The Importance of Co-Designing Questions: 10 Lessons from Inquiry-Driven Grantmaking


Article by Hannah Chafetz and Stefaan Verhulst: “How can a question-based approach to philanthropy enable better learning and deeper evaluation across both sides of the partnership and help make progress towards long-term systemic change? That’s what Siegel Family Endowment (Siegel), a family foundation based in New York City, sought to answer by creating an Inquiry-Driven Grantmaking approach

While many philanthropies continue to follow traditional practices that focus on achieving a set of strategic objectives, Siegel employs an inquiry-driven approach, which focuses on answering questions that can accelerate insights and iteration across the systems they seek to change. By framing their goal as “learning” rather than an “outcome” or “metric,” they aim to generate knowledge that can be shared across the whole field and unlock impact beyond the work on individual grants. 

The Siegel approach centers on co-designing and iteratively refining questions with grantees to address evolving strategic priorities, using rapid iteration and stakeholder engagement to generate insights that inform both grantee efforts and the foundation’s decision-making.

Their approach was piloted in 2020, and refined and operationalized the years that followed. As of 2024, it was applied across the vast majority of their grantmaking portfolio. Laura Maher, Chief of Staff and Director of External Engagement at Siegel Family Endowment, notes: “Before our Inquiry-Driven Grantmaking approach we spent roughly 90% of our time on the grant writing process and 10% checking in with grantees, and now that’s balancing out more.”

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Image of the Inquiry-Driven Grantmaking Process from the Siegel Family Endowment

Earlier this year, the DATA4Philanthropy team conducted two in-depth discussions with Siegel’s Knowledge and Impact team to discuss their Inquiry-Driven Grantmaking approach and what they learned thus far from applying their new methodology. While the Siegel team notes that there is still much to be learned, there are several takeaways that can be applied to others looking to initiate a questions-led approach. 

Below we provide 10 emerging lessons from these discussions…(More)”.

A World of Unintended Consequences


Essay by Edward Tenner: “One of the great, underappreciated facts about our technology-driven age is that unintended consequences tend to outnumber intended ones. As much as we would like to believe that we are in control, scholars who have studied catastrophic failures have shown that humility is ultimately the only justifiable attitude…

Here’s a story about a revolution that never happened. Nearly 90 years ago, a 26-year-old newly credentialed Harvard sociology PhD and future American Philosophical Society member, Robert K. Merton, published a paper in the American Sociological Review that would become one of the most frequently cited in his discipline: “The Unanticipated Consequences of Purposive Social Action.”While the language of the paper was modest, it offered an obvious but revolutionary insight: many or most phenomena in the social world are unintended – for better or worse. Today, even management gurus like Tom Peters acknowledge that, “Unintended consequences outnumber intended consequences. … Strategies rarely unfold as we imagined. Intended consequences are rare.”

Merton had promised a monograph on the history and analysis of the problem, with its “vast scope and manifold implications.” Somewhere along the way, however, he abandoned the project, perhaps because it risked becoming a book about everything. Moreover, his apparent retreat may have discouraged other social scientists from attempting it, revealing one of the paradoxes of the subject’s study: because it is so universal and important, it may be best suited for case studies rather than grand theories.

Ironically, while unintentionality-centered analysis might have produced a Copernican revolution in social science, it is more likely that it would have unleashed adverse unintended consequences for any scholar attempting it – just as Thomas Kuhn’s idea of scientific paradigms embroiled him in decades of controversies. Besides, there are also ideological barriers to the study of unintended consequences. For every enthusiast there seems to be a hater, and dwelling on the unintended consequences of an opponent’s policies invites retaliation in kind.

This was economist Albert O. Hirschman’s point in his own critique of the theme. Hirschman himself had formidable credentials as a student of unintended consequences. One of his most celebrated and controversial ideas, the “hiding hand,” was a spin-off of Adam Smith’s famous metaphor for the market (the invisible hand). In Development Projects Observed, Hirschman noted that many successful programs might never have been launched had all the difficulties been known; but once a commitment was made, human ingenuity prevailed, and new and unforeseen solutions were found. The Sydney Opera House, for example, exceeded its budget by 1,300%, but it turned out to be a bargain once it became Australia’s unofficial icon…(More)”

The world at our fingertips, just out of reach: the algorithmic age of AI


Article by Soumi Banerjee: “Artificial intelligence (AI) has made global movements, testimonies, and critiques seem just a swipe away. The digital realm, powered by machine learning and algorithmic recommendation systems, offers an abundance of visual, textual, and auditory information. With a few swipes or keystrokes, the unbounded world lies open before us. Yet this ‘openness’ conceals a fundamental paradox: the distinction between availability and accessibility.

What is technically available is not always epistemically accessible. What appears global is often algorithmically curated. And what is served to users under the guise of choice frequently reflects the imperatives of engagement, profit, and emotional resonance over critical understanding or cognitive expansion.

