Repository of 80+ real-life examples of how to anticipate migration using innovative forecast and foresight methods is now LIVE!

Curated on Posted on by Stefaan Verhulst
Launch! Repository of 80+ real-life examples of how to anticipate migration using innovative forecast and foresight methods is now LIVE!

BD4M Announcement: “Today, we are excited to launch the Big Data For Migration Alliance (BD4M) Repository of Use Cases for Anticipating Migration Policy! The repository is a curated collection of real-world applications of anticipatory methods in migration policy. Here, policymakers, researchers, and practitioners can find a wealth of examples demonstrating how foresight, forecast and other anticipatory approaches are applied to anticipating migration for policy making. 

Migration policy is a multifaceted and constantly evolving field, shaped by a wide variety of factors such as economic conditions, geopolitical shifts or climate emergencies. Anticipatory methods are essential to help policymakers proactively respond to emerging trends and potential challenges. By using anticipatory tools, migration policy makers can draw from both quantitative and qualitative data to obtain valuable insights for their specific goals. The Big Data for Migration Alliance — a join effort of The GovLab, the International Organization for Migration and the European Union Joint Research Centre that seeks to improve the evidence base on migration and human mobility — recognizes the importance of the role of anticipatory tools and has worked on the creation of a repository of use cases that showcases the current use landscape of anticipatory tools in migration policy making around the world. This repository aims to provide policymakers, researchers and practitioners with applied examples that can inform their strategies and ultimately contribute to the improvement of migration policies around the world. 

As part of our work on exploring innovative anticipatory methods for migration policy, throughout the year we have published a Blog Series that delved into various aspects of the use of anticipatory methods, exploring their value and challenges, proposing a taxonomy, and exploring practical applications…(More)”.

The limits of state AI legislation

Curated on Posted on by Stefaan Verhulst

Article by Derek Robertson: “When it comes to regulating artificial intelligence, the action right now is in the states, not Washington.

State legislatures are often, like their counterparts in Europe, contrasted favorably with Congress — willing to take action where their politically paralyzed federal counterpart can’t, or won’t. Right now, every state except Alabama and Wyoming is considering some kind of AI legislation.

But simply acting doesn’t guarantee the best outcome. And today, two consumer advocates warn in POLITICO Magazine that most, if not all, state laws are overlooking crucial loopholes that could shield companies from liability when it comes to harm caused by AI decisions — or from simply being forced to disclose when it’s used in the first place.

Grace Gedye, an AI-focused policy analyst at Consumer Reports, and Matt Scherer, senior policy counsel at the Center for Democracy & Technology, write in an op-ed that while the use of AI systems by employers is screaming out for regulation, many of the efforts in the states are ineffectual at best.

Under the most important state laws now in consideration, they write, “Job applicants, patients, renters and consumers would still have a hard time finding out if discriminatory or error-prone AI was used to help make life-altering decisions about them.”

Transparency around how and when AI systems are deployed — whether in the public or private sector — is a key concern of the growing industry’s watchdogs. The Netherlands’ tax authority infamously immiserated tens of thousands of families by accusing them falsely of child care benefits fraud after an algorithm used to detect it went awry…

One issue: a series of jargon-filled loopholes in many bill texts that says the laws only cover systems “specifically developed” to be “controlling” or “substantial” factors in decision-making.

“Cutting through the jargon, this would mean that companies could completely evade the law simply by putting fine print at the bottom of their technical documentation or marketing materials saying that their product wasn’t designed to be the main reason for a decision and should only be used under human supervision,” they explain…(More)”

Potential competition impacts from the data asymmetry between Big Tech firms and firms in financial services

Curated on Posted on by Stefaan Verhulst

Report by the UK Financial Conduct Authority: “Big Tech firms in the UK and around the world have been, and continue to be, under active scrutiny by competition and regulatory authorities. This is because some of these large technology firms may have both the ability and the incentive to shape digital markets by protecting existing market power and extending it into new markets.
Concentration in some digital markets, and Big Tech firms’ key role, has been widely discussed, including in our DP22/05. This reflects both the characteristics of digital markets and the characteristics and behaviours of Big Tech firms themselves. Although Big Tech firms have different business models, common characteristics include their global scale and access to a large installed user base, rich data about their users, advanced data analytics and technology, influence over decision making and defaults, ecosystems of complementary products and strategic behaviours, including acquisition strategies.
Through our work, we aim to mitigate the risk of competition in retail financial markets evolving in a way that results in some Big Tech firms gaining entrenched market power, as seen in other sectors and jurisdictions, while enabling the potential competition benefits that come from Big Tech firms providing challenge to incumbent financial services firms…(More)”.

