Leveraging the power of place in citizen science for effective conservation decision making – new paper

During the Citizen Science conference in 2015, a group of us, under the enthusiastic encouragement of John Gallo started talking about a paper that will discuss the power of place in citizen science. John provides a very detailed account about the way that a discussion and inspiration during the conference led to the development of the paper. Greg Newman took the lead on the process of writing, and the core analysis was based on classifying and analysing 134 citizen science projects.

My contribution to the paper is mostly in exploration of the concept of place including the interpretation within Human Geography of places as spaces of flows (so the paper cites Doreen Massey). I was also involved in various discussion about the development of the dimensions of place that were included in the analysis, while most of the work was done by Greg Newman, Bridie McGreavy  & Marc Chandler.

The paper is now out and free to read and reuse.

Place-based citizen science framework (a) before and (b) after leveraging the power of place. Note that after leveraging the power of place, the citizen science circle is enlarged to reflect a potential increase in participation, data collection, and quality of conservation decision making and that the overall influence of decision making also grew. Note also that the relative size of Zone One increased while the inherent capacity of the power of place remained the same size.
Place-based citizen science framework (a) before and (b) after leveraging the power of place. Note that after leveraging the power of place, the citizen science circle is enlarged to reflect a potential increase in participation, data collection, and quality of conservation decision making and that the overall influence of decision making also grew. Note also that the relative size of Zone One increased while the inherent capacity of the power of place remained the same size.

 

 

 

 

 

 

 

While it is, for me, expected that place will have an important role in citizen science, it is excellent to see that the analysis supported this observation through consistent classification of citizen science projects across three collections. The model above suggest how it can be used.

The paper development process, however, demonstrate the power of cyberspace, as the team met regularly online and shared documents, details and drafts along the way, with important regular online meeting that help it to come together. The paper started with all of us at the same place and at the same time, but this interaction was enough to sustain our team work all the way to publication.

The paper is open access and the abstract for it is:

Many citizen science projects are place-based – built on in-person participation and motivated by local conservation. When done thoughtfully, this approach to citizen science can transform humans and their environment. Despite such possibilities, many projects struggle to meet decision-maker needs, generate useful data to inform decisions, and improve social-ecological resilience. Here, we define leveraging the ‘power of place’ in citizen science, and posit that doing this improves conservation decision making, increases participation, and improves community resilience. First, we explore ‘place’ and identify five place dimensions: social-ecological, narrative and name-based, knowledge-based, emotional and affective, and performative. We then thematically analyze 134 case studies drawn from CitSci.org (n = 39), The Stewardship Network New England (TSN-NE; n = 39), and Earthwatch (n = 56) regarding: (1) use of place dimensions in materials (as one indication of leveraging the power of place), (2) intent for use of data in decision-making, and (3) evidence of such use. We find that 89% of projects intend for data to be used, 46% demonstrate no evidence of use, and 54% provide some evidence of use. Moreover, projects used in decision making leverage more (t = − 4.8, df = 117; p < 0.001) place dimensions (View the MathML source= 3.0; s = 1.4) than those not used in decision making (View the MathML source= 1.8; s = 1.2). Further, a Principal Components Analysis identifies three related components (aesthetic, narrative and name-based, and social-ecological). Given these findings, we present a framework for leveraging place in citizen science projects and platforms, and recommend approaches to better impart intended outcomes. We discuss place in citizen science related to relevance, participation, resilience, and scalability and conclude that effective decision making as a means towards more resilient and sustainable communities can be strengthened by leveraging the power of place in citizen science.

Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour

One of the facts about academic funding and outputs (that is, academic publications), is that there isn’t a simple relationship between the amount of funding and the number, size, or quality of outputs. One of the things that I have noticed over the years is that a fairly limited amount (about £4000-£10,000) are disproportionately effective. I guess that the reason for it is that on the one hand, it allow a specific period of dedicated time, but the short period focuses the mind on a specific task.

