Kenneth Evans, Rice University

For the first time in American history, quantum computing was mentioned by a candidate during a presidential debate, on Sept. 10, 2024. After Vice President Kamala Harris brought up quantum technology, she and former President Donald Trump went on to have a heated back-and-forth about American chipmaking and China’s rise in semiconductor manufacturing. Science and technology policy usually takes a back seat to issues such as immigration, the economy and health care during election season.

What’s changed for 2024?

From COVID-19 to climate change, ChatGPT to, yes, quantum computers, science-related issues are on the minds of American policymakers and voters alike. The federal government spends nearly US$200 billion each year on scientific research and development to address these challenges and many others. Presidents and Congress, however, rarely agree on how – and how much – money should be spent on science.

With the increasing public focus on global competitiveness, the climate crisis and artificial intelligence, a closer look at Trump’s and Harris’ records on science and technology policy could provide a hint about how they’d approach these topics if elected this fall.

Two distinct visions for science funding

If politics can be described as “who gets what and when,” U.S. science and technology policy can be assessed through the annual budget process for R&D. By this measure, the differences between the Trump and Biden-Harris administrations couldn’t be starker.

In his first budget request to Congress, in 2017, Trump spurned decades of precedent, proposing historic cuts across nearly every federal science agency. In particular, Trump targeted climate-related programs at the Department of Energy, the National Oceanic and Atmospheric Administration and the Environmental Protection Agency.

Trump’s fiscal policy took a page from Reagan-era conservative orthodoxy, prioritizing military spending over social programs, including R&D. Unlike Reagan, however, Trump also took aim at basic research funding, an area with long-standing bipartisan support in Congress. His three subsequent budget proposals were no different: across-the-board reductions to federal research programs, while pushing for increases to defense technology development and demonstration projects.

Congress rebuked nearly all of Trump’s requests. Instead, it passed some of the largest increases to federal R&D programs in U.S. history, even before accounting for emergency spending packages funded as part of the government’s pandemic response.

In contrast, the Biden-Harris administration made science and innovation a centerpiece of its early policy agenda – with budgets to match. Leveraging the slim Democratic majority during the 117th Congress, Biden and Harris shepherded three landmark bills into law: the Infrastructure Investment and Jobs Act, the Inflation Reduction Act and the CHIPS and Science Act. These laws contain significant R&D provisions focused on environmental projects (IIJA), clean energy (IRA) and American semiconductor manufacturing (CHIPS).

CHIPS set up programs within the National Science Foundation and the Department of Commerce to create regional technology hubs in support of American manufacturing. The act also set ambitious funding targets for federal science agencies, especially at NSF, calling for its budget to be doubled from $9 billion to over $18 billion over the course of five years.

Despite its initial push for R&D, the Biden-Harris administration’s final two budget proposals offered far less to science. Years of deficit spending and a new Republican majority in the House cast a cloud of budget austerity over Congress. Instead of moving toward doubling NSF’s budget, the agency suffered an 8% decrease in fiscal year 2024 – its biggest cut in over three decades. For FY2025, which runs from Oct. 1, 2024, through Sept. 30, 2025, Biden and Harris requested a meager 3% increase for NSF, billions of dollars short of CHIPS-enacted spending levels.

An emerging consensus on China

On technology policy, Biden and Harris share more with Trump than they let on.

Their approach to competing with China on tech follows Trump’s lead: They’ve expanded tariffs on Chinese goods and severely limited China’s access to American-made computer chips and semiconductor manufacturing equipment.

Biden and Harris have also ramped up research security efforts intended to protect U.S. ideas and innovation from China. Trump launched the China Initiative as an attempt to stop the Chinese government from stealing American research. The Biden-Harris administration ended the program in 2022, but pieces of it remain in place. Scientific collaborations between the United States and China continue to decline, to the detriment of American scientific leadership.

