Defining the Agenda: Key Lessons for Funders and Commissioners of Ethical Research in Fragile and Conflict Affected Contexts

By Leslie Groves-Williams. Funded by UK Research and Innovation (UKRI) and developed in collaboration with UNICEF, Office of Research – Innocenti.  A pdf copy is available online here

Publicised here because the issues and lessons identified also seem relevant to many evaluation activities

Text of the Introduction: The ethical issues that affect all research are amplified significantly in fragile and conflict-affected contexts. The power imbalances between local and international researchers are increased and the risk of harm is augmented within a context where safeguards are often reduced and the probabilities of unethical research that would be prohibited elsewhere are magnified. Funders and commissioners need to be confident that careful ethical scrutiny of the research process is conducted to mitigate risk, avoid potential harm and maximize the benefit of the commissioned research for affected populations, including through improving the quality and accuracy of data collected. The UKRI and UNICEF Ethical Research in Fragile and Conflict-Affected Contexts: Guidelines for Reviewers can support you to ensure that appropriate ethical scrutiny is taking place at review phase. But, what about mitigating for risks at the funding and commissioning phases? These phases are often not subject to ethical review yet carry strong ethical risks and opportunities. As a commissioner or a funder designing a call for research in fragile and conflict-affected contexts, how confident are you that you are commissioning the research in the most ethical way?

This document brings together some key lessons learned that provide guidance for funders and commissioners of research in fragile and conflict-affected contexts to ensure that ethical standards are applied, not just at the review stage, but also in formulating the research agenda. These lessons fall into four clusters:

1. Ethical Agenda Setting
2. Ethical Partnerships
3. Ethical Review
4. Ethical Resourcing.
In addition to highlighting the lessons, this paper provides mitigation strategies for funders and commissioners to explore as they seek to avoid the ethical risks highlighted

Algorithmic Impact Assessment – Three+ useful publications by Data & Society

In the movies, when a machine decides to be the boss — or humans let it — things go wrong. Yet despite myriad dystopian warnings, control by machines is fast becoming our reality. Photo: The Conversation / Shutterstock
As William Gibson famously said  circa 1992 “The future is already here — it’s just not very evenly distributed”  In 2021 the future is certainly here in the form of algorithms (rather than people) that manage low paid workers ( distribution centres, delivery services, etc), welfare service recipients and those caught up in the justice system. Plus anyone else having to deal with chatbots when trying to get through to other kinds of service providers. But is a counter-revolution brewing? Read on…

Selected quotes

“Algorithmic accountability is the process of assigning responsibility for harm when algorithmic decision-making results in discriminatory and inequitable outcomes”

“Among many applications, algorithms are used to:

• Sort résumés for job applications;
• Allocate social services;
• Decide who sees advertisements for open positions, housing, and products;
• Decide who should be promoted or fired;
• Estimate a person’s risk of committing crimes or the length of a prison term;
• Assess and allocate insurance and benefits;
• Obtain and determine credit; and
• Rank and curate news and information in search engines.”

“Algorithmic systems present a special challenge to assessors, because the harms of these systems are unevenly distributed, emerge only after they are integrated into society, or are often only visible in the aggregate”

“What our research indicates is that the risk of self-regulation lies not so much in a corrupted reporting and assessment process, but in the capacity of industry to define the methods and metrics used to measure the impact of proposed systems”

Algorithmic Accountability: A Primer.  Data & S0ciety. Caplan, R., Donovan, J., Hanson, L., & Matthews, J. (2018). 26 pages
What Is an Algorithm?
How Are Algorithms Used to Make Decisions?
Example: Racial Bias in Algorithms of Incarceration
Complications with Algorithmic Systems
• Fairness and Bias
• Opacity and Transparency
• Repurposing Data and
Repurposing Algorithms
• Lack of Standards for Auditing
• Power and Control
• Trust and Expertise
What is Algorithmic Accountability?
• Auditing by Journalists
• Enforcement and Regulation
Assembling accountability: Algorithmic Impact Assessment for the Public Interest. Data & Society. Moss, E., Watkins, E. A., Singh, R., Elish, M. C., & Jacob Metcalf. (2021).


