Artificial Intelligence and the City

Curated on Posted on by Stefaan Verhulst

Book edited by Federico Cugurullo, Federico Caprotti, Matthew Cook, Andrew Karvonen, Pauline McGuirk, and Simon Marvin: “This book explores in theory and practice how artificial intelligence (AI) intersects with and alters the city. Drawing upon a range of urban disciplines and case studies, the chapters reveal the multitude of repercussions that AI is having on urban society, urban infrastructure, urban governance, urban planning and urban sustainability.

Contributors also examine how the city, far from being a passive recipient of new technologies, is influencing and reframing AI through subtle processes of co-constitution. The book advances three main contributions and arguments:

  • First, it provides empirical evidence of the emergence of a post-smart trajectory for cities in which new material and decision-making capabilities are being assembled through multiple AIs.
  • Second, it stresses the importance of understanding the mutually constitutive relations between the new experiences enabled by AI technology and the urban context.
  • Third, it engages with the concepts required to clarify the opaque relations that exist between AI and the city, as well as how to make sense of these relations from a theoretical perspective…(More)”.

Steering Responsible AI: A Case for Algorithmic Pluralism

Curated on Posted on by Stefaan Verhulst

Paper by Stefaan G. Verhulst: “In this paper, I examine questions surrounding AI neutrality through the prism of existing literature and scholarship about mediation and media pluralism. Such traditions, I argue, provide a valuable theoretical framework for how we should approach the (likely) impending era of AI mediation. In particular, I suggest examining further the notion of algorithmic pluralism. Contrasting this notion to the dominant idea of algorithmic transparency, I seek to describe what algorithmic pluralism may be, and present both its opportunities and challenges. Implemented thoughtfully and responsibly, I argue, Algorithmic or AI pluralism has the potential to sustain the diversity, multiplicity, and inclusiveness that are so vital to democracy…(More)”.

Generative AI and Policymaking for the New Frontier

Curated on Posted on by Stefaan Verhulst

Essay by Beth Noveck: “…Embracing the same responsible experimentation approach taken in Boston and New Jersey and expanding on the examples in those interim policies, this November the state of California issued an executive order and a lengthy but clearly written report, enumerating potential benefits from the use of generative AI.

These include:

  1. Sentiment Analysis — Using generative AI (GenAI) to analyze public feedback on state policies and services.
  2. Summarizing Meetings — GenAI can find the key topics, conclusions, action items and insights.
  3. Improving Benefits Uptake — AI can help identify public program participants who would benefit from additional outreach. GenAI can also identify groups that are disproportionately not accessing services.
  4. Translation — Generative AI can help translate government forms and websites into multiple languages.
  5. Accessibility — GenAI can be used to translate materials, especially educational materials into formats like audio, large print or Braille or to add captions.
  6. Cybersecurity —GenAI models can analyze data to detect and respond to cyber attacks faster and safeguard public infrastructure.
  7. Updating Legacy Technology — Because it can analyze and generate computer code, generative AI can accelerate the upgrading of old computer systems.
  8. Digitizing Services — GenAI can help speed up the creation of new technology. And with GenAI, anyone can create computer code, enabling even nonprogrammers to develop websites and software.
  9. Optimizing Routing — GenAI can analyze traffic patterns and ride requests to improve efficiency of state-managed transportation fleets, such as buses, waste collection trucks or maintenance vehicles.
  10. Improving Sustainability — GenAI can be applied to optimize resource allocation and enhance operational efficiency. GenAI simulation tools could, for example, “model the carbon footprint, water usage and other environmental impacts of major infrastructure projects.”

Because generative AI tools can both create and analyze content, these 10 are just a small subset of the many potential applications of generative AI in governing…(More)”.

Urban Artificial Intelligence: From Real-world Observations to a Paradigm-Shifting Concept

Curated on Posted on by Stefaan Verhulst

Blog by Hubert Beroche: “Cities are facing unprecedented challenges. The figures are well known: while occupying only 2% of the earth’s surface, urban settlements host more than 50% of the global population and are responsible for 70% of greenhouse emissions. While concentrating most capital and human wealth, they are also places of systemic inequalities (Nelson, 2023), exacerbating and materializing social imbalances. In the meantime, cities have fewer and fewer resources to face those tensions. Increasing environmental constraints, combined with shrinking public budgets, are putting pressure on cities’ capacities. More than ever, urban stakeholders have to do more with less.

