Who gets to “speak up” in politics? Whose voices are silenced? We conducted two field experiments to understand how harassment shapes the everyday experiences of politics for men and women in the United States today. We randomized the names campaign volunteers used to text supporters reminders to participate in a protest and call their representatives. We find that female-named volunteers receive more offensive, silencing, and withdrawal responses than male-named or ambiguously named volunteers. However, supporters were also more likely to respond and agree to their asks. These findings help make sense of prior research that finds women are less likely than men to participate in politics, and raise new questions about whether individual women may be perceived as symbolic representatives of women as a group. We conclude by discussing the implications for gender equality and political activism.
A few months ago, Paolo Spada and I published a blog post about sortition and the representativeness of citizens’ assemblies. We were pleasantly surprised by the response to our post and the ensuing discussions.
In this new exchange at the Deliberative Democracy Digest, Kyle Redman, Paolo Spada, and I try to delve deeper, exploring further the challenges of achieving representativeness in deliberative mini-publics. We extend our gratitude to Nicole Curato and Lucy J. Parry from the Centre for Deliberative Democracy and Global Governance for suggesting and facilitating this discussion.
A few years ago, Tom Steinberg and I discussed the potential risks posed by AI bots in influencing citizen engagement processes and manipulating public consultations. With the rapid advancement of AI technology, these risks have only intensified. This escalating concern has even elicited an official response from the White House.
A recent executive order has tasked the Office of Information and Regulatory Affairs (OIRA) at the White House with considering the implementation of guidance or tools to address mass comments, computer-generated remarks, and falsely attributed comments. This directive comes in response to growing concerns about the impact of AI on the regulatory process, including the potential for generative chatbots to lead mass campaigns or flood the federal agency rule-making process with spam comments.
The threat of manipulation becomes even more pronounced when content generated by bots is viewed by policymakers as being on par with human-created content. There’s evidence to suggest that this may be already occurring in certain scenarios. For example, a recent experiment was designed to measure the impact of language models on effective communication with members of Congress. The goal was to determine if these models could divert legislative attention by generating a constant stream of unique emails directed at congressional members. Both human writers and GPT-3 were employed in the study. Emails were randomly sent to over 7,000 state representatives throughout the country, after which response rates were compared. The results showed a mere 2% difference in response rates, and for some of the policy topics studied, the response rates remained consistent.
Now, the real trouble begins when governments jump on the bot bandwagon and start using their own bots to respond, and we, the humans, are left out of the conversation entirely. It’s like being the third wheel on a digital date that we didn’t even know was happening. That’s a gloomy scenario.
Diversity is a key ingredient in the recipe for collective intelligence because it brings together a range of perspectives, tools, and abilities; allowing for a more comprehensive approach to problem-solving and decision-making. Gender diversity on corporate boards improves firms’ performance, ethnic diversity produces more impactful scientific research, diverse groups are better at solving crimes, popular juries are less biased than professional judges, and politically diverse editorial teams produce higher-quality Wikipedia articles.
Large language models, like those powering AI systems, rely heavily on datasets or corpora, with a significant part of it based on English content. This dominance is consequential. Just as diverse groups of people yield richer outcomes, an AI trained on diverse linguistic data offers a broader perspective. Each language encapsulates unique thoughts, metaphors, and wisdom. Without diverse linguistic representation, we risk fostering AI systems with limited collective intelligence. The quality, diversity, and quantity of the data they are trained on directly influence their epistemic outputs. Unsurprisingly, large language models struggle to capture long-tail knowledge.
This comes with two major — at least hypothetically — risks: 1) systems that do not fully leverage the knowledge dispersed in the population, 2) the benefits of AI may be more accessible to some groups over others; for instance, speakers of less-dominant languages might not equally benefit from AI’s advancements. It’s not merely about translation; it’s the nuances and knowledge embedded in languages that might be overlooked.
There are also two additional dimensions that could reinforce biases in AI systems: 1) as future models are trained on content that might have been generated by AI, there may be a reinforcing effect where biases present in the initial training data are amplified over time; and 2) techniques such as guided transfer learning may also increase biases if the source model used in transfer learning is trained on biased data.
This introduces a nuanced dimension to the digital divide. Historically, the digital divide was characterized by access to technology, internet connectivity, digital skills, and the socio-economic variables shaping these factors. Yet, with AI, our understanding of what constitutes digital divide should expand. It’s a subtler yet crucial divide that policymakers and development practitioners might not yet fully recognize.
For proponents of deliberative democracy, the last couple of years could not have been better. Propelled by the recent diffusion of citizens’ assemblies, deliberative democracy has definitely gained popularity beyond small circles of scholars and advocates. From CNN to the New York Times, the Hindustan Times (India), Folha de São Paulo (Brazil), and Expresso (Portugal), it is now almost difficult to keep up with all the interest in democratic models that promote the random selection of participants who engage in informed deliberation. A new “deliberative wave” is definitely here.
But with popularity comes scrutiny. And whether the deliberative wave will power new energy or crash onto the beach, is an open question. As is the case with any democratic innovation (institutions designed to improve or deepen our existing democratic systems), critically examining assumptions is what allows for management of expectations and, most importantly, gradual improvements.
Proponents of citizens’ assemblies put representativeness at the core of their definition. In fact, it is one of their main selling points. For example, a comprehensive report highlights that an advantage of citizens’ assemblies, compared to other mechanisms of participatory democracy, is their typical combination of random selection and stratification to form a public body that is “representative of the public.” This general argument resonates with the media and the wider public. A recent illustration is an article by The Guardian, which depicts citizens’ assemblies as “a group of people who are randomly selected and reflect the demographics of the population as a whole”
It should be noted that claims of representativeness vary in their assertiveness. For instance, some may refer to citizens’ assemblies as “representative deliberative democracy,” while others may use more cautious language, referring to assemblies’ participants as being “broadly representative” of the population (e.g. by gender, age, education, attitudes). This variation in terms used to describe representativeness should prompt an attentive observer to ask basic questions such as: “Are existing practices of deliberative democracy representative?” “If they are ‘broadly’ representative, how representative are they?” “What criteria, if any, are used to assess whether a deliberative democracy practice is more or less representative of the population?” “Can their representativeness be improved, and if so, how?” These are basic questions that, surprisingly, have been given little attention in recent debates surrounding deliberative democracy. The purpose of this article is to bring attention to these basic questions and to provide initial answers and potential avenues for future research and practice.