Murky Consent: An Approach to the Fictions of Consent in Privacy Law

Curated on Posted on by Stefaan Verhulst

Paper by Daniel J. Solove: “Consent plays a profound role in nearly all privacy laws. As Professor Heidi Hurd aptly said, consent works “moral magic” – it transforms things that would be illegal and immoral into lawful and legitimate activities. As to privacy, consent authorizes and legitimizes a wide range of data collection and processing.

There are generally two approaches to consent in privacy law. In the United States, the notice-and-choice approach predominates; organizations post a notice of their privacy practices and people are deemed to consent if they continue to do business with the organization or fail to opt out. In the European Union, the General Data Protection Regulation (GDPR) uses the express consent approach, where people must voluntarily and affirmatively consent.

Both approaches fail. The evidence of actual consent is non-existent under the notice-and-choice approach. Individuals are often pressured or manipulated, undermining the validity of their consent. The express consent approach also suffers from these problems – people are ill-equipped to decide about their privacy, and even experts cannot fully understand what algorithms will do with personal data. Express consent also is highly impractical; it inundates individuals with consent requests from thousands of organizations. Express consent cannot scale.

In this Article, I contend that most of the time, privacy consent is fictitious. Privacy law should take a new approach to consent that I call “murky consent.” Traditionally, consent has been binary – an on/off switch – but murky consent exists in the shadowy middle ground between full consent and no consent. Murky consent embraces the fact that consent in privacy is largely a set of fictions and is at best highly dubious….(More)”. See also: The Urgent Need to Reimagine Data Consent

The Crime Data Handbook

Curated on Posted on by Stefaan Verhulst

Book edited by Laura Huey and David Buil-Gil: “Crime research has grown substantially over the past decade, with a rise in evidence-informed approaches to criminal justice, statistics-driven decision-making and predictive analytics. The fuel that has driven this growth is data – and one of its most pressing challenges is the lack of research on the use and interpretation of data sources.

This accessible, engaging book closes that gap for researchers, practitioners and students. International researchers and crime analysts discuss the strengths, perils and opportunities of the data sources and tools now available and their best use in informing sound public policy and criminal justice practice…(More)”.

AI Is a Hall of Mirrors

Curated on Posted on by Stefaan Verhulst

Essay by Meghan Houser: “Here is the paradox… First: Everything is for you. TikTok’s signature page says it, and so, in their own way, do the recommendation engines of all social media. Streaming platforms triangulate your tastes, brand “engagements” solicit feedback for a better experience next time, Google Maps asks where you want to go, Siri and Alexa wait in limbo for reply. Dating apps present our most “compatible” matches. Sacrifices in personal data pay (at least some) dividends in closer tailoring. Our phones fit our palms like lovers’ hands. Consumer goods reach us in two days or less, or, if we prefer, our mobile orders are ready when we walk into our local franchise. Touchless, frictionless, we move toward perfect inertia, skimming engineered curves in the direction of our anticipated desires.

Second: Nothing is for you. That is, you specifically, you as an individual human person, with three dimensions and password-retrieval answers that actually mean something. We all know by now that “the algorithm,” that godlike personification, is fickle. Targeted ads follow you after you buy the product. Spotify thinks lullabies are your jam because for a couple weeks one put your child to sleep. Watch a political video, get invited down the primrose path to conspiracy. The truth of aggregation, of metadata, is that the for you of it all gets its power from modeling everyone who is not, in fact, you. You are typological, a predictable deviation from the mean. The “you” that your devices know is a shadow of where your data-peers have been. Worse, the “you” that your doctor, your insurance company, or your banker knows is a shadow of your demographic peers. And sometimes the model is arrayed against you. A 2016 ProPublica investigation found that if you are Black and coming up for sentencing before a judge who relies on a criminal sentencing algorithm, you are twice as likely to be mistakenly deemed at high risk for reoffending than your white counterpart….(More)”

Whoever you are, the algorithms’ for you promise at some point rings hollow. The simple math of automation is that the more the machines are there to talk to us, the less someone else will. Get told how important your call is to us, in endless perfect repetition. Prove you’re a person to Captcha, and (if you’re like me) sometimes fail. Post a comment on TikTok or YouTube knowing that it will be swallowed by its only likely reader, the optimizing feed.