A case in point is the funding through the UCL Grand Challenges Small Grants programme. In 2014, together with Dr Elizabeth Boakes and Gianfranco Gliozzo, I secured funding for a short project on ‘Using citizen science data to assess the impact of biodiversity on human wellbeing‘. We have enlisted other people to work with us, and this has led the analysis of citizen science contributions across London. On the basis of this work, and in collaboration with researchers in ExCiteS (Gianfranco Gliozzo, Valentine Seymour), GiGL (Chloe Smith), Biological Records Centre (David Roy), and the Open University (Martin C. Harvey), we have developed a paper that is now published in Scientific Reports. The paper experienced a rejection and subsequent improvements along the way, which have made its analysis more robust and clear. Lizzie’s perseverance with the peer reviews challenges was critical in getting the paper published.

At the core of the paper is examination of the information from citizen science projects, and using this information to understand the behaviour of the volunteers, and what we can learn from this about biodiversity citizen science projects in general.

The paper full citation is: Boakes, E., Gliozzo, G., Seymour, V., Harvey, M.C., Roy, D.B., Smith, C., and Haklay, M., 2016, Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour, Scientific Reports

The abstract of the paper reads:

Citizen science has become a well-established method of biological recording but the opportunistic nature of biodiversity data gathered in this way means that they will likely contain taxonomic, spatial and temporal biases. Although many of these biases can be accounted for within statistical models, they are usually seen in a negative light since they add uncertainty to biodiversity estimates. However, they also give valuable information regarding volunteers’ recording behaviour, thus providing a way to enhance the fit between volunteers’ interests and the needs of scientific projects. Using Greater London as a case-study we examined the composition of three citizen science datasets – Greenspace Information for Greater London (GiGL), iSpot and iRecord – with respect to recorder contribution and spatial and taxonomic biases. We found each dataset to have its own taxonomic and spatial signature suggesting that volunteers’ personal motivations for recording may attract them towards particular schemes although there were also patterns common to all three recording systems. We found most volunteers contribute only a few records and are active for one day only. Our analyses indicate that species’ abundance and ease of identification of birds and flowering plants are positively associated with number of records, as was plant height. We found clear hotspots of recording activity, blue space (waterbodies) being associated with birding hotspots. We note that biases are accrued as part of the recording process (e.g. species’ detectability, media coverage) as well as from volunteer preferences.

Published: Why is Participation Inequality Important?

bookcoverI’ve mentioned the European Handbook for Crowdsourced Geographic Information in the last post, and explained how it came about. My contribution to the book is a chapter titled ‘Why is Participation Inequality Important?. The issue of participation inequality, also known as the 90:9:1 rule, or skewed contribution, has captured my interest for a while now. I have also explored it in my talk at the ECSA conference on ‘participatory [citizen] science‘ and elsewhere.

In this fairly short chapter what I am trying to communicate is that while we know that participation inequality is happening and part of crowdsourced information, we need to consider how it influences issues such as data quality, and think how it come about. I am trying to make suggest how we ended with skewed contributions – after all, at the beginnings of most projects, everyone are at the same level – zero contribution, and then participation inequality emerge.

I have used the iconic graph of contribution to OpenStreetMap that Harry Wood created, but the chapter is discussing other projects and activities where you can come across this phenomena.

Here is a direct link to the chapter, and I’ll be very happy to hear comments about it!

 

Algorithmic governance in environmental information (or how technophilia shape environmental democracy)

These are the slides from my talk at the Algorithmic Governance workshop (for which there are lengthy notes in the previous post). The workshop explored the many ethical, legal and conceptual issues with the transition to Big Data and algorithm based decision-making.

My contribution to the discussion is based on previous thoughts on environmental information and public use of it. Inherently, I see the relationships between environmental decision-making, information, and information systems as something that need to be examined through the prism of the long history that linked them. This way we can make sense of the current trends. This three area are deeply linked throughout the history of the modern environmental movement since the 1960s (hence the Apollo 8 earth image at the beginning),  and the Christmas message from the team with the reference to Genesis (see below) helped in making the message stronger .

To demonstrate the way this triplet evolved, I’m using texts from official documents – Stockholm 1972 declaration, Rio 1992 Agenda 21, etc. They are fairly consistent in their belief in the power of information systems in solving environmental challenges. The core aspects of environmental technophilia are summarised in slide 10.