In summary: The Algorithmic Impact Assessment is a new concept for regulating algorithmic systems and protecting the public interest. Assembling Accountability: Algorithmic Impact Assessment for the Public Interest is a report that maps the challenges of constructing algorithmic impact assessments (AIAs) and provides a framework for evaluating the effectiveness of current and proposed AIA regimes. This framework is a practical tool for regulators, advocates, public-interest technologists, technology companies, and critical scholars who are identifying, assessing, and acting upon algorithmic harms.

First, report authors Emanuel Moss, Elizabeth Anne Watkins, Ranjit Singh, Madeleine Clare Elish, and Jacob Metcalf analyze the use of impact assessment in other domains, including finance, the environment, human rights, and privacy. Building on this comparative analysis, they then identify common components of existing impact assessment practices in order to provide a framework for evaluating current and proposed AIA regimes. The authors find that a singular, generalized model for AIAs would not be effective due to the variances of governing bodies, specific systems being evaluated, and the range of impacted communities.

After illustrating the novel decision points required for the development of effective AIAs, the report specifies ten necessary components that constitute robust impact assessment regimes.


What is an Impact?
What is Accountability?
What is Impact Assessment?
Sources of Legitimacy
Actors and Forum
Catalyzing Event
Time Frame
Public Access
Public Consultation
Harms and Redress
Existing and Proposed AIA Regulations
Algorithmic Audits
External (Third and Second Party) Audits
Internal (First-Party) Technical Audits and
Governance Mechanisms
Sociotechnical Expertise
See also

The revised UNEG Ethical Guidelines for Evaluations (2020)

The UNEG Ethical Guidelines for Evaluation were first published in 2008. This document is a revision of the original document and was approved at the UNEG AGM 2020. These revised guidelines are consistent with the standards of conduct in the Charter of the United Nations, the Staff Regulations and Rules of the United Nations, the Standards of Conduct for the International Civil Service, and in the Regulations Governing the Status, Basic Rights and Duties of Officials other than Secretariat. They  are also consistent with the United Nations’ core values of Integrity, Professionalism and Respect for Diversity, the humanitarian principles of Humanity, Neutrality, Impartiality and Independence and the values enshrined in the Universal Declaration of Human Rights.

This document aims to support UN entity leaders and governing bodies as well as those organizing and conducting evaluations for the UN to ensure that an ethical lens informs day to day evaluation practice.

This document provides:

  • Four ethical principles for evaluation;
  • Tailored guidelines for entity leaders and governing bodies, evaluation organizers, and evaluation practitioners;
  • A detachable Pledge of Commitment to Ethical Conduct in Evaluation that all those involved in evaluations will be required to sign.

These guidelines are designed to be useful and applicable to all UN agencies, regardless of differences in mission (operational vs. normative agencies), in structures (centralized vs. decentralized), in the contexts for the work (development, peacekeeping, humanitarian) and in the nature of evaluations that are undertaken (oversight/accountability focused vs. learning).

On the usefulness of deliberate (but bounded) randomness in decision making


An introduction

In many spheres of human activity, relevant information may be hard to find, and it may be of variable quality. Human capacities to objectively assess that information may also be limited and variable. Extreme cases may be easy to assess e.g projects or research that is definitely worth/not worth funding or papers that are definitely worth/not worth publishing. But in between these extremes there may be substantial uncertainty and thus room for tacit assumptions and unrecognised biases to influence judgements.  In some fields the size of this zone of uncertainty may be quite big (see Adam, 2019 below), so the consequences at stake can be substantial. This is the territory where a number of recent papers have argued that an explicitly random decision making process may be the best approach to take.