The transformative potential of AI in democratising access to information comes with risks. Algorithmic enclosure and content curation can deepen epistemic inequality, particularly for the youth, whose digital fluency often masks a lack of epistemic literacy. What we need is algorithmic transparency, civic education in media literacy, and inclusive knowledge formats…(More)”.

Building Community-Centered AI Collaborations


Article by Michelle Flores Vryn and Meena Das: “AI can only boost the under-resourced nonprofit world if we design it to serve the communities we care about. But as nonprofits consider how to incorporate AI into their work, many look to expertise from tech sector, expecting tools and implementation advice as well as ethical guidance. Yet when mission-driven entities—with a strong focus on people, communities, and equity—partner solely with tech companies, they may encounter a variety of obstacles, such as:

  1. Limited understanding of community needs: Sector-specific knowledge is essential for aligning AI with nonprofit missions, something many tech companies lack.
  2. Bias in AI models: Without diverse input, AI models may exacerbate biases or misrepresent the communities that nonprofits serve.
  3. Resource constraints: Tech solutions often presume budgets or capacity beyond what nonprofits can bring to bear, creating a reliance on tools that fit the nonprofit context.

We need creative, diverse collaborations across various fields to ensure that technology is deployed in ways that align with nonprofit values, build trust, and serve the greater good. Seeking partners outside of the tech world helps nonprofits develop AI solutions that are context-aware, equitable, and resource-sensitive. Most importantly, nonprofit practitioners must deeply consider our ideal future state: What does an AI-empowered nonprofit sector look like when it truly centers human well-being, community agency, and ethical technology?

Imagining this future means not just reacting to emerging technology but proactively shaping its trajectory. Instead of simply adapting to AI’s capabilities, nonprofits should ask:

  • What problems do we truly need AI to solve?
  • Whose voices must be centered in AI decision-making?
  • How do we ensure AI remains a tool for empowerment rather than control?..(More)”.

Policy Implications of DeepSeek AI’s Talent Base


Brief by Amy Zegart and Emerson Johnston: “Chinese startup DeepSeek’s highly capable R1 and V3 models challenged prevailing beliefs about the United States’ advantage in AI innovation, but public debate focused more on the company’s training data and computing power than human talent. We analyzed data on the 223 authors listed on DeepSeek’s five foundational technical research papers, including information on their research output, citations, and institutional affiliations, to identify notable talent patterns. Nearly all of DeepSeek’s researchers were educated or trained in China, and more than half never left China for schooling or work. Of the quarter or so that did gain some experience in the United States, most returned to China to work on AI development there. These findings challenge the core assumption that the United States holds a natural AI talent lead. Policymakers need to reinvest in competing to attract and retain the world’s best AI talent while bolstering STEM education to maintain competitiveness…(More)”.

Interoperability and Openness Between Different Governance Models: The Dynamics of Mastodon/Threads and Wikipedia/Google


Article by Aline Blankertz & Svea Windwehr: “Governments, businesses and civil society representatives, among others, call for “alternatives” to compete with and possibly replace big tech platforms. These alternatives are usually characterized by different governance approaches like being not-for-profit, open, free, decentralized and/or community-based. We find that strengthening alternative governance models needs to account for the dynamic effects of operating in a digital ecosystem shaped by ad-driven platforms. Specifically, we explore in this article: 1) how interoperability between the microblogging platforms Threads (by Meta) and Mastodon (a not-for-profit service running on a federated open-source protocol) may foster competition, but also create a risk of converging governance in terms of e.g. content moderation and privacy practices; 2) how openness of the online encyclopedia Wikipedia allows Google Search to appropriate most of the value created by their vertical interaction and how the Wikimedia Foundation seeks to reduce that imbalance; 3) which types of interventions might be suitable to support alternatives without forcing them to emulate big tech governance, including asymmetric interoperability, digital taxes and regulatory restraints on commercial platforms…(More)”.

How Bad Is China’s Economy? The Data Needed to Answer Is Vanishing


Article by Rebecca Feng and Jason Douglas: “Not long ago, anyone could comb through a wide range of official data from China. Then it started to disappear. 

Land sales measures, foreign investment data and unemployment indicators have gone dark in recent years. Data on cremations and a business confidence index have been cut off. Even official soy sauce production reports are gone.

In all, Chinese officials have stopped publishing hundreds of data points once used by researchers and investors, according to a Wall Street Journal analysis. 

In most cases, Chinese authorities haven’t given any reason for ending or withholding data. But the missing numbers have come as the world’s second biggest economy has stumbled under the weight of excessive debt, a crumbling real-estate market and other troubles—spurring heavy-handed efforts by authorities to control the narrative.China’s National Bureau of Statistics stopped publishing some numbers related to unemployment in urban areas in recent years. After an anonymous user on the bureau’s website asked why one of those data points had disappeared, the bureau said only that the ministry that provided it stopped sharing the data.