The Biden-Harris administration has also drawn from Trump-era policy to strengthen America’s leadership in “industries of the future.” The term, coined by Trump’s then-chief science adviser Kelvin Droegemeier, refers to five emerging technology areas: AI, quantum science, advanced manufacturing, advanced communications and biotechnology. This language has been parroted by the Biden-Harris administration as part of its focus on American manufacturing and throughout Harris’ campaign, including during the debate.

In short, both candidates align with the emerging Washington bipartisan consensus on China: innovation policy at home, strategic decoupling abroad.

Science advice not always a welcome resource

Trump’s dismissal of and at times outright contempt for scientific consensus is well documented. From “Sharpiegate,” when he mapped his own projected path for Hurricane Dorian, to pulling out of the Paris climate agreement, World Health Organization and the Iran nuclear deal, Trump has demonstrated an unwillingness to accept any advice, let alone from scientists.

Indeed, Trump took over two years to hire Droegemeier as director of the White House Office of Science and Technology Policy, or OSTP, doubling the previous record for the length of time a president has gone without a scientific adviser. This absence was no doubt reflected in Trump’s short-on-science budget requests to Congress, especially during the beginning of his administration.

On the other hand, the Biden-Harris administration has promoted science and innovation as a core part of its broader economic policy agenda. It elevated the role of OSTP: Biden is the first president to name his science adviser – a position currently held by Arati Prabhakar – as a member of his Cabinet.

By law, the president is required to appoint an OSTP director. But it is up to the president to decide how and when to use their advice. If the new White House wants the U.S. to remain a global leader in R&D, the science adviser will need to continue to fight for it.

Kenneth Evans, Scholar in Science and Technology Policy, Baker Institute for Public Policy, Rice University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Filippo Menczer, Indiana University

Foreign influence campaigns, or information operations, have been widespread in the run-up to the 2024 U.S. presidential election. Influence campaigns are large-scale efforts to shift public opinion, push false narratives or change behaviors among a target population. Russia, China, Iran, Israel and other nations have run these campaigns by exploiting social bots, influencers, media companies and generative AI.

At the Indiana University Observatory on Social Media, my colleagues and I study influence campaigns and design technical solutions – algorithms – to detect and counter them. State-of-the-art methods developed in our center use several indicators of this type of online activity, which researchers call inauthentic coordinated behavior. We identify clusters of social media accounts that post in a synchronized fashion, amplify the same groups of users, share identical sets of links, images or hashtags, or perform suspiciously similar sequences of actions.

We have uncovered many examples of coordinated inauthentic behavior. For example, we found accounts that flood the network with tens or hundreds of thousands of posts in a single day. The same campaign can post a message with one account and then have other accounts that its organizers also control “like” and “unlike” it hundreds of times in a short time span. Once the campaign achieves its objective, all these messages can be deleted to evade detection. Using these tricks, foreign governments and their agents can manipulate social media algorithms that determine what is trending and what is engaging to decide what users see in their feeds.

Generative AI

One technique increasingly being used is creating and managing armies of fake accounts with generative artificial intelligence. We analyzed 1,420 fake Twitter – now X – accounts that used AI-generated faces for their profile pictures. These accounts were used to spread scams, disseminate spam and amplify coordinated messages, among other activities.

We estimate that at least 10,000 accounts like these were active daily on the platform, and that was before X CEO Elon Musk dramatically cut the platform’s trust and safety teams. We also identified a network of 1,140 bots that used ChatGPT to generate humanlike content to promote fake news websites and cryptocurrency scams.

In addition to posting machine-generated content, harmful comments and stolen images, these bots engaged with each other and with humans through replies and retweets. Current state-of-the-art large language model content detectors are unable to distinguish between AI-enabled social bots and human accounts in the wild.

Model misbehavior

The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Therefore it is unclear, for example, whether online influence campaigns can sway election outcomes. Yet, it is vital to understand society’s vulnerability to different manipulation tactics.