Citizens Assemblies and three challenges of random sampling
Before discussing the subject of representativeness, it is important to provide some conceptual clarity. From an academic perspective, citizens’ assemblies are a variant of what political scientists normally refer to as “mini-publics.” These are processes in which participants: 1) are randomly selected (often combined with some form of stratification), 2) participate in informed deliberation on a specific topic, and 3) reach a public judgment and provide recommendations on that topic. Thus, in this text, mini-publics serves as a general term for a variety of practices such as consensus conferences, citizens’ juries, planning cells, and citizens’ assemblies themselves.
In this discussion, we will focus on what we consider to be the three main challenges of random sampling. First, we will examine the issue of sample size and the limitations of stratification in addressing this challenge. Second, we will focus on sampling error, which is the error that occurs when observing a sample rather than the entire population. Third, we will examine the issue of non-response, and how the typically small sample size of citizens’ assemblies exacerbates this problem. We conclude by offering alternatives to approach the trade-offs associated with mini-publics’ representativeness dilemma.
Minimal sample size, and why stratification does not help reducing sample size requirements in complex populations
Most mini-publics that we know of have a sample size of around 70 participants or less, with a few cases having more than 200 participants. However, even with a sample size of 200 people, representing a population accurately is quite difficult. This may be the reason why political scientist Robert Dahl, who first proposed the use of mini-publics over three decades ago, suggested a sample size of 1000 participants. This is also the reason why most surveys that attempt to represent a complex national population have a sample size of over 1000 people.
To understand why representing a population accurately is difficult, consider that a sample size of approximately 370 individuals is enough to estimate a parameter of a population of 20,000 with a 5% error margin and 95% confidence level (for example, estimating the proportion of the population that answers “yes” to a question). However, if the desired error margin is reduced to 2%, the sample size increases to over 2,000, and for a more realistic population of over 1 million, a sample size of over 16,000 is required to achieve a 1% error margin with 99% confidence. Although the size of the sample required to estimate simple parameters in surveys does not increase significantly with the size of the population, it still increases beyond the sample sizes currently used in most mini-publics. Sample size calculators are available online to demonstrate these examples without requiring any statistical knowledge.
Stratification is a strategy that can help reduce the error margin and achieve better precision with a fixed sample size. However, stratification alone cannot justify the very small sample sizes that are currently used in most mini-publics (70 or less).
To understand why, let’s consider that we want to create a sample that represents the five important strata of the population and includes all their intersections, such as ethnicity, age, income, geographical location, and gender. For simplicity, let’s assume that the first four categories have five equal groups in society, and gender is composed of two equal groups. The minimal sample required to include the intersections of all the strata and represent this population is equal to 5^4×2=1250. Note that we have maintained the somewhat unlikely assumption that all categories have equal size. If one stratum, such as ethnicity, includes a minority that is 1/10 of the population, then our multiplier would be 10 instead of 5, requiring a sample size of 5^3x10x2=2500.
The latter is independent of the number of categories within the strata, so even if the strata have only two categories, one comprising 90% (9/10) of the population and one comprising 10% (1/10) of the population, the multiplier would still be 10. When we want to represent a minority of 1% (1/100) of the population, the multiplier becomes 100. Note that this minimal sample size would include the intersection of all the strata in such a population, but such a small sample will not be representative of each stratum. To achieve stratum-level representation, we need to increase the number of people for each stratum following the same mathematical rules we used for simple sampling, as described at the beginning of this section, generating a required sample size in the order of hundreds of thousand of people (in our example above 370×2500=925000).
This is without even entering into the discussion of what should be the ideal set of strata to be used in order to achieve legitimacy. Should we also include attitudes such as liberal vs conservative? Opinions on the topic of the assembly? Metrics of type of personality? Education? Income? Previous level of engagement in politics? In sum, the more complex the population is, the larger the sample required to represent it.
Sampling error due to a lack of a clear population list
When evaluating sampling methods, it is important to consider that creating a random sample of a population requires a starting population to draw from. In some fields, the total population is well-defined and data is readily available (e.g. students in a school, members of parliament), but in other cases such as a city or country, it becomes more complicated.
The literature on surveys contains multiple publications on sampling issues, but for our purposes, it is sufficient to note that without a police state or similar means of collecting an unprecedented amount of information on citizens, creating a complete list of people in a country to draw our sample from is impossible. All existing lists (e.g. electoral lists, telephone lists, addresses, social security numbers) are incomplete and biased.
This is why survey companies charge significant amounts of money to allow customers to use their model of the population, which is a combination of multiple subsamples that have been optimized over time to answer specific questions. For example, a survey company that specializes in election forecasting will have a sampling model optimized to minimize errors in estimating parameters of the population that might be relevant for electoral studies, while a company that specializes in retail marketing will have a model optimized to minimize forecasting errors in predicting sales of different types of goods. Each model will draw from different samples, applying different weights according to complex algorithms that are optimized against past performance. However, each model will still be an imperfect representation of the population.
Therefore, even the best possible sampling method will have an inherent error. It is difficult, if not impossible, to perfectly capture the entire population, so our samples will be drawn from a subpopulation that carries biases. This problem is further accentuated for low-cost mini-publics that cannot afford expensive survey companies or do not have access to large public lists like electoral or census lists. These mini-publics may have a very narrow and biased initial subpopulation, such as only targeting members of an online community, which brings its own set of biases.
Non-response
A third factor, well-known among practitioners and community organizers, is the fact that receiving an invitation to participate does not mean a person will take part in the process. Thus, any invitation procedure has issues of non-participation. This is probably the most obvious factor that prevents one from creating representative samples of the population. In mini-publics with large samples of participants, such as Citizens’ Assemblies, the conversion rate is often quite low, sometimes less than 10%. By conversion rate, we mean the percentage of the people contacted that say that they are willing to participate and enter the recruitment pool. Simpler mini-publics of shorter duration (e.g. one weekend) often achieve higher engagement. A dataset on conversion rates of mini-publics does not exist, but our own experience in organizing Citizens Assemblies, Deliberative Polls, and clones tell us that it is possible to achieve more than 20% conversion when the topic is very controversial. For example, in the UK’s Citizens’ Assembly on Brexit in 2017, 1,155 people agreed to enter the recruitment pool out of the 5,000 contacted, generating a conversion rate of 23.1%, as illustrated below.[1]
Figure 1: Contact and recruitment numbers UK’s Citizens Assembly on Brexit (Renwick et al. 2017)
We do not pretend to know all the existing cases, and so this data should be taken with caution. Maybe there have been cases with 80% conversion, given it is possible to achieve such rates in surveys. But even in such hypothetical best practices, we would have failed to engage 20% of the population. More realistically, with 10 to 30% engagement, we are just engaging a very narrow subset of the population.