After you have scanned the references below, continue on to some musings about implications for how we think about complexity

The literature (a sample)

    • Nesta (2020) Why randomise funding? How randomisation can improve the diversity of ideas
    • Osterloh, M., & Frey, B. S. (2020, March 9). To ensure the quality of peer reviewed research introduce randomness. Impact of Social Sciences.  
      • Why random selection of contributions to which the referees do not agree? This procedure reduces the “conservative bias”, i.e. the bias against unconventional ideas. Where there is uncertainty over the quality of a contribution, referees have little evidence to draw on in order to make accurate evaluations. However, unconventional ideas may well yield high returns in the future. Under these circumstances a randomised choice among the unorthodox contributions is advantageous.
      • …two [possible] types of error: type I errors (“reject errors”) implying that a correct hypothesis is rejected, and type 2 errors implying that a false hypothesis is accepted (“accept errors”). The former matters more than the latter. “Reject errors” stop promising new ideas, sometimes for a long time, while “accept errors” lead to a waste of money, but may be detected soon once published. This is the reason why it is more difficult to identify “reject errors” than “accept errors”. Through randomisation the risks of “reject errors” are diversified.
  • Osterloh, M., & Frey, B. S. (2020). How to avoid borrowed plumes in academia. Research Policy, 49(1), 103831. Abstract: Publications in top journals today have a powerful influence on ac
  • Liu, M., Choy, V., Clarke, P., Barnett, A., Blakely, T., & Pomeroy, L. (2020). The acceptability of using a lottery to allocate research funding: A survey of applicants. Research Integrity and Peer Review, 5(1), 3.
    • Background: The Health Research Council of New Zealand is the first major government funding agency to use a lottery to allocate research funding for their Explorer Grant scheme. …  the Health Research Council of New Zealand wanted to hear from applicants about the acceptability of the randomisation process and anonymity of applicants.   The survey asked about the acceptability of using a lottery and if the lottery meant researchers took a different approach to their application. Results:… There was agreement that randomisation is an acceptable method for allocating Explorer Grant funds with 63% (n = 79) in favour and 25% (n = 32) against. There was less support for allocating funds randomly for other grant types with only 40% (n = 50) in favour and 37% (n = 46) against. Support for a lottery was higher amongst those that had won funding. Multiple respondents stated that they supported a lottery when ineligible applications had been excluded and outstanding applications funded, so that the remaining applications were truly equal. Most applicants reported that the lottery did not change the time they spent preparing their application. Conclusions: The Health Research Council’s experience through the Explorer Grant scheme supports further uptake of a modified lottery.
  • Roumbanis, L. (2019). Peer Review or Lottery? A Critical Analysis of Two Different Forms of Decision-making Mechanisms for Allocation of Research Grants. Science, Technology, & Human Values44(6), 994–1019.  
  • Adam, D. (2019). Science funders gamble on grant lotteries.A growing number of research agencies are assigning money randomly. Nature, 575(7784), 574–575.
    • ….says that existing selection processes are inefficient. Scientists have to prepare lengthy applications, many of which are never funded, and assessment panels spend most of their time sorting out the specific order in which to place mid-ranking ideas. Low­ and high­ quality applications are easy to rank, she says. “But most applications are in the midfield, which is very big
    • The fund tells applicants how far they got in the process, and feedback from them has been positive, he says. “Those that got into the ballot and miss out don’t feel as disappointed. They know they were good enough to get funded and take it as the luck of the draw.”
    • Fang, F. C., & Casadevall, A. (2016). Research Funding: The Case for a Modified Lottery. MBio, 7(2).
      • ABSTRACT The time-honored mechanism of allocating funds based on ranking of proposals by scienti?c peer review is no longer effective, because review panels cannot accurately stratify proposals to identify the most meritorious ones. Bias has a major in?uence on funding decisions, and the impact of reviewer bias is magni?ed by low funding paylines. Despite more than a decade of funding crisis, there has been no fundamental reform in the mechanism for funding research. This essay explores the idea of awarding research funds on the basis of a modi?ed lottery in which peer review is used to identify the most meritorious proposals, from which funded applications are selected by lottery. We suggest that a modi?ed lottery for research fund allocation would have many advantages over the current system, including reducing bias and improving grantee diversity with regard to seniority, race, and gender.
  • Avin, S (2015) Breaking the grant cycle: on the rational allocation of public resources to scientific research projects
    • Abstract: The thesis presents a reformative criticism of science funding by peer review. The criticism is based on epistemological scepticism, regarding the ability of scientific peers, or any other agent, to have access to sufficient information regarding the potential of proposed projects at the time of funding. The scepticism is based on the complexity of factors contributing to the merit of scientific projects, and the rate at which the parameters of this complex system change their values. By constructing models of different science funding mechanisms, a construction supported by historical evidence, computational simulations show that in a significant subset of cases it would be better to select research projects by a lottery mechanism than by selection based on peer review. This last result is used to create a template for an alternative funding mechanism that combines the merits of peer review with the benefits of random allocation, while noting that this alternative is not so far removed from current practice as may first appear.
  • Schulson, M. (2014). If you can’t choose wisely, choose randomly. Aeon. A quick review of known instances of the use of randomness across different cultures, nationalities and periods of history
  • Casadevall, F. C. F. A. (2014, April 14). Taking the Powerball Approach to Funding Medical Research. Wall Street Journal.
  • Stone, P. (2011). The Luck of the Draw: The Role of Lotteries in Decision Making. In The Luck of the Draw: The Role of Lotteries in Decision Making.
    • From the earliest times, people have used lotteries to make decisions–by drawing straws, tossing coins, picking names out of hats, and so on. We use lotteries to place citizens on juries, draft men into armies, assign students to schools, and even on very rare occasions, select lifeboat survivors to be eaten. Lotteries make a great deal of sense in all of these cases, and yet there is something absurd about them. Largely, this is because lottery-based decisions are not based upon reasons. In fact, lotteries actively prevent reason from playing a role in decision making at all. Over the years, people have devoted considerable effort to solving this paradox and thinking about the legitimacy of lotteries as a whole. However, these scholars have mainly focused on lotteries on a case-by-case basis, not as a part of a comprehensive political theory of lotteries. In The Luck of the Draw, Peter Stone surveys the variety of arguments proffered for and against lotteries and argues that they only have one true effect relevant to decision making: the “sanitizing effect” of preventing decisions from being made on the basis of reasons. While this rationale might sound strange to us, Stone contends that in many instances, it is vital that decisions be made without the use of reasons. By developing innovative principles for the use of lottery-based decision making, Stone lays a foundation for understanding when it is–and when it is not–appropriate to draw lots when making political decisions both large and small

