Paper by Devansh Saxena, et al: “Local and federal agencies are rapidly adopting AI systems to augment or automate critical decisions, efficiently use resources, and improve public service delivery. AI systems are being used to support tasks associated with urban planning, security, surveillance, energy and critical infrastructure, and support decisions that directly affect citizens and their ability to access essential services. Local governments act as the governance tier closest to citizens and must play a critical role in upholding democratic values and building community trust especially as it relates to smart city initiatives that seek to transform public services through the adoption of AI. Community-centered and participatory approaches have been central for ensuring the appropriate adoption of technology; however, AI innovation introduces new challenges in this context because participatory AI design methods require more robust formulation and face higher standards for implementation in the public sector compared to the private sector. This requires us to reassess traditional methods used in this space as well as develop new resources and methods. This workshop will explore emerging practices in participatory algorithm design – or the use of public participation and community engagement – in the scoping, design, adoption, and implementation of public sector algorithms…(More)”.
Combine AI with citizen science to fight poverty
Nature Editorial: “Of the myriad applications of artificial intelligence (AI), its use in humanitarian assistance is underappreciated. In 2020, during the COVID-19 pandemic, Togo’s government used AI tools to identify tens of thousands of households that needed money to buy food, as Nature reports in a News Feature this week. Typically, potential recipients of such payments would be identified when they apply for welfare schemes, or through household surveys of income and expenditure. But such surveys were not possible during the pandemic, and the authorities needed to find alternative means to help those in need. Researchers used machine learning to comb through satellite imagery of low-income areas and combined that knowledge with data from mobile-phone networks to find eligible recipients, who then received a regular payment through their phones. Using AI tools in this way was a game-changer for the country.Can AI help beat poverty? Researchers test ways to aid the poorest people
Now, with the pandemic over, researchers and policymakers are continuing to see how AI methods can be used in poverty alleviation. This needs comprehensive and accurate data on the state of poverty in households. For example, to be able to help individual families, authorities need to know about the quality of their housing, their children’s diets, their education and whether families’ basic health and medical needs are being met. This information is typically obtained from in-person surveys. However, researchers have seen a fall in response rates when collecting these data.
Missing data
Gathering survey-based data can be especially challenging in low- and middle-income countries (LMICs). In-person surveys are costly to do and often miss some of the most vulnerable, such as refugees, people living in informal housing or those who earn a living in the cash economy. Some people are reluctant to participate out of fear that there could be harmful consequences — deportation in the case of undocumented migrants, for instance. But unless their needs are identified, it is difficult to help them.Leveraging the collaborative power of AI and citizen science for sustainable development
Could AI offer a solution? The short answer is, yes, although with caveats. The Togo example shows how AI-informed approaches helped communities by combining knowledge of geographical areas of need with more-individual data from mobile phones. It’s a good example of how AI tools work well with granular, household-level data. Researchers are now homing in on a relatively untapped source for such information: data collected by citizen scientists, also known as community scientists. This idea deserves more attention and more funding.
Thanks to technologies such as smartphones, Wi-Fi and 4G, there has been an explosion of people in cities, towns and villages collecting, storing and analysing their own social and environmental data. In Ghana, for example, volunteer researchers are collecting data on marine litter along the coastline and contributing this knowledge to their country’s official statistics…(More)”.
Nonprofits, Stop Doing Needs Assessments.
Design for Social Impact: “Too many non-profits and funders still roll into communities with a clipboard and a mission to document everything “missing.”
Needs assessments have become a default tool for diagnosing deficits, reinforcing a saviour mentality where outsiders decide what’s broken and needs fixing.
I’ve sat in meetings where non-profits present lists of what communities lack:
- “Youth don’t have leadership skills”
- “Parents don’t value education”
- “Grassroots organisations don’t have capacity”
The subtext? “They need us.”