Frequent asked questions, and why we should not abandon sortition
It is clear from the points above that the assertion that the current generation of relatively small mini-publics is representative of the population from which it is drawn is questionable. Not surprisingly, the fact that participants of mini-publics differ from the population they are supposed to represent has already been documented over a decade ago.[2] However, in our experience, when confronted with these facts, practitioners and advocates of mini-publics often raise various questions. Below, we address five frequently asked questions and provide answers for them.
“But people use random sampling for surveys and then claim that the results are representative, what is the difference for mini-publics?”
The first difference we already discussed between surveys and mini-publics is that surveys that aim to represent a large population use larger samples.
The second difference, less obvious, is that a mini-public is not a system that aggregates fixed opinions. Rather, one of the core principles of mini-publics is that participants deliberate and their opinions may change as a result of the group process and composition. Our sampling procedures, however, are based on the task of estimating population parameters, not generating input for legitimate decision making. While a 5% error margin with 95% confidence level may be acceptable in a survey investigating the proportion of people who prefer one policy over another, this same measure cannot be applied to a mini-public because participants may change their opinions through the deliberation process. A mini-public is not an estimate derived from a simple mathematical formula, but rather a complex process of group deliberation that may transform input preferences into output preferences and potentially lead to important decisions. Christina Lafont has used a similar argument to criticize even an ideal sample that achieves perfect input representativeness.[3]
“But we use random assignment for experiments and then claim that the results are representative, what is the difference for mini-publics?”
Mini-publics can be thought of as experiments, similar to clinical trials testing the impact of a vaccine. This approach allows us to evaluate the impact of a mini-public on a subset of the population, providing insight into what would happen if a similar subset of the population were to deliberate. Continuing this metaphor, if the mini-public participants co-design a new policy solution and support its implementation, any similar subsets of the population going through an identical mini-public process should generate a similar output.
However, clinical trials require that the vaccine and a placebo be randomly assigned to treatment and control groups. This approach is only valid if the participants are drawn from a representative sample and cannot self-select into each experimental arm.
Unfortunately, few mini-publics compare the decisions made by members to those who were not selected, and this is not considered a key element for claiming representativeness or legitimacy. Furthermore, while random assignment of treatment and control is crucial for internal validity, it does not guarantee external validity. That is, the results may not be representative of the larger population, and the estimate of the treatment effect only applies to the specific sample used in the experiment.
While the metaphor of the experiment as a model to interpret mini-publics is preferable to the metaphor of the survey, it does not solve the issue of working with non-representative samples in practice. Therefore, we must continue to explore ways to improve the representativeness of mini-publics and take into account the limitations of the experimental metaphor when designing and interpreting their results.
“Ok, mini-publics may not be perfect, but are they not clearly better than other mechanisms?”
Thus far, we have provided evidence that the claim of mini-publics as representative of the population is problematic. But what about more cautious claims, such as mini-publics being more inclusive than other participatory processes (e.g., participatory budgeting, e-petitions) that do not employ randomization? Many would agree that traditional forms of consultation tend to attract “usual suspects” – citizens who have a higher interest in politics, more spare time, higher education, enjoy talking in public, and sometimes enjoy any opportunity to criticize. In the US, for instance, these citizens are often older white males, or as put by a practitioner once, “the male, pale and stale.” A typical mini-public instead manages to engage a more diverse set of participants than traditional consultations. While this is an obvious reality, the engagement strategies of mini-publics compared to traditional consultations based on self-selection have very different levels of sophistication and costs. Mini-publics tend to invest more resources in engagement, sometimes tens of thousands of dollars, and thus we cannot exclude that existing results in terms of inclusion are purely due to better outreach techniques, such as mass recruitment campaigns and stipends for the participants.
Therefore, it is not fair to compare traditional consultations to mini-publics. As it is not fair to compare mini-publics that are not specifically designed to include marginalized populations to open-to-all processes that are specifically designed for this purpose. The classic critique of feminist, intersectional and social movement scholars that mini-publics design does not consider existing inequalities, and thus is inferior to dedicated processes of minority engagement is valid in that case. This is because the amount dedicated to engagement is positively correlated with inclusion. For instance, processes specifically designed for immigrants and native populations will have more inclusive results than a general random selection strategy that does not have specific quotas for these groups and engagement strategies for them.
We talk past one another when we try to rank processes with respect to their supposed inclusion performance without considering the impact of the resources dedicated to engagement or their intended effects (e.g. redistribution, collective action).
It is also difficult to determine which approach is more inclusive without a significant amount of research comparing different participatory methods with similar outreach and resources. As far as we know, the only study that compares two similar processes – one using random engagement and the other using an open-to-all invitation – found little difference in inclusiveness.[4] It also highlighted the importance of other factors such as the design of the process, potential political impact, and the topic of discussion. Many practitioners do not take these factors into account, and instead focus solely on recruitment strategies. While one study is not enough to make a conclusive judgment, it does suggest that the assumption that mini-publics using randomly selected participants are automatically more inclusive than open-to-all processes is problematic.
“But what about the ergonomics of the process and deliberative quality? Small mini-publics are undeniably superior to large open-to-all meetings.”
One of the frequently advertised advantages of small mini-publics is their capacity to support high-quality deliberation and include all members of the sample in the discussion. This is a very clear advantage; however, it has nothing to do with random sampling. It is not difficult to imagine a system in which an open-to-all meeting is called and then such a meeting selects a smaller number of representatives that will proceed to discuss using high-quality deliberative procedures. The selection rule could include quotas so that the selected members respect criteria of diversity of interest (even though, as we argued before, that would not be representative of the entire group). The ergonomics and inclusion advantages are purely linked with the size of the assembly and the process used to support deliberation.
“So, are you saying we should abandon sortition?”
We hope that it is now clearer why we contend that it is conceptually erroneous to defend the application of sortition in mini-publics based on their statistical representation of the population. So, should sortition be abandoned? Our position is that it should not, and for one less obvious and counterintuitive argument in favor of random sampling: it offers a fair way to exclude certain groups from the mini-public. This is particularly so because, in certain cases, participatory mechanisms based on self-selection may be captured by organized minorities to the detriment of disengaged majorities.