Randomness in other species

    • Drew, L. (2020). Random Search Wired Into Animals May Help Them Hunt. Quanta Magazine. Retrieved 2 February 2021, from
        • Of special interest here is the description of  Levy walks, a variety of randomised movement where the frequency  distribution of distances moved has one long tail. Levy walks have been the subject of exploration across multiple disciples, as seen in…
    • Reynolds, A. M. (2018). Current status and future directions of Lévy walk research. Biology Open, 7(1).
        • Levy walks are specialised forms of random walks composed of clusters of multiple short steps with longer steps between them…. They are particularly advantageous when searching in uncertain or dynamic environments where the spatial scales of searching patterns cannot be tuned to target distributions…Nature repeatedly reveals the limits of our imagination. Lévy walks once thought to be the preserve of probabilistic foragers have now been identified in the movement patterns of human hunter-gatherers
Levy walk random versus Brownian motion random movement

Implications for thinking about complexity

Uncertainty of future states is a common characteristic of many complex systems, though not unique to these.  One strategy that human organisations can use to deal with uncertainty is to build up capital reserves, thus enhancing longer term resilience albeit at the cost of more immediate efficiencies. From the first set of papers referenced above, it seems like the deliberate and bounded use of randomness could provide a useful second option. The work being done on Levy walks also suggests that there are interesting variations on randomisation that should be explored.  It is already the case the designers of search/opitimisation algorithms have headed this way. If you are interested, you can read further on the subject of what are called  “Levy Flight ” algorithms.

On a more light hearted note, I would be interested to hear from the Cynefin school on how comfortable they would be marketing this approach to “managing” uncertainty to the managers and leaders they seem keen to engage with.

Another thought…years ago I did an analysis of data that had been collected on development projects that had been funded by the then DFID’s funded Civil Society Challenge Fund. This included data on project proposals, proposal assessments, and project outcomes. I used Rapid Miner Studio’s Decision Tree  module to develop predictive models of achievement ratings of the funded projects. Somewhat disappointingly, I failed to identify any attributes of project proposals, or how they had been initially assessed, which were good predictors of the subsequent performance of those projects. There are number of possible reasons why this might so. One of which may be the scale of the uncertainty gap between the evident likely failures and the evident likely successes. Various biases may have skewed judgements within this zone in a way that undermined the longer term predictive use of the proposal screening and approval process. Somewhat paradoxically, if instead a lottery mechanism had been used for selecting fundable proposals in the uncertainty zone this may well have led to the approval process being a better predictor eventual project performance.

