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
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
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
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
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
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
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
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)”.
‘Eugenics on steroids’: the toxic and contested legacy of Oxford’s Future of Humanity Institute
Article by Andrew Anthony: “Two weeks ago it was quietly announced that the Future of Humanity Institute, the renowned multidisciplinary research centre in Oxford, no longer had a future. It shut down without warning on 16 April. Initially there was just a brief statement on its website stating it had closed and that its research may continue elsewhere within and outside the university.
The institute, which was dedicated to studying existential risks to humanity, was founded in 2005 by the Swedish-born philosopher Nick Bostrom and quickly made a name for itself beyond academic circles – particularly in Silicon Valley, where a number of tech billionaires sang its praises and provided financial support.
Bostrom is perhaps best known for his bestselling 2014 book Superintelligence, which warned of the existential dangers of artificial intelligence, but he also gained widespread recognition for his 2003 academic paper “Are You Living in a Computer Simulation?”. The paper argued that over time humans were likely to develop the ability to make simulations that were indistinguishable from reality, and if this was the case, it was possible that it had already happened and that we are the simulations….
Among the other ideas and movements that have emerged from the FHI are longtermism – the notion that humanity should prioritise the needs of the distant future because it theoretically contains hugely more lives than the present – and effective altruism (EA), a utilitarian approach to maximising global good.
These philosophies, which have intermarried, inspired something of a cult-like following,…
Torres has come to believe that the work of the FHI and its offshoots amounts to what they call a “noxious ideology” and “eugenics on steroids”. They refuse to see Bostrom’s 1996 comments as poorly worded juvenilia, but indicative of a brutal utilitarian view of humanity. Torres notes that six years after the email thread, Bostrom wrote a paper on existential risk that helped launch the longtermist movement, in which he discusses “dysgenic pressures” – dysgenic is the opposite of eugenic. Bostrom wrote:
“Currently it seems that there is a negative correlation in some places between intellectual achievement and fertility. If such selection were to operate over a long period of time, we might evolve into a less brainy but more fertile species, homo philoprogenitus (‘lover of many offspring’).”…(More)”.
Lethal AI weapons are here: how can we control them?
Article by David Adam: “The development of lethal autonomous weapons (LAWs), including AI-equipped drones, is on the rise. The US Department of Defense, for example, has earmarked US$1 billion so far for its Replicator programme, which aims to build a fleet of small, weaponized autonomous vehicles. Experimental submarines, tanks and ships have been made that use AI to pilot themselves and shoot. Commercially available drones can use AI image recognition to zero in on targets and blow them up. LAWs do not need AI to operate, but the technology adds speed, specificity and the ability to evade defences. Some observers fear a future in which swarms of cheap AI drones could be dispatched by any faction to take out a specific person, using facial recognition.
Warfare is a relatively simple application for AI. “The technical capability for a system to find a human being and kill them is much easier than to develop a self-driving car. It’s a graduate-student project,” says Stuart Russell, a computer scientist at the University of California, Berkeley, and a prominent campaigner against AI weapons. He helped to produce a viral 2017 video called Slaughterbots that highlighted the possible risks.
The emergence of AI on the battlefield has spurred debate among researchers, legal experts and ethicists. Some argue that AI-assisted weapons could be more accurate than human-guided ones, potentially reducing both collateral damage — such as civilian casualties and damage to residential areas — and the numbers of soldiers killed and maimed, while helping vulnerable nations and groups to defend themselves. Others emphasize that autonomous weapons could make catastrophic mistakes. And many observers have overarching ethical concerns about passing targeting decisions to an algorithm…(More)”