Consider, for instance, one of President Obama’s first attempts to engage citizens at large-scale, the White House’s online town-hall. Through a platform named “open for questions,” citizens were able to submit questions to Obama and vote for which questions they would like to be answered by him. Over 92,000 people posted questions, and about 3.6 million votes were cast for and against those questions. Under the section “budget” of the questions, seven of the ten most popular queries were about legalizing marijuana, many of which were about taxing it. The popularity of this issue was attributed to a campaign led by NORML, an organization advocating for pot legalization. While the cause and ideas may be laudable, it is fair to assume that this was hardly the biggest budgetary concern of Americans in the aftermath of an economic downturn.
(Picture by Pete Souza, Wikimedia Commons)
In a case like the White House’s town-hall, the randomization of people to participate would be a fair and effective way to avoid the capture of the dialogue by organized groups. Randomization does not completely exclude the possibility of capture of a deliberative space, but it does increase the costs of doing so. The probability that members of an organized minority are randomly sampled to participate in a mini-public is minor, therefore the odds of their presence in the mini-public will be minor. Thus, even if we had a technological solution capable of organizing large-scale deliberation in the millions, a randomization strategy could still be an effective means to protect deliberation from the capture by organized minorities. A legitimate method of exclusion will remain an asset – at least until we have another legitimate way to mitigate the ability of small, organized minorities to bias deliberation.
The way forward for mini-publics: go big or go home?
There is clearly a case for increasing the size of mini-publics to improve their ability to represent the population. But there is also a trade-off between the size of the assembly and the cost required to sustain high-quality deliberation. With sizes approaching 1000 people, hundreds of moderators will be required and much of the exchange of information will occur not through synchronous exchanges in small groups, but through asynchronous transmission mechanisms across the groups. This is not necessarily a bad thing, but it will have the typical limitations of any type of aggregation mechanism that requires participant attention and effort. For example, in an ideation process with 100 groups of 10 people each, where each group proposes one idea and then discusses all other ideas, each group would have to discuss 100 ideas. This is a very intense task. However, there could be filtering mechanisms that require subgroups to eliminate non-interesting ideas, and other solutions designed to reduce the amount of effort required by participants.
All else being equal, as the size of the assembly grows, the logistical complexity and associated costs increases. At the same time, the ability to analyze and integrate all the information generated by participants diminishes. The question of whether established technologies like argument mapping, or even emerging artificial intelligence could help overcome the challenges associated with mass deliberation is an empirical one – but it’s certainly an avenue worth exploring through experiments and research. Recent designs of permanent mini-publics such as the one adopted in Belgium (Ostbelgien, Brussels) and Italy (Milan) that resample a small new group of participants every year could attempt to include over time a sufficiently large sample of the population to achieve a good level of representation, at least for some strata of the population, and as long as systematic sampling errors are corrected, and obvious caveats in terms of representativeness are clearly communicated.
Another approach is to abandon the idea of achieving representativeness and instead target specific problems of inclusion. This is a small change in the current approach to mini-publics, but in our opinion, it will generate significant returns in terms of long-term legitimacy. Instead of justifying a mini-public through a blanket claim of representation, the justification in this model would emerge from a specific failure in inclusion. For example, imagine that neighborhood-level urban planning meetings in a city consistently fail to involve renters and disproportionately engage developers and business owners. In such a scenario, a stratified random sample approach that reserves quotas for renters and includes specific incentives to attract them, and not the other types of participants, would be a fair strategy to prevent domination. However, note that this approach is only feasible after a clear inclusion failure has been detected.
In conclusion, from a democratic innovations’ perspective, there seems to be two productive directions for mini-publics: increasing their size or focusing on addressing failures of inclusiveness. Expanding the size of assemblies involves technical challenges and increased costs, but in certain cases it might be worth the effort. Addressing specific cases of exclusion, such as domination by organized minorities, may be a more practical and scalable approach. This second approach might not seem very appealing at first. But one should not be discouraged by our unglamorous example of fixing urban planning meetings. In fact, this approach is particularly attractive given that inclusion failures can be found across multiple spaces meant to be democratic – from neighborhood meetings to parliaments around the globe.
For mini-public practitioners and advocates like ourselves, this should come as a comfort: there’s no shortage of work to be done. But we might be more successful if, in the meantime, we shift the focus away from the representativeness claim.
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We would like to express our gratitude to Amy Chamberlain, Andrea Felicetti, Luke Jordan, Jon Mellon, Martina Patone, Thamy Pogrebinschi, Hollie Russon Gilman, Tom Steinberg, and Anthony Zacharewski for their valuable feedback on previous versions of this post.
[1] Renwick, A., Allan, S., Jennings, W., McKee, R., Russell, M. and Smith, G., 2017. A Considered Public Voice on Brexit: The Report of the Citizens’ Assembly on Brexit.
[2] Goidel, R., Freeman, C., Procopio, S., & Zewe, C. (2008). Who participates in the ‘public square’ and does it matter? Public Opinion Quarterly, 72, 792- 803. doi: 10.1093/poq/nfn043
[3] Lafont, C., 2015. Deliberation, participation, and democratic legitimacy: Should deliberative mini‐publics shape public policy?. Journal of political philosophy, 23(1), pp.40-63.
[4] Griffin J. & Abdel-Monem T. & Tomkins A. & Richardson A. & Jorgensen S., (2015) “Understanding Participant Representativeness in Deliberative Events: A Case Study Comparing Probability and Non-Probability Recruitment Strategies”, Journal of Public Deliberation 11(1). doi: https://doi.org/10.16997/jdd.221
“Civic tech” broadly refers to the use of digital technologies to support a range of citizen engagement processes. From allowing individuals to report problems to local government to enabling the crowdsourcing of national legislation, civic tech aims to promote better policies and services – while contributing to more inclusive democratic institutions. But could civic tech affect public issues in a way that benefits some and excludes others?
Over the decades, the question of who participates in and who is excluded from participation mediated by technology has been the focus of both civic tech critics and proponents. The latter tend to argue that, by enabling citizens to participate without constraints of time and distance, civic tech facilitates the participation of those who usually abstain from engaging with public issues, leading to more inclusive processes. Critics argue that, given the existing digital divide, unequal access to technology will tend to empower the already empowered, further deepening societal differences. Yet both critics and proponents do tend to share an intuitive assumption: the socio-economic profile of whoparticipates is the primary determinant of who benefits from digitally mediated civic participation. For instance, if more men participate, outcomes will favor male preferences, and if more young people participate, outcomes will be more aligned with the concerns of the youth.