And because funding is tied to these narratives of scarcity, organisations learn to describe themselves in the language of need rather than strength—because that’s what gets funded…Now, I’m not saying that organisations or funders should never ask people what their needs are. The key issue is how needs assessments are framed and used. Too often, they use extractive “data” collection methodologies and reinforce top-down, deficit-based narratives, where communities are defined primarily by what they lack rather than what they bring.
Starting with what’s already working (asset mapping) and then identifying what’s needed to strengthen and expand those assets is different from leading with gaps, which can frame communities as passive recipients rather than active problem-solvers.
Arguably, a balanced synergy between assessing needs and asset mapping can be powerful—so long as the process centres on community agency, self-determination, and long-term sustainability rather than diagnosing problems for external intervention.
Also, asset-based mapping to me does not mean that you swoop in with the same clipboard and demand people document their strengths…(More)”.
How Innovation Ecosystems Foster Citizen Participation Using Emerging Technologies in Portugal, Spain and the Netherlands
OECD Report: “This report examines how actors in Portugal, Spain and the Netherlands interact and work together to contribute to the development of emerging technologies for citizen participation. Through in-depth research and analysis of actors’ motivations, experiences, challenges, and enablers in this nascent but promising field, this paper presents a unique cross-national perspective on innovation ecosystems for citizen participation using emerging technology. It includes lessons and concrete proposals for policymakers, innovators, and researchers seeking to develop technology-based citizen participation initiatives…(More)”.
Why these scientists devote time to editing and updating Wikipedia
Article by Christine Ro: “…A 2018 survey of more than 4,000 Wikipedians (as the site’s editors are called) found that 12% had a doctorate. Scientists made up one-third of the Wikimedia Foundation’s 16 trustees, according to Doronina.
Although Wikipedia is the best-known project under the Wikimedia umbrella, there are other ways for scientists to contribute besides editing Wikipedia pages. For example, an entomologist could upload photos of little-known insect species to Wikimedia Commons, a collection of images and other media. A computer scientist could add a self-published book to the digital textbook site Wikibooks. Or a linguist could explain etymology on the collaborative dictionary Wiktionary. All of these are open access, a key part of Wikimedia’s mission.
Although Wikipedia’s structure might seem daunting for new editors, there are parallels with academic documents.
For instance, Jess Wade, a physicist at Imperial College London, who focuses on creating and improving biographies of female scientists and scientists from low- and middle-income countries, says that the talk page, which is the behind-the-scenes portion of a Wikipedia page on which editors discuss how to improve it, is almost like the peer-review file of an academic paper…However, scientists have their own biases about aspects such as how to classify certain topics. This matters, Harrison says, because “Wikipedia is intended to be a general-purpose encyclopaedia instead of a scientific encyclopaedia.”
One example is a long-standing battle over Wikipedia pages on cryptids and folklore creatures such as Bigfoot. Labels such as ‘pseudoscience’ have angered cryptid enthusiasts and raised questions about different types of knowledge. One suggestion is for the pages to feature a disclaimer that says that a topic is not accepted by mainstream science.
Wade raises a point about resourcing, saying it’s especially difficult for the platform to retain academics who might be enthusiastic about editing Wikipedia initially, but then drop off. One reason is time. For full-time researchers, Wikipedia editing could be an activity best left to evenings, weekends and holidays…(More)”.
Public participation in policymaking: exploring and understanding impact
Report by the Scottish Government: “This research builds on that framework and seeks to explore how Scottish Government might better understand the impact of public participation on policy decision-making. As detailed above, there is a plethora of potential, and anticipated, benefits which may arise from increased citizen participation in policy decision-making, as well as lots of participatory activity already taking place across the organisation. Now is an opportune time to consider impact, to support the design and delivery of participatory engagements that are impactful and that are more likely to realise the benefits of public participation. Through a review of academic and grey literature along with stakeholder engagement, this study aims to answer the following questions:
- 1. How is impact conceptualised in literature related to public participation, and what are some practice examples?
- 2. How is impact conceptualised by stakeholders and what do they perceive as the current blockers, challenges or facilitators in a Scottish Government setting?