In a recent paper, we introduced a social media model called SimSoM that simulates how information spreads through the social network. The model has the key ingredients of platforms such as Instagram, X, Threads, Bluesky and Mastodon: an empirical follower network, a feed algorithm, sharing and resharing mechanisms, and metrics for content quality, appeal and engagement.

SimSoM allows researchers to explore scenarios in which the network is manipulated by malicious agents who control inauthentic accounts. These bad actors aim to spread low-quality information, such as disinformation, conspiracy theories, malware or other harmful messages. We can estimate the effects of adversarial manipulation tactics by measuring the quality of information that targeted users are exposed to in the network.

We simulated scenarios to evaluate the effect of three manipulation tactics. First, infiltration: having fake accounts create believable interactions with human users in a target community, getting those users to follow them. Second, deception: having the fake accounts post engaging content, likely to be reshared by the target users. Bots can do this by, for example, leveraging emotional responses and political alignment. Third, flooding: posting high volumes of content.

Our model shows that infiltration is the most effective tactic, reducing the average quality of content in the system by more than 50%. Such harm can be further compounded by flooding the network with low-quality yet appealing content, thus reducing quality by 70%.

Curbing coordinated manipulation

We have observed all these tactics in the wild. Of particular concern is that generative AI models can make it much easier and cheaper for malicious agents to create and manage believable accounts. Further, they can use generative AI to interact nonstop with humans and create and post harmful but engaging content on a wide scale. All these capabilities are being used to infiltrate social media users’ networks and flood their feeds with deceptive posts.

These insights suggest that social media platforms should engage in more – not less – content moderation to identify and hinder manipulation campaigns and thereby increase their users’ resilience to the campaigns.

The platforms can do this by making it more difficult for malicious agents to create fake accounts and to post automatically. They can also challenge accounts that post at very high rates to prove that they are human. They can add friction in combination with educational efforts, such as nudging users to reshare accurate information. And they can educate users about their vulnerability to deceptive AI-generated content.

Open-source AI models and data make it possible for malicious agents to build their own generative AI tools. Regulation should therefore target AI content dissemination via social media platforms rather then AI content generation. For instance, before a large number of people can be exposed to some content, a platform could require its creator to prove its accuracy or provenance.

These types of content moderation would protect, rather than censor, free speech in the modern public squares. The right of free speech is not a right of exposure, and since people’s attention is limited, influence operations can be, in effect, a form of censorship by making authentic voices and opinions less visible.

Filippo Menczer, Professor of Informatics and Computer Science, Indiana University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Julie Sullivan, Emory University and Wilbur Lam, Georgia Institute of Technology

A scratchy, sore throat, a relentless fever, a pounding head and a nasty cough – these symptoms all scream upper respiratory illness. But which one?

Many of the viruses that cause upper respiratory infections such as influenza A or B and the virus that causes COVID-19 all employ similar tactics. They target the same areas in your body – primarily the upper and lower airways – and this shared battleground triggers a similar response from your immune system. Overlapping symptoms – fever, cough, fatigue, aches and pains – make it difficult to determine what may be the underlying cause.

Now, at-home rapid tests can simultaneously determine whether someone has COVID-19 or the flu. Thanks in part to the National Institutes of Health‘s Rapid Acceleration of Diagnostics, or RADx, program, the Food and Drug Administration has provided emergency use authorization for seven at-home rapid tests that can distinguish between COVID-19, influenza A and influenza B.

Our team in Atlanta – composed of biomedical engineers, clinicians and researchers at Emory University, Children’s Healthcare of Atlanta and Georgia Institute of Technology – is part of the RADx Test Verification Core. We closely collaborate with other institutions and agencies to determine whether and how well COVID-19 and influenza diagnostics work, effectively testing the tests. Our center has worked with almost every COVID and flu diagnostic on the market, and our data helped inform the instructions you might see in many of the home test kits on the market.

While no test is perfect, to now be able to test for certain viruses at home when symptoms begin can help patients and their doctors come up with appropriate care plans sooner.