In a new paper, we show that the link between the demographics of those participating through digital channels, and the beneficiaries of the participation process, is not necessarily as straightforward as commonly assumed. We review four civic tech cases where data allow us to trace the full participatory chain through:
the initial digital divide
the participant’s demographics
the demands made through the process
the policy outcomes
We examine online voting in the Brazilian state of Rio Grande do Sul’s participatory budgeting process, the local problem reporting platform Fix My Street (FMS) in the United Kingdom, Iceland’s online crowdsourced constitution process, and the global petitioning platform Change.org.
Counterintuitive findings
Change.org has been used by nearly half a billion people around the globe. Using a dataset of 3.9 million signers of online petitions in 132 countries, we examine the number of successful petitions and assess whether petitions created by women have more success than those submitted by men. Our analysis shows that, even if women create fewer online petitions than men, their petitions are more likely to be successful. All else equal, when online petitions have an impact on government policy, the agenda being implemented is much closer to the issues women choose to focus on.
In Rio Grande do Sul’s digital participatory budgeting (PB), we show that despite important demographic differences between online and offline voters, these inequalities do not affect which types of projects are selected for funding – a consequence of PB’s unique institutional design, which favors redistributive effects.
In fact, of all the cases analyzed, none reflect the standard assumption that inequalities in who participates translate directly into inequalities in who benefits from the policy outcomes. Our results suggest that the socio-economic profile of participants predicts only in part who benefits from civic tech. Just as important to policy outcomes is how the platform translates civic participation into policy demands, and how the government responds to those demands. While civic tech practitioners pay a lot of attention to design from a technological perspective, our findings highlight the importance of considering how civic tech platforms function as political institutions that encourage certain types of behavior while discouraging others.
Civic tech, it seems, is not inherently good nor bad for democratic institutions. Instead, its effect is a combination of who participates on digital platforms and the choices of platform designers and governments.
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Post co-authored with Jonathan Mellon and Fredrik M. Sjoberg. Cross-posted from the World Bank’s Let’s Talk Developmentblog.
Voices in the Code, by David G. Robinson, is finally out. I had the opportunity to read the book prior to its publication, and I could not recommend it enough. David shows how, between 2004 and 2014 in the US, experts and citizens came together to build a new kidney transplant matching algorithm. David’s work is a breath of fresh air for the debate surrounding the impact of algorithms on individuals and societies – a debate typically focused on the negative and sometimes disastrous effects of algorithms. While David conveys these risks at the outset of the book, focusing solely on these threats would add little to a public discourse already saturated with concerns.
One of the major missing pieces in the “algorithmic literature” is precisely how citizens, experts and decision-makers can make their interactions more successful, working towards algorithmic solutions that better serve societal goals. The book offers a detailed and compelling case where a long and participatory process leads to the crafting of an algorithm that delivers a public good. This, despite the technical complexities, moral dilemmas, and difficult trade-offs involved in decisions related to the allocation of kidneys to transplant patients. Such a feat would not be achieved without another contribution of the book, which is to offer a didactical demystification of what algorithms are, normally treated as a reserved domain of few experts.
As David conducts his analysis, one also finds an interesting reversal of the assumed relationship between technology and participatory democracy. This relationship has mostly been examined from a civic tech angle, focusing on how technologies can support democratic participation through practices such as e-petitions, online citizens’ assemblies, and digital participatory budgeting. Thus, another original contribution of this book is to look at this relationship from the opposite angle: how can participatory processes better support technological deployments. While technology for participation (civic tech) remains an important topic, we should probably start paying more attention to how participation can support technological solutions (civic for tech).
Continuing on through the book, other interesting insights emerge. For instance, technology and participatory democracy pundits normally subscribe to the virtues of decentralized systems, both from a technological and institutional perspective. Yet David depicts precisely the virtues of a decision-making system centralized at the national level. Should organ transplant issues be decided at the local level in the US, the results would probably not be as successful. Against intuition, David presents a clear case where centralized (although participatory) systems might offer better collective outcomes. Surfacing this counterintuitive finding is a welcome contribution to debates on the trade-offs between centralization and decentralization, both from a technological and institutional standpoint.
But a few paragraphs here cannot do the book justice. Voices in the Code is certainly a must-read for anybody working on issues ranging from institutional design and participatory democracy, all the way to algorithmic accountability and decision support systems.
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P.s. As an intro to the book, here’s a nice 10 min. conversation with David on the Marketplace podcast.
In a dialogue with his disciple Tsze-kung, Confucius advises that a government needs three things: weapons, food, and trust. And if a ruler cannot maintain the three of them, he should give up the weapons first and the food next.
Fast forward over two thousand years, and trust in government remains a hot topic. After all, rule of law and democratic institutions clearly require some minimal consent and trust in government. And, to varying degrees, the success of most public policies and programs, from paying taxes to recycling, depends on citizens’ compliance and cooperation.
Trust in government has also generated increased interest during the pandemic. For example, in an article for The Atlantic, Francis Fukuyama described trust in government as the most important predictor of a government’s capacity to respond to the ongoing health crisis. His argument is intuitive: particularly during times of crisis, discretionary authority needs to be delegated to executive branches, as “no set of preexisting laws or rules can ever anticipate all of the novel and rapidly changing situations that countries will face”. In that context, so goes the argument, “citizens have to believe that the executive knows what it is doing.” Fukuyama’s take on the importance of trust during the pandemic resonates with evidence from previous health crises and, not surprisingly, scholarly interest in the subject has soared since the outbreak. Confucius’ argument still resonates.
Equally interesting, however, has been the emergence of a public debate that also asks whether, in some cases, trust in government may be unwarranted. For instance, reporting on the health situation in Lebanon, a Washington Post article highlighted that “paradoxically, distrust of the notoriously dysfunctional government may have helped.” In a similar vein, The Economist argued that, during an epidemic, trust can be a “double-edged sword”. The behavior of many political leaders in downplaying the seriousness of the crisis has also illustrated how, in certain cases, distrusting the government is the healthiest option available.
These insights run counter to the conventional wisdom, which tends to consider trust in government as a good in itself. For instance, a frequent selling point for open government reforms is the (often misleading) claim that transparency will lead to increased trust in government. But it shouldn’t take a pandemic to realize that trust in government can also be overrated. Checks and balances, foundational to the modern state, were built upon the premise of distrust. For Montesquieu, the separation of powers was necessary to avoid exposing citizens to “arbitrary control.” David Hume cautioned that when designing government systems, “every man ought to be supposed a knave.” And, for Madison, “If angels were to govern men, neither external nor internal controls on government would be necessary.”
But if there are good reasons to trust in government, as well as good reasons not to, when is trust more or less appropriate? This is certainly a complex question, and the purpose of this post is by no means to provide a definitive answer. Instead, it aims to put forward a very simple (and perhaps simplistic) framework to start thinking about the imponderable problem of trust in government according to its context.