This leads to environmental democracy principles (slide 11) and the assumptions behind them (slide 12). While information is open, it doesn’t mean that it’s useful or accessible to members of the public. This was true when raw air monitoring observations were released as open data in 1997 (before anyone knew the term), and although we have better tools (e.g. Google Earth) there are consistent challenges in making information meaningful – what do you do with Environment Agency DSM if you don’t know what it is or how to use a GIS? How do you interpret Global Forest Watch analysis about change in tree cover in your area if you are not used to interpreting remote sensing data (a big data analysis and algorithmic governance example)? I therefore return to the hierarchy of technical knowledge and ability to use information (in slide 20) that I covered in the ‘Neogeography and the delusion of democratisation‘ and look at how the opportunities and barriers changed over the years in slide 21.

The last slides show that despite of all the technical advancement, we can have situations such as the water contamination in Flint, Michigan which demonstrate that some of the problems from the 1960s that were supposed to be solved, well monitored, with clear regulations and processes came back because of negligence and lack of appropriate governance. This is not going to be solved with information systems, although citizen science have a role to play to deal with the governmental failure. This whole sorry mess and the re-emergence of air quality as a Western world environmental problem is a topic for another discussion…

Algorithmic Governance Workshop (NUI Galway)

Algorithmic Governance Workshop (source: Niall O Brolchain)

The workshop ‘Algorithmic Governance’ was organised as an intensive one day discussion and research needs development. As the organisers Dr John Danaher
and Dr Rónán Kennedy identified:

‘The past decade has seen an explosion in big data analytics and the use  of algorithm-based systems to assist, supplement, or replace human decision-making. This is true in private industry and in public governance. It includes, for example, the use of algorithms in healthcare policy and treatment, in identifying potential tax cheats, and in stopping terrorist plotters. Such systems are attractive in light of the increasing complexity and interconnectedness of society; the general ubiquity and efficiency of ‘smart’ technology, sometimes known as the ‘Internet of Things’; and the cutbacks to government services post-2008.
This trend towards algorithmic governance poses a number of unique challenges to effective and legitimate public-bureaucratic decision-making. Although many are already concerned about the threat to privacy, there is more at stake in the rise of algorithmic governance than this right alone. Algorithms are step-by-step computer coded instructions for taking some input (e.g. tax return/financial data), processing it, and converting it into an output (e.g. recommendation for audit). When algorithms are used to supplement or replace public decision-making, political values and policies have to be translated into computer code. The coders and designers are given a set of instructions (a project ‘spec’) to guide them in this process, but such project specs are often vague and underspecified. Programmers exercise considerable autonomy when translating these requirements into code. The difficulty is that most programmers are unaware of the values and biases that can feed into this process and fail to consider how those values and biases can manifest themselves in practice, invisibly undermining fundamental rights. This is compounded by the fact that ethics and law are not part of the training of most programmers. Indeed, many view the technology as a value-neutral tool. They consequently ignore the ethical ‘gap’ between policy and code. This workshop will bring together an interdisciplinary group of scholars and experts to address the ethical gap between policy and code.

The workshop was structured around 3 sessions of short presentations of about 12 minutes, with an immediate discussion, and then a workshop to develop research ideas emerging from the sessions. This very long post are my notes from the meeting. These are my takes, not necessarily those of the presenters. For another summery of the day, check John Danaher’s blog post.

Session 1: Perspective on Algorithmic Governance

Professor Willie Golden (NUI Galway)Algorithmic governance: Old or New Problem?’ focused on an information science perspective.  We need to consider the history – an RO Mason paper from 1971 already questioned the balance between the decision-making that should be done by humans, and that part that need to be done by the system. The issue is the level of assumptions that are being integrated into the information system. Today the amount of data that is being collected and the assumption on what it does in the world is a growing one, but we need to remain sceptical at the value of the actionable information. Algorithms needs managers too. Davenport in HBR 2013 pointed that the questions by decision makers before and after the processing are critical to effective use of data analysis systems. In addition, people are very concerned about data – we’re complicit in handing over a lot of data as consumers and the Internet of Things (IoT) will reveal much more. Debra Estrin 2014 at CACM provided a viewpoint – small data, where n = me where she highlighted the importance of health information that the monitoring of personal information can provide baseline on you. However, this information can be handed over to health insurance companies and the question is what control you have over it. Another aspect is Artificial Intelligence – Turing in 1950’s brought the famous ‘Turing test’ to test for AI. In the past 3-4 years, it became much more visible. The difference is that AI learn, which bring the question how you can monitor a thing that learn and change over time get better. AI doesn’t have self-awareness as Davenport 2015 noted in Just How Smart are Smart Machines and arguments that machine can be more accurate than humans in analysing images. We may need to be more proactive than we used to be.