A new era of at-home tests

Traditionally, identifying the virus causing upper respiratory illness symptoms required going to a clinic or hospital for a trained medical professional to collect a nasopharyngeal sample. This involves inserting a long, fiber-tipped swab that looks like a skinny Q-tip into one of your nostrils and all the way to the back of your nose and throat to collect virus-containing secretions. The sample is then typically sent to a lab for analysis, which could take hours to days for results.

Thanks to the COVID-19 pandemic, the possibility of using over-the-counter tests to diagnose respiratory illnesses at home became a reality. These tests used a much gentler and less invasive nasal swab and could also be done by anyone, anytime and in their own home. However, these tests were designed to diagnose only COVID-19 and could not distinguish between other types of illnesses.

Since then, researchers have developed over-the-counter multiplex tests that can screen for more than one respiratory infection at once. In 2023, Pfizer’s Lucira test became the first at-home diagnostic test for both COVID-19 and influenza to gain emergency use authorization.

What are multiplex rapid tests?

There are two primary forms of at-home COVID-19 and COVID-19/flu combination tests: molecular tests such as PCR that detect genetic material from the virus, and antigen tests – commonly referred to as rapid tests – that detect proteins called antigens from the virus.

The majority of over-the-counter COVID-19 and COVID-19/flu tests on the market are antigen tests. They detect the presence of antigens in your nasal secretions that act as a biological signature for a specific virus. If viral antigens are present, that means you’re likely infected.

To detect these antigens, rapid tests have paper-like strips coated with specially engineered antibodies that function like a molecular Velcro, sticking only to a specific antigen. Scientists design and manufacture specialized strips to recognize specific viral antigens, like those belonging to influenza A, influenza B or the virus that causes COVID-19.

The antibodies for these viral targets are placed on the strip, and when someone’s nasal sample has viral proteins that are applied to the test strip, a line will appear for that virus in particular.

Advancing rapid antigen tests

Like all technologies, rapid antigen tests have limitations.

Compared with lab-based PCR tests that can detect the presence of small amounts of pathogen by amplifying them, antigen tests are typically less sensitive than PCR and could miss an infection in some cases.

All at-home COVID-19 and COVID-19/flu antigen tests are authorized for repeat use. This means if someone is experiencing symptoms – or has been exposed to someone with COVID-19 but is not experiencing symptoms – and has a negative result for their first test, they should retest 48 hours later.

Another limitation to rapid antigen tests is that currently they are designed to test only for COVID-19, influenza A and influenza B. Currently available over-the-counter tests aren’t able to detect illnesses from pathogens that look like these viruses and cause similar symptoms, such as adenovirus or strep.

Because multiplex texts can detect several different viruses, they can also produce findings that are more complex to interpret than tests for single viruses. This may increase the risk of a patient incorrectly interpreting their results, misreading one infection for another.

Researchers are actively developing even more sophisticated tests that are more sensitive and can simultaneously screen for a wider range of viruses or even bacterial infections. Scientists are also examining the potential of using saliva samples in tests for bacterial or viral infections.

Additionally, scientists are exploring integrating multiplex tests with smartphones for rapid at-home diagnosis and reporting to health care providers. This may increase the accessibility of these tests for people with vision impairment, low dexterity or other challenges with conducting and interpreting at-home tests.

Faster and more accurate diagnoses lead to more targeted and effective treatment plans, potentially reducing unnecessary antibiotic use and improving patient outcomes. The ability to rapidly identify and track outbreaks can also empower public health officials to better mitigate the spread of infectious diseases.

Julie Sullivan, Chief Operating Officer of RADx Tech, Emory University and Wilbur Lam, Chief Innovation Officer, Children’s Healthcare of Atlanta Pediatric Technology Center; Professor of Biomedical Engineering, Georgia Institute of Technology

This article is republished from The Conversation under a Creative Commons license. Read the original article.