To understand the proposed framework, we can start with the following premise:
All else equal, individuals’ trust in their government should be expected to be proportional to the trustworthiness of that government.
This premise, I believe, is uncontroversial. As put by philosopher Onora O’Neill, trust is valuable only “when placed in trustworthy agents and activities, but damaging or costly when (mis)placed in untrustworthy agents and activities.”
But how do we define trustworthiness? Any attempt is bound to generate contestations, as has been the case for the definition of trust in government. For the purpose of this exercise, I borrow a definition from Margaret Levi, a prominent scholar on the subject, who considers a government to be worthy of citizens’ trust when it
(…) keeps its promises (or has exceptionally good reasons why it fails to), is relatively fair in its decision-making and enforcement processes, and delivers goods and services.
With this definition in mind, and the premise put forward earlier, the more a government keeps its promises, is fair, and delivers goods and services, the more citizens should trust that government, and vice-versa. If we depict the relationships between levels of trust and trustworthiness in a matrix, an interesting perspective emerges, as shown below:
Figure 1: Trust Matrix
In the top right and lower left (quadrants 2 and 3), we have scenarios of consistency, where citizens’ level of trust in governments corresponds to the level of government trustworthiness. In quadrant 2 we have scenarios of constructive consistency, characterized by a virtuous cycle where high trust leads to higher trustworthiness and vice-versa. In these scenarios citizens are, for instance, more willing to cooperate with government policies (e.g. taxation, vaccination), thereby enhancing governmental capacity to respond to public needs (e.g. public investments, disease control). Quadrant 3 represents scenarios of disruptive consistency, with disruptive meaning a context that can lead to both negative and positive developments. On the one hand, it may engender a vicious and destructive cycle whereby the government’s poor performance leads to less trust, further reducing the likelihood that governments are able to perform. On the other hand, it may lead to a process of creative destruction. After all, distrust of authorities often sparks democratic progress. In this respect, a context of disruptive consistency may open spaces of contestation and competition (e.g. through elections), generating incentives for political actors to perform better.
Quadrants 1 and 4 reflect scenarios of inconsistency, where the level of trust in government does not correspond to a government’s trustworthiness. In the cynical quadrant, despite government trustworthiness, citizens still show low levels of trust towards their government. Such a scenario is not free from implications: governmental policies may have disproportionately higher implementation costs, given that the public is less likely to comply and cooperate with these policies. In quadrant 4, (credulous), citizens trust their governments even though they are not deserving of that trust. In this scenario, untrustworthy governments have little incentive to change their behavior (e.g. delivering public goods and services), given that their citizens already trust them and do not present any threat to the status quo. This becomes a low-accountability trap, with citizens unlikely to engage to keep office-holders accountable for their actions in the public realm.
What does this all look like in practice? Into which quadrants do countries fall? While measuring trustworthiness goes beyond the scope of this post (more on that below), we can map it against existing indicators linked to trustworthiness, such as the Government Effectiveness Index, which captures, among other things, quality of public services, quality of civil service and governments’ commitments to their policies.
Figure 2. All over the map: the relationship between trust and government effectiveness
Source: author’s own based on Government Effectiveness Index (2020) and World Economic Forum Executive Survey (2018)
As the figure above illustrates, the 135 countries covered by the dataset fall into all four quadrants. Trust in government, as it turns out, is not consistently aligned with dimensions of government trustworthiness. Yet two-thirds of the countries (89) fall into the dark green consistent scenarios, indicating that in most countries, the level of trust in government is commensurate with the level of trustworthiness. This calls into question narratives that typically suggest a widespread crisis of trust in governments. Increased trust in government in this majority of countries would result in shifts towards situations of credulity: arguably the worst-case scenario, and a boon for women and men in power to behave more as knaves than angels. More importantly, the figure shows that increased trust in government emerges as a clearly desirable goal in just one of the four scenarios of the trust matrix (cynical, upper-left), where only 29 of 135 countries currently find themselves, including Argentina, Poland and South Korea.
Even if this framework may be reductionist, I believe it could still be useful for several reasons, a few of which I would like to highlight. First, the matrix gives us an indication of where trust in government may need to be increased or decreased. If one knows how to increase trust in government (and that’s a bold assumption), the framework provides some guidance on where it would make sense to start.
Second, these scenarios may help us better hypothesize which types of policy reforms would be desirable to pursue. For instance, in the scenario of credulous inconsistency (lower-right), the desirable outcome is that citizens become less credulous of their government and replace it through the ballots (in the case of a democracy), or dissent (in the case of an authoritarian regime). In these cases, potential activities could be efforts that i) increase transparency of government’s poor performance (to reduce trust), and ii) facilitate individual and collective action (to sanction poor performance). If these assumptions hold true in practice and at scale, and whether they can be voluntarily induced, remains an empirical question.
Finally, by adding trustworthiness as a variable, we expand our focus beyond “the eyes of the [governed] beholder” to a relational dimension in which governmental actions play an important role. By doing so, we also become more wary of hasty assumptions such as “transparency leads to trust”, where low trust in government is perceived more as a function of the opacity of a government’s behavior, rather than of its behavior. Which brings me to my next point.
Pathways towards consistency
If we consider that individuals’ trust in their government should be proportional to the trustworthiness of that government, we should contemplate the factors contributing to such consistency. Two are particularly noteworthy here: education and transparency.
As highlighted by Armen Hakhverdian and Quinton Mayne, citizens with a higher level of education are more likely to i) identify practices that negatively affect the functioning of government institutions, and ii) be normatively troubled by these practices. For example, the authors show that education is negatively related to institutional trust in corrupt societies and positively related to institutional trust in clean societies. If we consider that integrity is a property of trustworthy governments, education acts as a force creating consistency and moving away from cynical or credulous inconsistency.
But how do citizens identify, in the first place, the government practices that affect the functioning of institutions? One way is through personal experience: for instance, when citizens are either victims or witnesses of abuse by state agents (e.g. bribery requests, police violence). However, the most important way in which citizens identify these practices is through publicly available information on governmental actions. This brings me to the issue of transparency: its instrumental value is not whether it leads to more trust in government, as is often advocated. Rather, its value lies in its capacity to help individuals calibrate their trust vis-à-vis their governments, either towards more, or less, trust.