Dr Kalpana Shankar (UCD), ‘Algorithmic Governance – and the
Death of Governance?’ focused on digital curation/data sustainability and implication for governance. We invest in data curation as a socio-technical practice, but need to explore what it does and how effective are current practices. What are the implications if we don’t do ‘data labour’ to maintain it, to avoid ‘data tumbleweed. We are selecting data sets and preserving them for the short and long term. There is an assumption that ‘data is there’ and that it doesn’t need special attention. Choices that people make to preserve data sets will influence the patterns of  what appear later and directions of research. Downstream, there are all sort of business arrangement to make data available and the preserving of data – the decisions shape disciplines and discourses around it – for example, preserving census data influenced many of the social sciences and direct them towards certain types of questions. Data archives influenced the social science disciplines – e.g. using large data set and dismissing ethnographic and quantitative data. The governance of data institutions need to get into and how that influence that information that is stored and share. What is the role of curating data when data become open is another question. Example for the complexity is provided in a study of a system for ‘match making’ of refugees to mentors which is used by an NGO, when the system is from 2006, and the update of job classification is from 2011, but the organisation that use the system cannot afford updating and there is impacts on those who are influenced by the system.

Professor John Morison (QUB), ‘Algorithmic Governmentality’. From law perspective, there is an issue of techno-optimism. He is interested in e-participation and participation in government. There are issue of open and big data, where we are given a vision of open and accountable government and growth in democratisation – e.g. social media revolution, or opening government through data. We see fantasy of abundance, and there are also new feedback loops – technological solutionism to problems in politics with technical fixes. Simplistic solutions to complex issues. For example, an expectation that in research into cybersecurity, there are expectations of creating code as a scholarly output. Big Data have different creators (from Google to national security bodies) and they don’t have the same goals. There is also issues of technological authoritarianism as a tool of control. Algorithmic governance require to engage in epistemology, ontology or governance. We need to consider the impact of democracy – the AI approach is arguing for the democratisation through N=all argument. Leaving aside the ability to ingest all the data, what is seemed to assume that subjects are not viewed any more as individuals but as aggregate that can be manipulated and act upon. Algorithmic governance, there is a false emancipation by promise of inclusiveness, but instead it is responding to predictions that are created from data analysis. The analysis is arguing to be scientific way to respond to social needs. Ideas of individual agency disappear. Here we can use Foucault analysis of power to understand agency.  Finally we also see government without politics – arguing that we make subjects and objects amenable to action. There is not selfness, but just a group prediction. This transcend and obviates many aspects of citizenship.

Niall O’Brolchain (Insight Centre), ‘The Open Government’. There is difference between government and governance. The eGov unit in Galway Insight Centre of Data Analytics act as an Open Data Institute node and part of the Open Government Partnership. OGP involve 66 countries, to promote transparency, empower citizens, fight corruption, harness new technologies to strengthen governance. Started in 2011 and involved now 1500 people, with ministerial level involvement. The OGP got set of principles, with eligibility criteria that involve civic society and government in equal terms – the aim is to provide information so it increase civic participation, requires the highest standards of professional integrity throughout administration, and there is a need to increase access to new technologies for openness and accountability. Generally consider that technology benefits outweigh the disadvantages for citizenship. Grand challenges – improving public services, increasing public integrity, public resources, safer communities, corporate accountability. Not surprisingly, corporate accountability is one of the weakest.