- 3. What evaluation tools or frameworks are used to evaluate the impact of public participation processes, and which ones might be applicable /usable in a Scottish Government setting?…(More)”.
Citizens’ assemblies in fragile and conflict-affected settings
Article by Nicole Curato, Lucy J Parry, and Melisa Ross: “Citizens’ assemblies have become a popular form of citizen engagement to address complex issues like climate change, electoral reform, and assisted dying. These assemblies bring together randomly selected citizens to learn about an issue, consider diverse perspectives, and develop collective recommendations. Growing evidence highlights their ability to depolarise views, enhance political efficacy, and rebuild trust in institutions. However, the story of citizens’ assemblies is more complicated on closer look. This demanding form of political participation is increasingly critiqued for its limited impact, susceptibility to elite influence, and rigid design features unsuitable to local contexts. These challenges are especially pronounced in fragile and conflict-affected settings, where trust is low, expectations for action are high, and local ownership is critical. Well-designed assemblies can foster civic trust and dialogue across difference, but poorly implemented ones risk exacerbating tensions.
This article offers a framework to examine citizens’ assemblies in fragile and conflict-affected settings, focusing on three dimensions: deliberative design, deliberative integrity, and deliberative sustainability. We apply this framework to cases in Bosnia and France to illustrate both the transformative potential and the challenges of citizens’ assemblies when held amidst or in the aftermath of political conflict. This article argues that citizens’ assemblies can be vital mechanisms to manage intractable conflict, provided they are designed with intentionality, administered deliberatively, and oriented towards sustainability…(More)”.
Artificial Intelligence for Participation
Policy Brief by the Brazil Centre of the University of Münster: “…provides an overview of current and potential applications of artificial intelligence (AI) technologies in the context of political participation and democratic governance processes in cities. Aimed primarily at public managers, the document also highlights critical issues to consider in the implementation of these technologies, and proposes an agenda for debate on the new state capabilities they require…(More)”.
The Nature and Dynamics of Collaboration
Book edited by Paul F. M. J. Verschure: “Human existence depends critically on how well diverse social, cultural and political groups can collaborate. Yet the phenomenon of collaboration itself is ill-defined and badly understood, and there is no straightforward formula for its successful realization. In The Nature and Dynamics of Collaboration, edited by Paul F. M. J. Verschure, experts from wide-ranging disciplines examine how human collaboration arises, breaks down, and potentially recovers. They explore the different contexts, boundary conditions, and drivers of collaboration to expand understanding of the underlying dynamic, multiscale processes in an effort to increase chances for ethical, sustainable, and productive collaboration in the future. This volume is accompanied by twenty-four podcasts, which provide insights from real-world examples…(More)”.
Leveraging Crowd Intelligence to Enhance Fairness and Accuracy in AI-powered Recruitment Decisions
Paper by Zhen-Song Chen and Zheng Ma: “Ensuring fair and accurate hiring outcomes is critical for both job seekers’ economic opportunities and organizational development. This study addresses the challenge of mitigating biases in AI-powered resume screening systems by leveraging crowd intelligence, thereby enhancing problem-solving efficiency and decision-making quality. We propose a novel counterfactual resume-annotation method based on a causal model to capture and correct biases from human resource (HR) representatives, providing robust ground truth data for supervised machine learning. The proposed model integrates multiple language embedding models and diverse HR-labeled data to train a cohort of resume-screening agents. By training 60 such agents with different models and data, we harness their crowd intelligence to optimize for three objectives: accuracy, fairness, and a balance of both. Furthermore, we develop a binary bias-detection model to visualize and analyze gender bias in both human and machine outputs. The results suggest that harnessing crowd intelligence using both accuracy and fairness objectives helps AI systems robustly output accurate and fair results. By contrast, a sole focus on accuracy may lead to severe fairness degradation, while, conversely, a sole focus on fairness leads to a relatively minor loss of accuracy. Our findings underscore the importance of balancing accuracy and fairness in AI-powered resume-screening systems to ensure equitable hiring outcomes and foster inclusive organizational development…(More)”