Final notes: measurements and definitions
One practical issue when it comes to trust in government is the availability of data. The most recent World Values Survey (WVS) has data on trust in government institutions for only 50 countries, the Gallup Poll for 43 countries, and the widely publicized Edelman Trust Barometer Survey covers no more than 28 countries. And by any count, there are at least 193 countries in the world. While claims about a global crisis of trust abound, global data about trust in government is less abundant.[1]
And which data one uses also matters, as illustrated below. For instance, when using data from the WVS, the countries falling under each quadrant differ from the results presented earlier (from the World Economic Forum’s Executive Survey). In short, different surveys will offer different results, each of them with their own shortcomings in terms of sample size, accuracy, or both.[2] In short, claims about trust in government, particularly at the global level, should be made carefully. Bearing these considerations in mind, the analytical framework put forward remains useful. Whichever indicator of trust one finds more appropriate to use, the framework still helps highlight in which cases trust in government is consistent or not with measures of government trustworthiness. In a passage of his most recent book, Ethan Zuckerman refers to “a sweet spot between too much trust and too little.” This framework, I believe, provides clues on where that spot may be.
Figure 3: Government Effectiveness vs Trust in Government (WVS survey)
Source: author’s own based on Government Effectiveness Index (2020) and World Values Survey (Wave 7)
A final note regarding the definition of trustworthiness is important. Levi’s definition certainly covers most of the characteristics that one would attribute to a trustworthy government, such as keeping promises, fairness in decision-making and enforcement, and delivery of goods and services. But as Levi highlights, the relative fairness of processes involves “the norms of place and time,” which makes fairness a concept that is contextual and of an evolving nature. This adds layers of complexity and raises some interesting questions, two of which I would like to highlight here.
The first regards the procedural and substantive dimensions of fairness and trustworthiness. In recent decades, many high-income democracies have performed well in procedural terms, with free and fair elections and laws that are mostly clear, stable, and equally enforced. From a procedural angle, these governments are worthy of trust. Nevertheless, on substantive grounds, one could contend that the opposite is also true. Take, for example, the overwhelming evidence that in these same democracies, government responsiveness has been systematically biased towards the needs of the better-off. Add to that growing inequality and a shrinking provision of public goods and services, and it becomes difficult to advocate for trustworthiness calculations based solely on procedural attributes.
As a side note, precisely because of the factors described above, one could say that part of the political polarization we see within countries today can be attributed to a growing divide between segments of the population that favor either procedural or substantive aspects of trustworthiness. For instance, some segments may privilege fair elections, while others attach more importance to substantive matters, such as bridging the gap between the rich and poor. Going back to the proposed trust matrix, the question then becomes whether trustworthiness should be assessed on procedural or substantive grounds and, if the answer is “both”, how to conciliate them. While answering this question brings its own normative and methodological challenges, evading it may fail to capture nuances that underpin the relationship between trust in government and government trustworthiness.
The second question is much simpler, albeit one with procedural and substantive consequences. Shouldn’t a government’s trustworthiness also be measured by the extent to which it offers participatory avenues for citizens to express themselves, beyond regular elections? After all, considering that trust is a two-way street, if governments expect to be trusted, shouldn’t they also trust citizens to shape decisions that affect their lives?
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P.S.: I’m thankful for comments on earlier versions of this text from Amy Chamberlain, Jonathan Fox, Quinton Mayne, Jon Mellon, Hollie Russon Gilman, Paolo Spada and Tom Steinberg.
[1] On claims of a global crisis of trust see, for instance, here, here and here.
As the saying goes: “if you think technology is the solution, you don’t understand the problem”. The same goes for technologies for civic or public service purposes. In fact, many argue that a common trait of good professionals working in the digital space is their capacity to, whenever necessary, politely convince their counterparts that their new “app” idea sucks.
Over time, practitioners will develop their own intuition and tricks of the trade to assess what should be built or not, and if so, how. That knowledge is mostly shared within networks of practitioners, sometimes through a few humorous tips, such as: “If building a dashboard is the first thing a government official asks you, run for your life!”
But this kind of tacit knowledge remains mostly contained and fragmented across small groups, and it is rarely translated for the benefit of others in an accessible manner. And these others are often the ones who come up either with the ideas, the money to fund the ideas, or both.
This is why I’m particularly happy to see that Luke Jordan’s most recent work is finally out: Don’t Build It: A Guide For Practitioners In Civic Tech / Tech For Development. Currently at the MIT GOV/LAB, Luke is a seasoned technologist, also known for being very straight-to-the point when the issue is technology. Luke’s knowledge and communication style is nicely reflected through the content of the guide: dense, direct and sometimes amusing, as in the excerpt below:
Construct a monstrous building in the middle of nowhere and movies might be made about you; build a pointless app that no one uses and you will just need to cite a misleading metric in a donor report and no one will care. Conversely, construct a good building in a sensible place and no one will think it worthy of notice; build a not-terrible app that people use for longer than the launch press release circulates, and you will immediately be nominated to half a dozen “X under X” lists.
So, by far the best method is to adopt a simple principle: Don’t build it.
When someone says, “We should build some tech for that”, just say no. When an investor or donor says, “Why don’t you build some technology”, just say no. When you read another article or see another TEDx talk about someone pretending their app achieved something, while citing numbers that are both unverified and meaningless, and a voice inside says, “why don’t we also build technology”, just say no.
Does that mean that the rest of this guide is pointless? Hopefully so. But in reality, at some point some idea may gather such momentum or such force of conviction that the “do not build it” ethos will start to falter. At that point, ask these questions…
I will stop here. In short, this guide is a must-read for anybody designing, implementing, funding or just coming up with “disruptive” ideas on technologies for civic engagement and public service delivery.
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Ps.: The teaser for the guide, is itself priceless:
Open government’s uncertain effects and the Biden opportunity: what now?
A review of 10 years of open government research reveals: 1) “a transparency-driven focus”, 2) “methodological concerns”, and 3) [maybe not surprising] “the lack of empirical evidence regarding the effects of open government”. My take on this is that these findings are, somewhat, self-reinforcing.
First, the early focus on transparency by open government advocates, while ignoring the conditions under which transparency could lead to public goods, should be, in part, to blame. This is even more so if open government interventions insist on tactical, instead of strategicapproaches to accountability. Second, the fact that many of those engaging in open government efforts do not take into account the existing evidence doesn’t help in terms of designing appropriate reforms, nor in terms of calibrating expectations. Proof of this is the recurrent and mostly unsubstantiated spiel that “transparency leads to trust”, voiced by individuals and organizations who should have known better. Third, should there be any effects of open government reforms, these are hard to verify in a credible manner given that evaluations often suffer from methodological weaknesses, as indicated by the paper.