Discussion:

Using the Foucault framework, the question is about the potential for resistance that is created because of the power increase. There are cases to discuss about hacktivism and use of technologies. There is an issue of the ability of resisting power – e.g. passing details between companies based on prediction. The issue is not about who use the data and how they control it. Sometime need to use approaches that are being used by illegal actors to hide their tracks to resist it.
A challenge to the workshop is that the area is so wide, and we need to focus on specific aspects – e.g. use of systems in governments, and while technology is changing. Interoperability.  There are overlaps between environmental democracy and open data, with many similar actors – and with much more government buy-in from government and officials. There was also technological change that make it easier for government (e.g. Mexico releasing environmental data under OGP).
Sovereignty is also an issue – with loss of it to technology and corporations over the last years, and indeed the corporate accountability is noted in the OGP framework as one that need more attention.
There is also an issue about information that is not allowed to exists, absences and silences are important. There are issues of consent – the network effects prevent options of consent, and therefore society and academics can force businesses to behave socially in a specific way. Keeping of information and attributing it to individuals is the crux of the matter and where governance should come in. You have to communicate over the internet about who you are, but that doesn’t mean that we can’t dictate to corporations what they are allowed to do and how to use it. We can also consider of privacy by design.

Session 2: Algorithmic Governance and the State

Dr Brendan Flynn (NUI Galway), ‘When Big Data Meets Artificial Intelligence will Governance by Algorithm be More or Less Likely to Go to War?’. When looking at autonomous weapons we can learn about general algorithmic governance. Algorithmic decision support systems have a role to play in very narrow scope – to do what the stock market do – identifying very dangerous response quickly and stop them. In terms of politics – many things will continue. One thing that come from military systems is that there are always ‘human in the loop’ – that is sometime the problem. There will be HCI issues with making decisions quickly based on algorithms and things can go very wrong. There are false positive cases as the example of the USS Vincennes that uses DSS to make a decision on shooting down a passenger plane. The decision taking is limited by the decision shaping, which is handed more and more to algorithms. There are issues with the way military practices understand command responsibility in the Navy, which put very high standard from responsibility of failure. There is need to see how to interpret information from black boxes on false positives and false negatives. We can use this extreme example to learn about civic cases. Need to have high standards for officials. If we do visit some version of command responsibility to those who are using algorithms in governance, it is possible to put responsibility not on the user of the algorithm and not only on the creators of the code.

Dr Maria Murphy (Maynooth), ‘Algorithmic Surveillance: True
Negatives’. We all know that algorithmic interrogation of data for crime prevention is becoming commonplace and also in companies. We know that decisions can be about life and death. When considering surveillance, there are many issues. Consider the probability of assuming someone to be potential terrorist or extremist. In Human Rights we can use the concept of private life, and algorithmic processing can challenge that. Article 8 of the Human Right Convention is not absolute, and can be changed in specific cases – and the ECHR ask for justifications from governments, to show that they follow the guidelines. Surveillance regulations need to explicitly identify types of people and crimes that are open to observations. You can’t say that everyone is open to surveillance. When there are specific keywords that can be judged, but what about AI and machine learning, where the creator can’t know what will come out? There is also need to show proportionality to prevent social harm. False positives in algorithms – because terrorism are so rare, there is a lot of risk to have a bad impact on the prevention of terrorism or crime. The assumption of more data is better data, we left with a problem of generalised surveillance that is seen as highly problematic. Interestingly the ECHR do see a lot of potential in technologies and their potential use by technologies.

Professor Dag Weise Schartum (University of Oslo), ‘Transformation of Law into Algorithm’. His focus was on how algorithms are created, and thinking about this within government systems. They are the bedrock of our welfare systems – which is the way they appear in law. Algorithms are a form of decision-making: general decisions about what should be regarded, and then making decisions. The translation of decisions to computer code, but the raw material is legal decision-making process and transform them to algorithms. Programmers do have autonomy when translating requirements into code – the Norwegian experience show close work with experts to implement the code. You can think of an ideal transformation model of a system to algorithms, that exist within a domain – service or authority of a government, and done for the purpose of addressing decision-making. The process is qualification of legal sources, and interpretations that are done in natural language, which then turn into specification of rules, and then it turns into a formal language which are then used for programming and modelling it. There are iterations throughout the process, and the system is being tested, go through a process of confirming the specification and then it get into use. It’s too complex to test every aspect of it, but once the specifications are confirmed, it is used for decision-making.  In terms of research we need to understand the transformation process in different agency – overall organisation, model of system development, competences, and degree of law-making effects. The challenge is the need to reform of the system: adapting to changes in the political and social change over the time. Need to make the system flexible in the design to allow openness and not rigidness.