Offline, the shadow of depersonalization follows. Physical spaces are atomized and standardized into what we have long been calling brick and mortar. QR, a language readable only to the machines, proliferates. The world becomes a little less legible. Want to order at this restaurant? You need your phone as translator, as intermediary, in this its newly native land…(More)”.

Automated Social Science: Language Models as Scientist and Subjects

Curated on Posted on by Stefaan Verhulst

Paper by Benjamin S. Manning, Kehang Zhu & John J. Horton: “We present an approach for automatically generating and testing, in silico, social scientific hypotheses. This automation is made possible by recent advances in large language models (LLM), but the key feature of the approach is the use of structural causal models. Structural causal models provide a language to state hypotheses, a blueprint for constructing LLM-based agents, an experimental design, and a plan for data analysis. The fitted structural causal model becomes an object available for prediction or the planning of follow-on experiments. We demonstrate the approach with several scenarios: a negotiation, a bail hearing, a job interview, and an auction. In each case, causal relationships are both proposed and tested by the system, finding evidence for some and not others. We provide evidence that the insights from these simulations of social interactions are not available to the LLM purely through direct elicitation. When given its proposed structural causal model for each scenario, the LLM is good at predicting the signs of estimated effects, but it cannot reliably predict the magnitudes of those estimates. In the auction experiment, the in silico simulation results closely match the predictions of auction theory, but elicited predictions of the clearing prices from the LLM are inaccurate. However, the LLM’s predictions are dramatically improved if the model can condition on the fitted structural causal model. In short, the LLM knows more than it can (immediately) tell…(More)”.

Shaping the Future of Learning: The Role of AI in Education 4.0

Curated on Posted on by Stefaan Verhulst

WEF Report: “This report explores the potential for artificial intelligence to benefit educators, students and teachers. Case studies show how AI can personalize learning experiences, streamline administrative tasks, and integrate into curricula.

The report stresses the importance of responsible deployment, addressing issues like data privacy and equitable access. Aimed at policymakers and educators, it urges stakeholders to collaborate to ensure AI’s positive integration into education systems worldwide leads to improved outcomes for all…(More)”

The Secret Life of Data

Curated on Posted on by Stefaan Verhulst

Book by Aram Sinnreich and Jesse Gilbert: “…explore the many unpredictable, and often surprising, ways in which data surveillance, AI, and the constant presence of algorithms impact our culture and society in the age of global networks. The authors build on this basic premise: no matter what form data takes, and what purpose we think it’s being used for, data will always have a secret life. How this data will be used, by other people in other times and places, has profound implications for every aspect of our lives—from our intimate relationships to our professional lives to our political systems.

With the secret uses of data in mind, Sinnreich and Gilbert interview dozens of experts to explore a broad range of scenarios and contexts—from the playful to the profound to the problematic. Unlike most books about data and society that focus on the short-term effects of our immense data usage, The Secret Life of Data focuses primarily on the long-term consequences of humanity’s recent rush toward digitizing, storing, and analyzing every piece of data about ourselves and the world we live in. The authors advocate for “slow fixes” regarding our relationship to data, such as creating new laws and regulations, ethics and aesthetics, and models of production for our datafied society.

Cutting through the hype and hopelessness that so often inform discussions of data and society, The Secret Life of Data clearly and straightforwardly demonstrates how readers can play an active part in shaping how digital technology influences their lives and the world at large…(More)”

AI chatbots refuse to produce ‘controversial’ output − why that’s a free speech problem

Curated on Posted on by Stefaan Verhulst

Article by Jordi Calvet-Bademunt and Jacob Mchangama: “Google recently made headlines globally because its chatbot Gemini generated images of people of color instead of white people in historical settings that featured white people. Adobe Firefly’s image creation tool saw similar issues. This led some commentators to complain that AI had gone “woke.” Others suggested these issues resulted from faulty efforts to fight AI bias and better serve a global audience.

The discussions over AI’s political leanings and efforts to fight bias are important. Still, the conversation on AI ignores another crucial issue: What is the AI industry’s approach to free speech, and does it embrace international free speech standards?…In a recent report, we found that generative AI has important shortcomings regarding freedom of expression and access to information.

Generative AI is a type of AI that creates content, like text or images, based on the data it has been trained with. In particular, we found that the use policies of major chatbots do not meet United Nations standards. In practice, this means that AI chatbots often censor output when dealing with issues the companies deem controversial. Without a solid culture of free speech, the companies producing generative AI tools are likely to continue to face backlash in these increasingly polarized times…(More)”.