Finally, open government’s semantic extravaganza makes building critical mass all the more difficult. For example, I have my doubts over whether the paper would reach similar conclusions should it have expanded the review to open government practices that, in the literature, are not normally labeled as open government. This would be the case, for instance, of participatory budgeting (which has shown to improve service delivery and increase tax revenues), or strategic approaches to social accountability that present substantial results in terms of development outcomes.
In any case, the research findings are still troubling. The election of President Biden gives some extra oxygen to the open government agenda, and that is great news. But in a context where autocratization turns viral, making a dent in how governments operate will take less policy-based evidence searching and more evidence-based strategizing. That involves leveraging the existing evidence when it is available, and when it is not, the standard path applies: more research is needed.
Open Government Partnership and Justice
On another note, Joe Foti, from the Open Government Partnership (OGP), writes on the need to engage more lawyers, judges and advocates in order to increase the number of accountability-focused OGP commitments. I particularly like Joe’s ideas on bringing these actors together to identify where OGP commitments could be stronger, and how. This resonates with a number of cases I’ve come across in the past where the judiciary played a key role in ensuring that citizens’ voice also had teeth.
I also share Joe’s enthusiasm for the potential of a new generation of commitments that put forward initiatives such as specialized anti-corruption courts and anti-SLAPP provisions. Having said this, the judiciary itself needs to be open, independent and capable. In most countries that I’ve worked in, a good part of open government reforms fail precisely because of a dysfunctional judiciary system.
Diversity, collective intelligence and deliberative democracy
Part of the justification for models of deliberative democracy is their epistemic quality, that is, large and diverse crowds are smarter than the (elected or selected) few. A good part of this argument finds its empirical basis in the fantastic work by Scott Page.
But that’s not all. We know, for instance, that gender diversity on corporate boards improves firms’ performance, ethnic diversity produces more impactful scientific research, diverse groups are better at solving crimes, popular juries are less biased than professional judges, and politically diverse editorial teams produce higher-quality Wikipedia articles. Diversity also helps to explain classical Athens’ striking superiority vis-à-vis other city-states of its time, due to the capacity of its democratic system to leverage the dispersed knowledge of its citizens through sortition.
Now, a Nature article, “Algorithmic and human prediction of success in human collaboration from visual features”, presents new evidence of the power of diversity in problem-solving tasks. In the paper, the authors examine the patterns of group success in Escape The Room, an adventure game in which a group attempts to escape a maze by collectively solving a series of puzzles. The authors find that groups that are larger, older and more gender diverse are significantly more likely to escape. But there’s an exception to that: more age diverse groups are less likely to escape. Intriguing isn’t it?
Deliberative processes online: rough review of the evidence
As the pandemic pushes more deliberative exercises online, researchers and practitioners start to take more seriously the question of how effective online deliberation can be when compared to in-person processes. Surprisingly, there are very few empirical studies comparing the two methods.
But a quick run through the literature offers some interesting insights. For instance, an online 2004 deliberative poll on U.S. foreign policy, and a traditional face-to-face deliberative poll conducted in parallel, presented remarkably similar results. A 2007 experiment comparing online and face-to-face deliberation found that both approaches can increase participants’ issue knowledge, political efficacy, and willingness to participate in politics. A similar comparison from 2009 looking at deliberation over the construction of a power plant in Finland found considerable resemblance in the outcomes of online and face-to-face processes. A study published in 2012 on waste treatment in France found that, compared to the offline process, online deliberation was more likely to: i) increase women’s interventions, ii) promote the justification or arguments, and iii) be oriented towards the common good (although in this case the processes were not similar in design).
The external validity of these findings, however encouraging they may be, remains an empirical question. Particularly given that since these studies were conducted the technology used to support deliberations has in many cases changed (e.g. from written to “zoomified” deliberations). Anyhow, kudos should go to the researchers who started engaging with the subject well over a decade ago: if that work was a niche subject then, their importance now is blatantly obvious.
(BTW, on a related issue, here’s a fascinating 2021 experiment examining whether online juries can make consistent, repeatable decisions: interestingly, deliberating groups are much more consistent than non-deliberating groups)
Fixing the Internet?
Anne Applebaum and Peter Pomerantsev published a great article in The Atlantic on the challenges to democracy by an Internet model that fuels disinformation and polarization, presenting alternative paths to address this. I was thankful for the opportunity to make a modest contribution to such a nice piece.
Of course, that doesn’t mean we shouldn’t be concerned about the effects of the Internet in politics. For instance, a new study in the American Political Science Review finds that radical right parties benefit more than any other parties from malicious bots on social media.
Open democracy
2021 continues to be a good year for the proponents of deliberative democracy, with growing coverage of the subject in the mainstream media, in part fueled by the recent launch of Helène Landemore’s great book “Open Democracy.” Looking for something to listen to? Look no further and listen to this interview by Ezra Klein with Helène.
A dialogue among giants
The recording of the roundtable Contours of Participatory Democracy in the 21st Century is now available. The conversation between Jane Mansbridge, Mark Warren and Cristina Lafont can be found here.
Democracy and design thinking
Speaking of giants, the new book by Michael Saward “Democratic Design”, is finally out. I’m a big fan of Michael’s work, so my recommendation may be biased. In this new book Michael brings design thinking together with democratic theory and practice. If the design of democratic institutions is one of your topics, you should definitely check it out!
Civic Tech
I was thrilled to have the opportunity to deliver a lecture at the Center for Collective Learning – Artificial and Natural Intelligence Institute. My presentation, Civic Technologies: Past, Present and Future, can be found here.
Scholar articles:
And finally, for those who really want to geek-out, a list of 15 academic articles I enjoyed reading:
Modern Grantmaking: That’s the title of a new book by Gemma Bull and Tom Steinberg. I had the privilege of reading snippets of this, and I can already recommend it not only to those working with grantmaking, but also pretty much anyone working in the international development space.
Lectures: The Center for Collective Learning has a fantastic line-up of lectures open to the public. Find out more here.
Learning from Togo: While unemployment benefits websites were crashing in the US, the Togolese government showed how to leverage mobile money and satellite data to effectively get cash into the hands of those who need it the most
Nudging the nudgers: British MPs are criticising academics for sending them fictitious emails for research. I wonder if part of their outrage is not just about the emails, but about what the study could reveal in terms of their actual responsiveness to different constituencies.
DataViz: Bringing data visualization to physical/offline spaces has been an obsession of mine for quite a while. I was happy to come across this project while doing some research for a presentation
DemocracySpot 