Postscript: Subsequent finds…

  •  The Powerball Revolution. By Malcom Gladwell (n.d.). Revisionist History Season 5 Episode 3. Retrieved 7 April 2021, from
    • On school student council lotteries in Bolivia
      • “Running for an office” and “Running an office” can be two very different things. Lotteries diminish the former and put the focus on the latter
      • “Its a more diverse group” that end up on the council, compared to those selected via election
      • “Nobody knows anything” -initial impressions of capacity are often not good predictors of leadership capacity. Contra assumption that voters can be good predictors of capacity in office.
    • Medical research grant review and selection
      • Review scores of proposals are poor predictors of influential and innovative research (based on citation analysis), but has been in use for decades.
    • A boarding school in New Jersey


Mapping the Standards of Evidence used in UK social policy.

Puttick, R. (2018). Mapping the Standards of Evidence used in UK social policy. Alliance for Useful Evidence.
“Our analysis focuses on 18 frameworks used by 16 UK organisations for judging evidence used in UK domestic social policy which are relevant to government, charities, and public service providers.
In summary:
• There has been a rapid proliferation of standards of evidence and other evidence frameworks since 2000. This is a very positive development and reflects the increasing sophistication of how evidence is generated and used in social policy.
• There are common principles underpinning them, particularly the shared goal of improving decision-making, but they often ask different questions, are engaging different audiences, generate different content, and have varying uses. This variance reflects the host organisation’s goals, which can be to inform its funding decisions, to make recommendations to the wider field, or to provide a resource for providers to help them evaluate.
• It may be expected that all evidence frameworks assess whether an intervention is working, but this is not always the case, with some frameworks assessing the quality of evidence, not the success of the intervention itself.
• The differences between the standards of evidence are often for practical reasons and reflect the host organisation’s goals. However, there is a need to consider more philosophical and theoretical tensions about what constitutes good evidence. We identified examples of different organisations reaching different conclusions about the same intervention; one thought it worked well, and the other was less confident. This is a problem: Who is right? Does the intervention work, or not? As the field develops, it is crucial that confusion and disagreement is minimised.
• One suggested response to minimise confusion is to develop a single set of standards of evidence. Although this sounds inherently sensible, our research has identified several major challenges which would need to be overcome to achieve this.
• We propose that the creation of a single set of standards of evidence is considered in greater depth through engagement with both those using standards of evidence, and those being assessed against them. This engagement would also help share learning and insights to ensure that standards of evidence are effectively achieving their goals.

Computational Modelling: Technological Futures

Council for Science and Technology & Government Office for Science, 2020. Available as pdf

Not the most thrilling/enticing title, but differently of interest. Chapter  3 provides a good overview of different ways of building models. Well worth a read, and definitely readable.

Recommendation 2: Decision-makers need to be intelligent customers for models, and those that supply models should provide appropriate
guidance to model users to support proper use and interpretation. This includes providing suitable model documentation detailing the model purpose, assumptions, sensitivities, and limitations, and evidence of appropriate quality assurance.

Chapters 1-3

The Alignment Problem: Machine Learning and Human Values

By Brian Christian. 334 pages. 2020 Norton. Author’s web page here

Brian Christian talking about his book on YouTube

RD comment: This is one of the most interesting and informative books I have read in the last few years. Totally relevant for evaluators thinking about the present and about future trends

Releasing the power of digital data for development. A guide to new opportunities

Releasing the power of digital data for development: A guide to new opportunities. (2020). Frontier Technologies, UKAID, NIRAS.

Section 1  Executive Summary
Section 2 Introduction
Section 3 Understanding and navigating the new data landscape
Section 4  What is needed to release the new potential?
Section 5  Further considerations
Appendix 1: Data opportunities potentially useful now in testing  environments
Appendix 2: Bibliography and further reading
Appendix 3: Methodological notes

Executive Summary

There are 8 conclusions we discuss in this report.