In this context, Artificial Intelligence has usually been seen as a much-welcomed technology. This technology can be defined as machines’ ability to perform cognitive functions, mainly through learning algorithms since 2012. First embedded in heavy top-down Smart City projects, AI applications in cities have gradually proliferated under the impetus of various stakeholders. Today’s cities are home to numerous AIs, owned and used by multiple stakeholders to serve different, sometimes divergent, interests.

The diversity of urban AIs in cities is well illustrated in our project co-produced with Cornell Tech: “The Future of Urban AI”. This graph represents different urban AI trends based on The Future of UrbanTech Horizon Scan. Each colored dot represents a major urban tech/urban AI trend, with its ramifications. Some of these trends are opposed but still cohabiting (eg “Dark Plans” and “New Screen Deal”)…(More)”.

Shaping the Future: Indigenous Voices Reshaping Artificial Intelligence in Latin America

Curated on Posted on by Stefaan Verhulst

Blog by Enzo Maria Le Fevre Cervini: “In a groundbreaking move toward inclusivity and respect for diversity, a comprehensive report “Inteligencia artificial centrada en los pueblos indígenas: perspectivas desde América Latina y el Caribe” authored by Cristina Martinez and Luz Elena Gonzalez has been released by UNESCO, outlining the pivotal role of Indigenous perspectives in shaping the trajectory of Artificial Intelligence (AI) in Latin America. The report, a collaborative effort involving Indigenous communities, researchers, and various stakeholders, emphasizes the need for a fundamental shift in the development of AI technologies, ensuring they align with the values, needs, and priorities of Indigenous peoples.

The core theme of the report revolves around the idea that for AI to be truly respectful of human rights, it must incorporate the perspectives of Indigenous communities in Latin America, the Caribbean, and beyond. Recognizing the UNESCO Recommendation on the Ethics of Artificial Intelligence, the report highlights the urgency of developing a framework of shared responsibility among different actors, urging them to leverage their influence for the collective public interest.

While acknowledging the immense potential of AI in preserving Indigenous identities, conserving cultural heritage, and revitalizing languages, the report notes a critical gap. Many initiatives are often conceived externally, prompting a call to reevaluate these projects to ensure Indigenous leadership, development, and implementation…(More)”.

A Manifesto on Enforcing Law in the Age of ‘Artificial Intelligence’

Curated on Posted on by Stefaan Verhulst

Manifesto by the Transatlantic Reflection Group on Democracy and the Rule of Law in the Age of ‘Artificial Intelligence’: “… calls for the effective and legitimate enforcement of laws concerning AI systems. In doing so, we recognise the important and complementary role of standards and compliance practices. Whereas the first manifesto focused on the relationship between democratic law-making and technology, this second manifesto shifts focus from the design of law in the age of AI to the enforcement of law. Concretely, we offer 10 recommendations for addressing the key enforcement challenges shared across transatlantic stakeholders. We call on those who support these recommendations to sign this manifesto…(More)”.

Using AI to support people with disability in the labour market

Curated on Posted on by Stefaan Verhulst

OECD Report: “People with disability face persisting difficulties in the labour market. There are concerns that AI, if managed poorly, could further exacerbate these challenges. Yet, AI also has the potential to create more inclusive and accommodating environments and might help remove some of the barriers faced by people with disability in the labour market. Building on interviews with more than 70 stakeholders, this report explores the potential of AI to foster employment for people with disability, accounting for both the transformative possibilities of AI-powered solutions and the risks attached to the increased use of AI for people with disability. It also identifies obstacles hindering the use of AI and discusses what governments could do to avoid the risks and seize the opportunities of using AI to support people with disability in the labour market…(More)”.

