Don’t know much about criminology.
Don’t know much about mendacity.
But I know we must protect democracy.
And if you and I voted regularly,
what a wonderful world this would be
Dharmapala: The Characteristics Of Tax Havens
NY Times Op-Ed: An Apology for Saying ‘Sorry’
The Worst Covid Strategy Was Not Picking One
The lessons of the pandemic are clearer in a global comparison.
In the US, the staggering toll is a reminder that all the wealth and vaccines in the world cannot save lives in a nation fractured by politics…”
Can a Writers Strike Save Hollywood from Monopoly? BIG by Matt Stoller
My AI Girlfriend Charges $1/Minute and Only Wants to Talk About Sex Vice
Pro Wrestling Artists Call Out AEW For Perceived AI-Generated T-Shirt Releases Paste
On Spoutible, Mastodon, and the Fediverse. "Should we stick with an easy system like the ones we know on Twitter and Facebook, where a few media kings rule us all? Or embrace the ambiguity of decentralization in the name of freedom?"
Google’s new Magic Editor pushes us toward AI-perfected fakery The Verge
India’s religious AI chatbots are speaking in the voice of god — and condoning violence Rest of World
Google’s AI Hype Circle Cory Doctorow
Measuring trends in Artificial Intelligence
Stanford University AI Index Report – “The AI Index Report developed at the Stanford Institute for Human-Centered Artificial Intelligence at Stanford University in California. The AI Index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. The annual report tracks, collates, distills, and visualizes data relating to artificial intelligence, enabling decision-makers to take meaningful action to advance AI responsibly and ethically with humans in mind. The AI Index collaborates with many different organizations to track progress in artificial intelligence. These organizations include: the Center for Security and Emerging Technology at Georgetown University, LinkedIn, NetBase Quid, Lightcast, and McKinsey. The 2023 report also features more self-collected data and original analysis than ever before. This year’s report included new analysis on foundation models, including their geopolitics and training costs, the environmental impact of AI systems, K-12 AI education, and public opinion trends in AI. The AI Index also broadened its tracking of global AI legislation from 25 countries in 2022 to 127 in 2023″
Help! My Political Beliefs Were Altered by a Chatbot!
AI assistants may be able to change our views without our realizing it. Says one expert: ‘What’s interesting here is the subtlety.’ When we ask ChatGPT or another bot to draft a memo, email, or presentation, we think these artificial-intelligence assistants are doing our bidding.
A growing body of research shows that they also can change our thinking—without our knowing. One of the latest studies in this vein, from researchers spread across the globe, found that when subjects were asked to use an AI to help them write an essay, that AI could nudge them to write an essay either for or against a particular view, depending on the bias of the algorithm.
Performing this exercise also measurably influenced the subjects’ opinions on the topic, after the exercise. “You may not even know that you are being influenced,” says Mor Naaman, a professor in the information science department at Cornell University, and the senior author of the paper. He calls this phenomenon “latent persuasion.” These studies raise an alarming prospect: As AI makes us more productive, it may also alter our opinions in subtle and unanticipated ways.
This influence may be more akin to the way humans sway one another through collaboration and social norms, than to the kind of mass-media and social media influence we’re familiar with..”
See also Whose Opinions Do Language Models Reflect? 30 Mar 2023. Language models (LMs) are increasingly being used in open-ended contexts, where the opinions reflected by LMs in response to subjective queries can have a profound impact, both on user satisfaction, as well as shaping the views of society at large.
In this work, we put forth a quantitative framework to investigate the opinions reflected by LMs — by leveraging high-quality public opinion polls and their associated human responses. Using this framework, we create OpinionsQA, a new dataset for evaluating the alignment of LM opinions with those of 60 US demographic groups over topics ranging from abortion to automation.
Across topics, we find substantial misalignment between the views reflected by current LMs and those of US demographic groups: on par with the Democrat-Republican divide on climate change. Notably, this misalignment persists even after explicitly steering the LMs towards particular demographic groups. Our analysis not only confirms prior observations about the left-leaning tendencies of some human feedback-tuned LMs, but also surfaces groups whose opinions are poorly reflected by current LMs (e.g., 65+ and widowed individuals). Our code and data are available at this https URL.”