Heike Felzman (NUI Galway), ‘The Imputation of Mental Health
from Social Media Contributions’ philosophy and psychological background. Algorithms can access different sources – blogs, social media and this personal data are being used to analyse mood analysis, and that can lead to observations about mental health. In 2013, there are examples of identifying of affective disorders, and the research doesn’t consider the ethical implication. Data that is being used in content, individual metadata like time of online activities, length of contributions, typing speed. Also checking network characteristics and biosensing such as voice, facial expressions. Some ethical challenges include: contextual integrity (Nissenbaum 2004/2009) privacy expectations are context specific and not as constant rules. Secondly, lack of vulnerability protection – analysis of mental health breach the rights of people to protect their health. Third, potential negative consequences, with impacts on employment, insurance, etc. Finally, the irrelevance of consent – some studies included consent in the development, but what about applying it in the world. We see no informed consent, no opt-out, no content related vulnerability protections, no duty of care and risk mitigation, there is no feedback and the number of participants number is unlimited. All these are in contrast to practices in Human Subjects Research guidelines.

Discussion:

In terms of surveillance, we should think about self-surveillance in which the citizens are providing the details of surveillance yourself. Surveillance is not only negative – but modern approach are not only for negative reasons. There is hoarding mentality of the military-industrial complex.
The area of command responsibility received attention, with discussion of liability and different ways in which courts are treating military versus civilian responsibility.

Panel 3: Algorithmic Governance in Practice

Professor Burkhard Schafer (Edinburgh), ‘Exhibit A – Algorithms as
Evidence in Legal Fact Finding’. The discussion about legal aspects can easily go to 1066 – you can go through a whole history. There are many links to medieval law to today. As a regulatory tool, there is the issue with the rule of proof. Legal scholars don’t focus enough on the importance of evidence and how to understand it. Regulations of technology is not about the law but about the implementation on the ground, for example in the case of data protection legislations. In a recent NESTA meeting, there was a discussion about the implications of Big Data – using personal data is not the only issue. For example, citizen science project that show low exposure to emission, and therefore deciding that it’s relevant to use the location in which the citizens monitored their area as the perfect location for a polluting activity – so harming the person who collected data. This is not a case of data protection strictly. How can citizen can object to ‘computer say no’ syndrome? What are the minimum criteria to challenge such a decision? What are the procedural rules of fairness. Have a meaningful cross examination during such cases is difficult in such cases. Courts sometimes accept and happy to use computer models, and other times reluctant to take them. There are issues about the burden of proof from systems (e.g. to show that ATM was working correctly when a fraud was done). DNA tests are relying on computer modelling, but systems that are proprietary and closed. Many algorithms are hidden for business confidentiality and there are explorations of these issues. One approach is to rely on open source tools. Replication is another way of ensuring the results. Escrow ownership of model by third party is another option. Next, there is a possibility to questioning software, in natural language.

Dr Aisling de Paor (DCU), ‘Algorithmic Governance and Genetic Information’ – there is an issue in law, and massive applications in genetic information. There is rapid technological advancement in many settings, genetic testing, pharma and many other aspects – indications of behavioural traits, disability, and more. There are competing rights and interests. There are rapid advances in this area – use in health care, and the technology become cheaper (already below $1000). Genetic information. In commercial settings use in insurance, valuable for economic and efficiency in medical settings. There is also focus on personalised medicine. A lot of the concerns are about misuse of algorithms. For example, the predictive assumption about impact on behaviour and health. The current state of predictability is limited, especially the environmental impacts on expressions of genes. There is conflicting rights – efficiency and economic benefits but challenge against human rights – e.g. right to privacy . Also right for non-discrimination – making decisions on the basis of probability may be deemed as discriminatory. There are wider societal and public policy concerns – possible creation of genetic underclass and the potential of exacerbate societal stigma about disability, disease and difference. Need to identify gaps between low, policy and code, decide use, commercial interests and the potential abuses.