1. There is justified excitement and proven benefits in the use of new digital data sources, particularly where timeliness of data is important or there are persistent gaps in traditional data sources.  This might include data from fragile and conflict-affected states, data supporting decision-making about marginalised population groups, or in finding solutions to address persistent ethical issues where traditional sources have not proved adequate.

2. In many cases, improvements in and greater access to traditional data sources could be more effective than just new data alone, including developing traditional data in tandem with new data sources. This includes innovations in digitising traditional data sources, supporting the sharing of data between and within organisations, and integrating the use of new data sources with traditional data.

3. Decision-making around the use of new data sources should be highly devolved by empowering individual staff and be focused on multiple dimensions of data quality, not least because there are no “one size fits all” rules that determine how new digital data sources fit to specific needs, subject matters or geographies. This could be supported by ensuring:
a. Research, innovation, and technical support are highly demand-led, driven by specific data user needs in specific contexts; and
b. Staff have accessible guidance that demystifies the complexities of new data sources, clarifies the benefits and risks that need to be managed, and allows them to be ‘data brokers’ confident in navigating the new data landscape, innovating in it, and coordinating the technical expertise of others.

The main report includes a description of the evidence and conclusions in a way that supports these aims, including a set of guides for staff about the most promising new data sources.

4. Where traditional data sources are failing to provide the detailed data needed, most new data sources provide a potential route to helping with the Agenda 2030 goal to ‘leave no-one behind,’ as often they can provide additional granularity on population sub-groups.  But, to avoid harming the interests of marginalised groups, strong ethical frameworks are needed, and affected people should be involved in decisionmaking about how data is processed and used. Action is also required to ensure strong data protection environments according to each type of new data and the contexts of its use.

5. New data sources with the highest potential added value for exploitation now, especially when combined with each other or traditional data sources, were found to be:
a. data from Earth Observation (EO) platforms (including satellites and drones)
b. passive location data from mobile phones

6. While there are specific limitations and risks in different circumstances, each of these data sources provides for significant gains in certain dimensions of data quality compared to some traditional sources and other new data sources. The use of Artificial Intelligence (AI) techniques, such as through machine learning, has high potential to add value to digital datasets in terms of improving aspects of data quality from many different sources, such as social media data, and particularly with large complex datasets and across multiple data sources.

7. Beyond the current time horizon, the most potential for emerging data sources is likely to come from:
• The next generation of Artificial Intelligence
• The next generation of Earth Observation platforms
• Privacy Preserving Data Sharing (PPDS) via the Cloud and
• the Internet of Things (IoT).
No significant other data sources, technologies or techniques were found with high potential to benefit FCDO’s work, which seems to be in line with its current research agenda and innovative activities. Some longer-term data prospects have been identified and these could be monitored to observe increases in their potential in the future.

8. Several other factors are relevant to the optimal use of digital data sources which should be investigated and/or work in these areas maintained. These include important internal and external corporate developments, importantly including continued support to Open Data/ data sharing and enhanced data security systems to underpin it, learning across disciplinary boundaries with official statistics principles at the core, and continued support to capacity-building of national statistical systems in developing countries in traditional data and data innovation.

Brian Castellani’s Map of the Complexity Sciences

I have limited tolerance for “complexity babble” That is, people talking about complexity in abstract and ungrounded, and in effect, practically inconsequential terms. Also in ways that give no acknowledgement to the surrounding history of ideas.

So, I really appreciate the work Brian has put into his “Map of the Complexity Sciences” produced in 2018. And thought it deserves wider circulation. Note that this is one of a number of iterations and more iterations are likely in the future. Click on the image to go to a bigger copy.

And please note: when you get taken to the bigger copy and when you click on any node a hypertext link there, this will take you to another web page providing detailed information about that concept or person. A lot of work has gone into the construction of this map, which deserves recognition.

