Carbon Chemist

Tag: Information technology

  • Misinformation might sway elections — but not in the way that you think

    Misinformation might sway elections — but not in the way that you think

    [ad_1]

    In January, some US voters in New Hampshire received automated phone messages in which President Joe Biden’s voice urged them not to vote in the state’s Democratic Party primary election. It wasn’t actually Biden, however: the message had been generated by artificial intelligence (AI).

    The rise of such AI fakery has made more people than ever concerned about the effect of misinformation on elections, particularly because 2024 is a standout year for democracy. More than 50 countries will have held elections by the end of December, including high-profile polls in the United States, United Kingdom, India, South Africa and Mexico, totalling more than 2 billion potential voters. The broad umbrella of misinformation — encompassing all false information, whether spread unknowingly or with intent to deceive — could have devastating impacts.

    The World Economic Forum has ranked misinformation as its top global risk over the next two years, above extreme weather events and even war. Policymakers around the world have drafted and passed laws and measures in an attempt to combat the growing threat.

    Although the problem is undoubtedly real, the true impact of misinformation in elections is less clear. Some researchers say the claimed risks to democracy posed by misinformation are overblown. “I think there’s a lot of moral panic, if you will, about misinformation,” says Erik Nisbet, a communications and policy researcher at Northwestern University in Evanston, Illinois. A body of research suggests that it is notoriously difficult to persuade people to change their vote, for example. It’s also far from clear how any one message — true or false — can penetrate amid the media chaos.

    What we do — and don’t — know about how misinformation spreads online

    Still, as others point out, misinformation does not have to change minds about politics to have an impact. It can, for example, mislead people about when and where to vote, or even whether they should do so at all. Furthermore, just knowing that misinformation is out there — and believing it is influential — is enough for many people to lose faith and trust in robust systems, from science and health care to fair elections.

    And even if misinformation affects only small numbers of people, if it drives them to action, then that too can have an amplified impact. “We might not expect widespread effects across the whole population, but it might have some radicalizing effects on tiny groups of people who can do a lot of harm,” says Gregory Eady, a political scientist at the University of Copenhagen, who studies the effects of social media.

    Steadfast opinions

    The World Economic Forum justified placing misinformation as the planet’s most urgent priority because, according to its Global Risks Report for 2024, it could “radically disrupt electoral processes in several economies” and “trigger civil unrest and possibly confrontation”.

    Historians can point to many examples of both. In ancient Rome, Octavian (adopted son and heir of the murdered Julius Caesar) launched a smear campaign that falsely portrayed his rival Marc Antony as a traitor, as part of a successful bid to become the first emperor of Rome. More recently, misinformation has been blamed for a swathe of social and political trends — from people’s reluctance to get vaccinated against COVID-19 and rising discrimination against migrants, to the Brexit vote for the United Kingdom to leave the European Union and scepticism about the seriousness of climate change.

    The problem, researchers say, is that it’s very hard to prove cause and effect: to determine that any given piece of misinformation made a material difference to how people behaved. “It’s generally a very difficult kind of question, to get at what the effects of misinformation are in the real world,” says Eady.

    Blocking information on COVID-19 can fuel the spread of misinformation

    Difficult, but not impossible. Last year, Eady and his colleagues published the results of one such empirical analysis1, which considered a high-profile and controversial question: to what extent did misinformation spread on the social-media platform Twitter (as it was then known) by Russian sources influence the 2016 US election?

    There’s little doubt that Russian social-media accounts impersonated US users in a way that was intended to polarize the US electorate and build support for the Republican presidential candidate, Donald Trump. Eady and his colleagues showed that those trolls reached potentially millions of people. But the analysis also showed that the bulk of the misinformation was probably seen by only a small proportion of them — and by people who already self-identified as Republican.

    Although it might seem that the world is drowning in misinformation, it’s only a drop in the ocean compared with the tsunami of other news that people see and hear every day, Eady says. The Twitter users were exposed to hundreds of times more posts from domestic news sources and politicians, Eady found, especially as the election drew nearer.

    Sacha Altay, an experimental psychologist at the University of Zurich, Switzerland, says people tend to decide who to vote for on the basis of gut instinct, values and beliefs, rather than on information — whether that information is true or not. Particularly in the US two-party system, people tend to identify strongly with the values of one party or the other, says Altay. “We should start from the premise that it’s very unlikely that any type of information will change people’s decision,” he says.