Anthony Behan (IBM but at a personal capacity), ‘Ad Tech, Big Data and Prediction Markets: The Value of Probability’. Thinking about advertising, it is very useful use case to consider what happen in such governance processes. What happen in 200 milliseconds for advertising, which is the standards on the internet. The process of real-time-bid is becoming standardised. Start from a click – the publisher invokes an API and give information about the interactions from the user based on their cookie and there are various IDs. Supply Side Platform open an auction. on the demand side, there are advertisers that want to push content to people – age group, demographic, day, time and objectives such as click through rates. The Demand Side platform looks at the SSPs. Each SSP is connected to hundreds of Demand Side Platforms (DSPs). Complex relationships exist between these systems. There are probability score or engage in a way that they want to engage, and they offer how much it is worth for them – all in micropayment. The data management platform (DMP) is important to improve the bidding. e.g., if they can get information about users/platform/context at specific times places etc is important to guess how people tend to behave. The economy of the internet on advert is based on this structure. We get abstractions of intent – the more privacy was invaded and understand personality and intent, the less they were interested in a specific person but more in the probability and the aggregate. Viewing people as current identity and current intent, and it’s all about mathematics – there are huge amount of transactions, and the inventory become more valuable. The interactions become more diverse with the Internet of Things. The Internet become a ‘data farm’ – we started with a concept that people are valuable, to view that data is valuable and how we can extract it from people. Advertising goes into the whole commerce element.

I’ll blog about my talk ‘Algorithmic Governance in Environmental Information (or How Technophilia Shapes Environmental Democracy) later.

 Discussion:

There are issues with genetics and eugenics. Eugenics fell out of favour because of science issues, and the new genetics is claiming much more predictive power. In neuroscience there are issues about brain scans, which are not handled which are based on insufficient scientific evidence. There is an issue with discrimination – shouldn’t assume that it’s only negative. Need to think about unjustified discrimination. There are different semantic to the word. There are issues with institutional information infrastructure.

New publication: Citizen Science and the Nexus (water, energy, food, population)

Under the leadership of Roger Fradera of the Centre for Environmental Policy at Imperial College London, I was involved as a co-author on a ‘thinkpiece’ about citizen science and the nexus. If you haven’t come across the term, ‘nexus’ is the linkage of food, energy, water and the environment as a major challenge for the future.

The paper is now published:

Fradera, R., Slawson, D., Gosling, L., Geoghegan, H., Lakeman-Fraser, P.,  Makuch, K. Makuch, Z., Madani, K., Martin, K., Slade, R., Moffat, A. and Haklay, M. Exploring the nexus through citizen science, Nexus Network think piece Series, Paper 010, November 2015

The paper explores the background of citizen science, and then suggests few recommendations in the context of the nexus, including:

  • Inclusivity: a co-created citizen science approach is likely to be more appropriate both to address the more complex nexus issues and to engage all sectors of society.
  • Engagement: Citizen science practitioners and nexus scientists should explore developing citizen science programmes with multi-scale engagement of citizens, for example programmes focusing on a nexus issue that combine local, citizen-led or co-created projects.
  • Barriers: Research is needed to understand the motivations, attitudes and willingness to change behaviours across all nexus stakeholders, and to better understand and find solutions to barriers.

The work was funded under the ESRC Nexus Network initiative

Being philosophical about crowdsourced geographic information

This is a post by Renee Sieber and myself, providing a bit of a background on why we wrote the paper “The epistemology(s) of volunteered geographic information: a critique” – this is in addition to what I’ve written about it in this blog post

Geo: Geography and Environment

By Renée Sieber (McGill University, Canada) and Muki Haklay (University College London, UK)

Our recent paper, The epistemology(s) of volunteered geographic information: a critique, started from a discussion we had about changes within the geographic information science (GIScience) research communities over the past two decades. We’ve both been working in the area of participatory geographic information systems (GIS) and critical studies of geographic information science (GIScience) since the late 1990s, where we engaged with people from all walks of life with the information that is available in GIS. Many times we’d work together with people to create new geographic information and maps. Our goal was to help reflect their point of view of the world and their knowledge about local conditions, not always aim for universal rules and principles. For example, the image below is from a discussion with the community in Hackney Wick, London, where individuals collaborated to…

View original post 819 more words