Here is a discussion of an earlier iteration:

Linked Democracy Foundations, Tools, and Applications

Poblet, Marta, Pompeu Casanovas, and Víctor Rodríguez-Doncel. 2019. Linked Democracy: Foundations, Tools, and Applications. SpringerBriefs in Law. Cham: Springer International Publishing. Available in PDF form online

“It is only by mobilizing knowledge that is widely dispersed
across a genuinely diverse community that a free society can
hope to outperform its rivals while remaining true to its

(Ober 2008, 5) cited on page v

Chapter 1 Introduction to Linked Data Abstract This chapter presents Linked Data, a new form of distributed data on the web which is especially suitable to be manipulated by machines and to share knowledge. By adopting the linked data publication paradigm, anybody can publish data on the web, relate it to data resources published by others and run artificial intelligence algorithms in a smooth manner. Open linked data resources may democratize the future access to knowledge by the mass of internet users, either directly or mediated through algorithms. Governments have enthusiastically adopted these ideas, which is in harmony with the broader open data movement.

Chapter 2 Deliberative and Epistemic Approaches to Democracy Abstract Deliberative and epistemic approaches to democracy are two important dimensions of contemporary democratic theory. This chapter studies these dimensions in the emerging ecosystem of civic and political participation tools, and appraises their collective value in a new distinct concept: linked democracy. Linked democracy is the distributed, technology-supported collective decision-making process, where data, information and knowledge are connected and shared by citizens online. Innovation and learning are two key elements of Athenian democracies which can be facilitated by the new digital technologies, and a cross-disciplinary research involving computational scientists and democratic theorists can lead to new theoretical insights of democracy

Chapter 3 Multilayered Linked Democracy An infinite amount of knowledge is waiting to be unearthed. —Hess and Ostrom (2007) Abstract Although confidence in democracy to tackle societal problems is falling, new civic participation tools are appearing supported by modern ICT technologies. These tools implicitly assume different views on democracy and citizenship which have not been fully analysed, but their main fault is their isolated operation in non-communicated silos. We can conceive public knowledge, like in Karl Popper’s World 3, as distributed and connected in different layers and by different connectors, much as it happens with the information in the web or the data in the linked data cloud. The interaction between people, technology and data is still to be defined before alternative institutions are founded, but the so called linked democracy should rest on different layers of interaction: linked data, linked platforms and linked ecosystems; a robust connectivity between democratic institutions is fundamental in order to enhance the way knowledge circulates and collective decisions are made.

Chapter 4 Towards a Linked Democracy Model Abstract In this chapter we lay out the properties of participatory ecosystems as linked democracy ecosystems. The goal is to provide a conceptual roadmap that helps us to ground the theoretical foundations for a meso-level, institutional theory of democracy. The identification of the basic properties of a linked democracy eco-system draws from different empirical examples that, to some extent, exhibit some of these properties. We then correlate these properties with Ostrom’s design principles for the management of common-pool resources (as generalised to groups cooperating and coordinating to achieve shared goals) to open up the question of how linked democracy ecosystems can be governed

Chapter 5 Legal Linked Data Ecosystems and the Rule of Law Abstract This chapter introduces the notions of meta-rule of law and socio-legal ecosystems to both foster and regulate linked democracy. It explores the way of stimulating innovative regulations and building a regulatory quadrant for the rule of law. The chapter summarises briefly (i) the notions of responsive, better and smart regulation; (ii) requirements for legal interchange languages (legal interoperability); (iii) and cognitive ecology approaches. It shows how the protections of the substantive rule of law can be embedded into the semantic languages of the web of data and reflects on the conditions that make possible their enactment and implementation as a socio-legal ecosystem. The chapter suggests in the end a reusable multi-levelled meta-model and four notions of legal validity: positive, composite, formal, and ecological.

Chapter 6 Conclusion Communication technologies have permeated almost every aspect of modern life, shaping a densely connected society where information flows follow complex patterns on a worldwide scale. The World Wide Web created a global space of information, with its network of documents linked through hyperlinks. And a new network is woven, the Web of Data, with linked machine-readable data resources that enable new forms of computation and more solidly grounded knowledge. Parliamentary debates, legislation, information on political parties or political programs are starting to be offered as linked data in rhizomatic structures, creating new opportunities for electronic government, electronic democracy or political deliberation. Nobody could foresee that individuals, corporations and government institutions alike would participate …(continues)