Category Archives for algorithm

Unwritten 2025

In a discussion with a government official last week, she made a point that stuck with me: “Every time we discuss AI readiness,” she said, “someone tells us to wait, or to get something else done before trying it. But waiting is a decision that may cost us in the future.”

She’s right. The technology sector has mastered the art of sophisticated hand-wringing. In AI discussions, over and over again, the same cautionary refrain echoes: “We don’t know where this technology is going.” It sounds thoughtful. It feels responsible. But increasingly, I’m convinced it’s neither.

Consider how differently we approached other transformative technologies. When my colleagues and I started experimentation with mobile phones, Internet, and voice recognition over two decades ago for participatory processes, we didn’t have a crystal ball. We couldn’t have predicted cryptocurrency, TikTok, or the weaponization of social media. What we did have was a vision of the democracy we wanted to build, one where technology served citizens, not the other way around.

The results of those who have been purposefully designing technology for the public good are far from perfect, but they are revealing. While social media algorithms were amplifying political divisions in the US and Myanmar, in Taiwan technology was used for large scale consensus building. While Cambridge Analytica was mining personal data, Estonian citizens were using secure digital IDs to access public services and to conveniently vote from their homes. The difference isn’t technological sophistication – it is purpose and values.

I see the same pattern repeating with AI. In India, OpenNyAI (‘Open AI for Justice’) isn’t waiting for perfect models to explore how AI can improve access to justice. In Africa, Viamo isn’t waiting for universal internet access to leverage AI, delivering vital information to citizens through simple mobile phones without internet.

This isn’t an argument for reckless adoption – ensuring that the best guardrails available are in place must be a constant pursuit. But there’s a world of difference between thoughtful experimentation and perpetual hesitation. When we say “we don’t know where this technology is going,” we’re often abdicating our responsibility to shape its direction. It’s a comfortable excuse that mainly serves those who benefit from the status quo. That is reckless.

The future of AI isn’t a set destination we discover with time. The question isn’t whether we can predict it perfectly, but whether we’re willing to shape it at all.

Being wrong is part of the job. 

Waiting for perfect clarity is a luxury we can’t afford. But that shouldn’t mean falling prey to solutionism. This week alone, I came across one pitch promising to solve wealth inequality with blockchain-powered AI (whatever that means) and another claiming to democratize healthcare with an empathy-enhanced chatbot. Technology won’t bend the arc of history on its own – that’s still on us. 

But we can choose to stay curious, to keep questioning our assumptions, and to build technology that leaves room for human judgment, trial, and error. The future isn’t written in binary. It’s written in the messy, imperfect choices we will all make while navigating uncertainty.

Voices in the Code: Citizen Participation for Better Algorithms

Image by mohamed Hassan from Pixabay

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.