. From my work in ethical questions in medical technology it was a short step to working in issues in artificial intelligence.
Who inspires you?
Of the many possible answers, I’d have to say members of my family. My father always told me that I could do anything I wanted in life. His own mother had started out life as the illegitimate daughter of a Victorian barmaid, brought up in Tiger Bay in Cardiff, and she became the headmistress of a girl’s Grammar School. So Dad had a great belief in women’s abilities. On my Mum’s side, her grandmother was the first woman in Cardiff to have her own alcohol licence and ran her own pub, also in Tiger Bay. She had six children, and during the Depression when work was hard to find, she started doing pub lunches to provide income for them - the family always claim that she invented the pub lunch. Whether that’s strictly true or not, ‘get an education, get an education’ was like a mantra breathed in the air, the idea that education was a key to success and that family was crucial too, and that yes, you can get around obstacles and make a go of things.
Dr Boddington is the author of the book Towards a Code of Ethics for Artificial Intelligence.