The emergence of crowdsourced data has significantly reshaped social science, enabling extensive exploration of collective human actions, viewpoints, and societal dynamics. However, ensuring safe, fair, and reliable participation remains a persistent challenge. Traditional polling methods have seen a notable decline in engagement over recent decades, raising concerns about the credibility of collected data. Meanwhile, social and peer-to-peer networks have become increasingly widespread, but data from these platforms can suffer from credibility issues due to fraudulent or ineligible participation. In this paper, we explore how social interactions can help restore credibility in crowdsourced data collected over social networks. We present an empirical study to detect ineligible participation in a polling task through AI-based graph analysis of social interactions among imperfect participants composed of honest and dishonest actors. Our approach focuses solely on the structure of social interaction graphs, without relying on the content being shared. We simulate different levels and types of dishonest behavior among participants who attempt to propagate the task within their social networks. We conduct experiments on real-world social network datasets, using different eligibility criteria and modeling diverse participation patterns. Although structural differences in social interaction graphs introduce some performance variability, our study achieves promising results in detecting ineligibility across diverse social and behavioral profiles, with accuracy exceeding 90% in some configurations.

A large majority of American voters are skeptical of the argument that the U.S. should race ahead to build ever more powerful artificial intelligence, unconstrained by domestic regulations, in an effort to compete with China, according to new polling shared exclusively with TIME. The findings indicate that American voters disagree with a common narrative levied by the tech industry, in which CEOs and lobbyists have repeatedly argued the U.S. must tread carefully with AI regulation in order to not hand the advantage to their geopolitical rival. And they reveal a startling level of bipartisan consensus on AI policy, with both Republicans and Democrats in support of the government placing some limits on AI development in favor of safety and national security. According to the poll, 75% of Democrats and 75% of Republicans believe that "taking a careful controlled approach" to AI--by preventing the release of tools that terrorists and foreign adversaries could use against the U.S.--is preferable to "moving forward on AI as fast as possible to be the first country to get extremely powerful AI." A majority of voters support more stringent security practices at AI companies, and are worried about the risk of China stealing their most powerful models, the poll shows.

Even a halfway-decent political campaign knows you better than you know yourself. A candidate's army of number crunchers vacuums up any morsel of personal information that might affect the choice we make at the polls. In 2020, Donald Trump and the Republican Party compiled 3,000 data points on every single voter in America. In 2012, the data nerds helped Barack Obama parse the electorate to microtarget his door-knocking efforts toward the most-persuadable swing voters. And in 1960, John F. Kennedy had the People Machine.

Shocker: people aren't quite sure that they trust artificial intelligence to operate in their best interests, per a new poll. In a press release, the think tank MITRE released the results of a new poll, conducted in tandem with the marketing research firm Harris, that asked people their opinions about AI. Spoiler alert: they lowkey hate it! "Most Americans express reservations about AI for high-value applications such as autonomous vehicles, accessing government benefits, or healthcare," the press release reads. "Moreover, only 48 percent believe AI is safe and secure, and 78 percent are very or somewhat concerned that AI can be used for malicious intent."

Predicting future events is hard, but it's among the most important tasks a journalist can perform. Especially if you work at a section called Future Perfect. Our mission is to explain the world around us to our readers, and it's impossible to do that without anticipating what comes next. Will inflation continue to rise in the US and Europe, or level off? Will the Supreme Court allow states to ban abortion, eliminating legal access in red states? Will Brazil's 212 million people be led by a left-wing populist, or a far-right anti-vaxxer? All of these questions matter, and preparing ourselves for potential outcomes -- and having a good sense of how likely specific outcomes are -- is a major part of explaining the world accurately. And if policymakers could rely on accurate predictions about the outcome of a foreign war or the advisability of a budget proposal, they could make much better policy decisions. Being good at predictions is a skill like any other -- you have to practice it.

On October 18, 2016, the New York Times gave Hillary Clinton a ninety-one-per-cent chance of beating Donald Trump. Five days later, ABC News released a tracking poll showing her ahead of Trump by twelve points. Buoyed by the polls, Democrats--especially Democratic women--approached November 8th with a joyful sense of inevitability. The collective disbelief when Clinton lost was tinged with confusion: How could the pollsters have been so wrong? Now, with Joe Biden leading Trump by double digits in the lead-up to Election Day, according to the latest NPR/PBS NewsHour/Marist survey, the question has to be asked: Are voters hoping for a Biden victory about to fall in the same trap?

Campaigns and elections have always been about data--underneath the empathetic promises to fix your problems and fight for your family, it's a business of metrics. If a campaign is lucky, it will find its way through a wilderness of polling, voter attributes, demographics, turnout, impressions, gerrymandering, and ad buys to connect with voters in a way that moves or even inspires them. Obama, MAGA, AOC--all have had some of that special sauce. Still, campaigns that collect and use the numbers best win. That's been true for some time, of course.

Artificial intelligence is likely to be the defining technology of the century, affecting everything from war to jobs to health care. So, understanding what the general public wants from AI is important. A new survey suggests that while there's no strong consensus on the topic, more Americans are in favor of AI than actively oppose it. In polling organized by the University of Oxford's Future of Humanity Institute, forty-one percent of respondents said they somewhat or strongly supported the development of AI, while 22 percent said they somewhat or strongly opposed it. The remaining 28 percent said they had no strong feelings one way or the other.

Donald Trump was lagging behind in nearly all opinion polls leading up to the 2016 US presidential election, but he surprisingly won the election. This raises the following important questions: 1) why most opinion polls were not accurate in 2016? and 2) how to improve the accuracies of opinion polls? In this paper, we study the inaccuracies of opinion polls in the 2016 election through the lens of information theory. We first propose a general framework of parameter estimation, called clean sensing (polling), which performs optimal parameter estimation with sensing cost constraints, from heterogeneous and potentially distorted data sources. We then cast the opinion polling as a problem of parameter estimation from potentially distorted heterogeneous data sources, and derive the optimal polling strategy using heterogenous and possibly distorted data under cost constraints. Our results show that a larger number of data samples do not necessarily lead to better polling accuracy, which give a possible explanation of the inaccuracies of opinion polls in 2016. The optimal sensing strategy should instead optimally allocate sensing resources over heterogenous data sources according to several factors including data quality, and, moreover, for a particular data source, it should strike an optimal balance between the quality of data samples, and the quantity of data samples. As a byproduct of this research, in a general setting, we derive a group of new lower bounds on the mean-squared errors of general unbiased and biased parameter estimators. These new lower bounds can be tighter than the classical Cram\'{e}r-Rao bound (CRB) and Chapman-Robbins bound. Our derivations are via studying the Lagrange dual problems of certain convex programs. The classical Cram\'{e}r-Rao bound and Chapman-Robbins bound follow naturally from our results for special cases of these convex programs.

Throughout 2017, I have been running polls on the public's appetite for risk regarding the pursuit of superintelligence. I've been running these on Surveymonkey, paying for audiences so as to minimize distortions in the data. I've spent nearly $10,000 on this project. I did this in about the most scientific way I could. It is not a "passed around" survey, but rather paid polling across the entire American spectrum.