    Swayed behaviour

    A more effective form of political persuasion, researchers and strategists say, is to focus not on getting people to change their minds, but rather on getting them to act, or not, on their existing beliefs.

    Misinformation about politics and public health can have this kind of effect, says Kate Starbird, a computer scientist who co-founded the University of Washington’s Center for an Informed Public in Seattle. This is especially the case if the misinformation is picked up and amplified by public figures, even if they are prominent only in small communities. “Those communities can have impact on politics at scale,” she says.

    Residents sitting while casting their vote for Presidential Primary Elections in San Francisco.

    In primary elections earlier this year, US voters selected candidates for November’s presidential election.Credit: Tayfun Coskun/Anadolu/Getty

    For example, misinformation about the validity of the 2020 US presidential election was amplified and spread by a subset of Trump supporters to trigger the attack on the US Capitol building on 6 January 2021. One recent study2 concluded that, in a sample of nearly 665,000 US registered voters on X (formerly Twitter), just over 2,100 people accounted for 80% of the fake news shared about this election. Starbird adds that an increasing distrust of measles vaccinations in Florida in recent years, which has led to a spike in cases, has been fuelled by small groups taking up that cause and spreading false facts.

    The same leverage on people’s behaviour could apply to voting. Although it might be hard to convince people to switch allegiances, it could be easier to persuade them that they don’t need to bother to vote at all, for example. Researchers say that misinformation about the electoral process is on the rise. “We see that in more and more elections,” says Max Grömping, a political scientist who studies election disinformation at Griffith University in Brisbane, Australia. “Basically, messages saying, ‘Oh, you know, the election is postponed, it’s next week, you don’t have to show up.’”

    An example was seen last year in the lead up to an Australian referendum on changing the constitution to establish a formal Indigenous representative body in parliament. At the time, misinformation circulated online stating that the referendum was not compulsory, seemingly to discourage voter turnout. In fact, all voting in Australia is compulsory. In this case, turnout did not seem to be affected; it was slightly higher than in the 2022 national election.

    The deepfaked Biden robocalls in January were traced to a political consultant who said he was trying to draw attention to the potential harm that such misinformation could cause. The Federal Communications Commission is now attempting to levy a US$6-million fine against the consultant. The spoof calls, made on 21 January, became high-profile news on 22 January, before the 23 January vote.

    Seeing is believing

    The Biden case in particular showcases people’s concerns that AI might fuel these kinds of deception, with deepfakes making misinformation seem more realistic. Researcher Hany Farid at the University of California, Berkeley, is keeping track of deepfake cases in the run-up to the US election. Examples on his website include an AI image of Biden in military fatigues, seemingly on the verge of authorizing military strikes, and one of Donald Trump purportedly meeting Black voters.

    Deepfake of Trump smiling with his arms around a group of black women.

    An image generated by artificial intelligence shows former US president Donald Trump with Black supporters. Credit: Generated by AI

    The recent election in India was plagued by deepfakes, from deceased political figures making speeches to Bollywood actors giving fake endorsements to political parties. And in Slovakia, fake audio of the parliamentary candidate Michal Šimečka talking about buying votes and plans to raise the price of beer was released on the eve of the September 2023 election.

    AI is likely to increase the volume of misinformation because it lowers the level of technical skill needed to create credible content, Nisbet says. “But much like the other research on misinformation, it will be difficult to make any actual causal claims unless it’s studied very carefully,” he says.

    Some studies have looked at whether the type of media (text versus imagery or video, for example) affects a message’s persuasiveness. Their results have been mixed. Some do suggest that images can be more persuasive: in one study of health messaging about alcohol and cancer, for example, women were more likely to say they would drink less if they saw social-media posts that added images to narrative text3.

    AI-fuelled election campaigns are here — where are the rules?

    But research by a team at the Massachusetts Institute of Technology (MIT) in Cambridge in 2021 suggests that the persuasive effects of imagery might be minimal. In the study4, the researchers showed people either video clips or transcripts from political advertisements and COVID-19 messaging. They found that people were more likely to believe that an event really occurred when they saw pictures rather than just reading about it. But there was little difference in measures of persuasiveness, such as whether their attitude was changed by the information or whether they were inclined to share it.

    “Just because video might be more believable than text doesn’t mean that it is noticeably better at changing people’s minds,” says Adam Berinsky, a co-author of the study and a political scientist at MIT.