The disappearing data have made it harder for people to know what’s going on in China at a pivotal time, with the trade war between Washington and Beijing expected to hit China hard and weaken global growth. Plunging trade with the U.S. has already led to production shutdowns and job cuts.

Getting a true read on China’s growth has always been tricky. Many economists have long questioned the reliability of China’s headline gross domestic product data, and concerns have intensified recently. Official figures put GDP growth at 5% last year and 5.2% in 2023, but some have estimated that Beijing overstated its numbers by as much as 2 to 3 percentage points. 

To get what they consider to be more realistic assessments of China’s growth, economists have turned to alternative sources such as movie box office revenues, satellite data on the intensity of nighttime lights, the operating rates of cement factories and electricity generation by major power companies. Some parse location data from mapping services run by private companies such as Chinese tech giant Baidu to gauge business activity. 

One economist said he has been assessing the health of China’s services sector by counting news stories about owners of gyms and beauty salons who abruptly close up and skip town with users’ membership fees…(More)”.

Governing in the Age of AI: Reimagining Local Government


Report by the Tony Blair Institute for Global Change: “…The limits of the existing operating model have been reached. Starved of resources by cuts inflicted by previous governments over the past 15 years, many councils are on the verge of bankruptcy even though local taxes are at their highest level. Residents wait too long for care, too long for planning applications and too long for benefits; many people never receive what they are entitled to. Public satisfaction with local services is sliding.

Today, however, there are new tools – enabled by artificial intelligence – that would allow councils to tackle these challenges. The day-to-day tasks of local government, whether related to the delivery of public services or planning for the local area, can all be performed faster, better and cheaper with the use of AI – a true transformation not unlike the one seen a century ago.

These tools would allow councils to overturn an operating model that is bureaucratic, labour-intensive and unresponsive to need. AI could release staff from repetitive tasks and relieve an overburdened and demotivated workforce. It could help citizens navigate the labyrinth of institutions, webpages and forms with greater ease and convenience. It could support councils to make better long-term decisions to drive economic growth, without which the resource pressure will only continue to build…(More)”.

Nonprofit AI: A Comprehensive Guide to Implementing Artificial Intelligence for Social Good


Book by Nathan Chappell and Scott Rosenkrans: “…an insightful and practical overview of how purpose-driven organizations can use AI to increase their impact and advance their missions. The authors offer an all-encompassing guide to understanding the promise and peril of implementing AI in the nonprofit sector, addressing both the theoretical and hands-on aspects of this necessary transformation.

The book provides you with case studies, practical tools, ethical frameworks and templates you can use to address the challenges of AI adoption – including ethical limitations – head-on. It draws on the authors’ thirty years of combined experience in the nonprofit industry to help you equip your nonprofit stakeholders with the knowledge and tools they need to successfully navigate the AI revolution.

You’ll also find:

  • Innovative and proven approaches to responsible and beneficial AI implementation taken by real-world organizations that will inspire and guide you as you move forward
  • Strategic planning, project management, and data governance templates and resources you can use immediately in your own nonprofit
  • Information on available AI training programs and resources to build AI fluency and capacity within nonprofit organizations.
  • Best practices for ensuring AI systems are transparent, accountable, and aligned with the mission and values of nonprofit organizations…(More)”.

Co-Designing AI Systems with Value-Sensitive Citizen Science


Paper by Sachit Mahajan and Dirk Helbing: “As artificial intelligence (AI) systems increasingly shape everyday life, integrating diverse community values into their development becomes both an ethical imperative and a practical necessity. This paper introduces Value Sensitive Citizen Science (VSCS), a systematic framework combining Value Sensitive Design (VSD) principles with citizen science methods to foster meaningful public participation in AI. Addressing critical gaps in existing approaches, VSCS integrates culturally grounded participatory methods and structured cognitive scaffolding through the Participatory Value-Cognition Taxonomy (PVCT). Through iterative value-sensitive participation cycles guided by an extended scenario logic (What-if, If-then, Then-what, What-now), community members act as genuine coresearchers-identifying, translating, and operationalizing local values into concrete technical requirements. The framework also institutionalizes governance structures for ongoing oversight, adaptability, and accountability across the AI lifecycle. By explicitly bridging participatory design with algorithmic accountability, VSCS ensures that AI systems reflect evolving community priorities rather than reinforcing top-down or monocultural perspectives. Critical discussions highlight VSCS’s practical implications, addressing challenges such as power dynamics, scalability, and epistemic justice. The paper concludes by outlining actionable strategies for policymakers and practitioners, alongside future research directions aimed at advancing participatory, value-driven AI development across diverse technical and sociocultural contexts…(More)”.