AI and Democracy’s Digital Identity Crisis

Curated on Posted on by Stefaan Verhulst

Paper by Shrey Jain, Connor Spelliscy, Samuel Vance-Law and Scott Moore: “AI-enabled tools have become sophisticated enough to allow a small number of individuals to run disinformation campaigns of an unprecedented scale. Privacy-preserving identity attestations can drastically reduce instances of impersonation and make disinformation easy to identify and potentially hinder. By understanding how identity attestations are positioned across the spectrum of decentralization, we can gain a better understanding of the costs and benefits of various attestations. In this paper, we discuss attestation types, including governmental, biometric, federated, and web of trust-based, and include examples such as e-Estonia, China’s social credit system, Worldcoin, OAuth, X (formerly Twitter), Gitcoin Passport, and EAS. We believe that the most resilient systems create an identity that evolves and is connected to a network of similarly evolving identities that verify one another. In this type of system, each entity contributes its respective credibility to the attestation process, creating a larger, more comprehensive set of attestations. We believe these systems could be the best approach to authenticating identity and protecting against some of the threats to democracy that AI can pose in the hands of malicious actors. However, governments will likely attempt to mitigate these risks by implementing centralized identity authentication systems; these centralized systems could themselves pose risks to the democratic processes they are built to defend. We therefore recommend that policymakers support the development of standards-setting organizations for identity, provide legal clarity for builders of decentralized tooling, and fund research critical to effective identity authentication systems…(More)”.

Transmission Versus Truth, Imitation Versus Innovation: What Children Can Do That Large Language and Language-and-Vision Models Cannot (Yet)

Curated on Posted on by Stefaan Verhulst

Paper by Eunice Yiu, Eliza Kosoy, and Alison Gopnik: “Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents. We present an alternative perspective. First, we argue that these artificial intelligence (AI) models are cultural technologies that enhance cultural transmission and are efficient and powerful imitation engines. Second, we explore what AI models can tell us about imitation and innovation by testing whether they can be used to discover new tools and novel causal structures and contrasting their responses with those of human children. Our work serves as a first step in determining which particular representations and competences, as well as which kinds of knowledge or skills, can be derived from particular learning techniques and data. In particular, we explore which kinds of cognitive capacities can be enabled by statistical analysis of large-scale linguistic data. Critically, our findings suggest that machines may need more than large-scale language and image data to allow the kinds of innovation that a small child can produce…(More)”.

Can AI solve medical mysteries? It’s worth finding out

Curated on Posted on by Stefaan Verhulst

Article by Bina Venkataraman: “Since finding a primary care doctor these days takes longer than finding a decent used car, it’s little wonder that people turn to Google to probe what ails them. Be skeptical of anyone who claims to be above it. Though I was raised by scientists and routinely read medical journals out of curiosity, in recent months I’ve gone online to investigate causes of a lingering cough, ask how to get rid of wrist pain and look for ways to treat a bad jellyfish sting. (No, you don’t ask someone to urinate on it.)

Dabbling in self-diagnosis is becoming more robust now that people can go to chatbots powered by large language models scouring mountains of medical literature to yield answers in plain language — in multiple languages. What might an elevated inflammation marker in a blood test combined with pain in your left heel mean? The AI chatbots have some ideas. And researchers are finding that, when fed the right information, they’re often not wrong. Recently, one frustrated mother, whose son had seen 17 doctors for chronic pain, put his medical information into ChatGPT, which accurately suggested tethered cord syndrome — which then led a Michigan neurosurgeon to confirm an underlying diagnosis of spina bifida that could be helped by an operation.

The promise of this trend is that patients might be able to get to the bottom of mysterious ailments and undiagnosed illnesses by generating possible causes for their doctors to consider. The peril is that people may come to rely too much on these tools, trusting them more than medical professionals, and that our AI friends will fabricate medical evidence that misleads people about, say, the safety of vaccines or the benefits of bogus treatments. A question looming over the future of medicine is how to get the best of what artificial intelligence can offer us without the worst.

It’s in the diagnosis of rare diseases — which afflict an estimated 30 million Americans and hundreds of millions of people worldwide — that AI could almost certainly make things better. “Doctors are very good at dealing with the common things,” says Isaac Kohane, chair of the department of biomedical informatics at Harvard Medical School. “But there are literally thousands of diseases that most clinicians will have never seen or even have ever heard of.”..(More)”.