    Sowing distrust

    As fears about misinformation have risen, policymakers have been scrambling to keep up. In recent years, many countries have passed laws and regulations that they claim tackle misinformation, but which have raised concerns about free speech.

    As of this January, for example, people in the United Kingdom who release information they know to be false, with the intent to cause “non-trivial” harm, can be punished with fines or several months in jail. Ministers said the move was explicitly intended to clamp down on “dangerous disinformation and election interference online”.

    Several bills have been proposed to limit misinformation in the United States. Some would outlaw deceptive uses of AI to portray something that didn’t happen or wasn’t said, whereas others call for better labelling of AI-generated content. Whether such labels actually influence people’s assessments of content or their take-home messages from exposure to altered media is also unclear5.

    Protester with placard saying 'Uk broken democracy, why vote?' in front of the parliament during Brexit demonstration.

    During a 2019 demonstration near the UK Parliament, a protester rallied against the government.Credit: David Rowe/Alamy

    Many researchers warn that, ominously, reports discussing the rising tide of misinformation (perhaps including this one) might have the same effect as the misinformation itself. “A lot of disinformation campaigns are aimed at sowing distrust,” says Altay. “When we tell people that disinformation works and that misinformation is everywhere, we are also sowing doubt and reducing trust in reliable sources.”

    Faith in institutions from politics to science and health care has certainly decreased in many countries. Earlier this year, the latest annual survey by global communications firm Edelman found that British people’s trust in the UK government had fallen to 30% — its lowest value since 2012. And in 2023, the Pew Research Center in Washington DC found that 57% of Americans said science has had a mostly positive effect on society. This is a drop of 8 percentage points since November 2021 and of 16 points since before the start of the COVID-19 outbreak.

    “Misinformation is a serious problem that we need to address,” says Nisbet, “but we don’t want to communicate about it in a way that actually makes things worse.”

    [ad_2]

    Source link

  • The dream of electronic newspapers becomes a reality — in 1974

    The dream of electronic newspapers becomes a reality — in 1974

    [ad_1]

    Nature, Published online: 07 May 2024; doi:10.1038/d41586-024-01220-3

    Efforts to develop an electronic newspaper providing information at the touch of a button took a step forward 50 years ago, and airborne bacteria in the London Underground come under scrutiny, in the weekly dip into Nature’s archive.

    [ad_2]

    Source link

  • How scientists are making the most of Reddit

    How scientists are making the most of Reddit

    [ad_1]

    A hallway at Reddit's office in New York, with a large Reddit logo on the white wall

    Reddit’s many ‘subreddit’ communities offer channels for discussing science and are of interest to social-media scholars.Credit: Amy Lombard/New York Times/Redux/eyevine

    It has been almost 18 months since Elon Musk purchased Twitter, now known as X. Since the tech mogul took ownership, in October 2022, the number of daily active users of the platform’s mobile app has fallen by around 15%, and in April 2023 the company cut its workforce by 80%. Thousands of scientists are reducing the time they spend on the platform (Nature 613, 19–21; 2023). Some have gravitated towards newer social-media alternatives, such as Mastodon and Bluesky. But others are finding a home on a system that pre-dates Twitter: Reddit.

    The site was founded in 2005, originally as one all-encompassing forum where users (known as redditors) could post content such as links, texts, images and videos. Anonymous user upvote (or downvote) and comment on each other’s content, deciding on what performs well enough to reach others’ feeds.

    Social media for scientists

    Today, Reddit is divided into communities, called subreddits, each with volunteer moderators who review content. These subreddits have names that begin with ‘r/’ and are devoted to all sorts of subjects, such as literature, solo travel and Washington DC. Reddit is regularly irreverent: r/trees is for people to share content about marijuana, whereas r/marijuanaenthusiasts is the place to look at trees. It is sometimes dangerous — some communities have amplified conspiracy theories. And there are subreddits devoted to science, ranging from the broad r/science to more specific ones, such as r/bacteriophages.

    As of December 2023, according to Reddit’s own statistics, the site had 73 million daily active users, more than 100,000 active communities and had amassed over 16 billion posts and comments. In February 2024, it was the eighth most visited website in the world, ahead of both Amazon and TikTok (see go.nature.com/3tugxbq). And on 20 March, the company floated on the New York Stock Exchange, where it was initially valued at US$6.4 billion. With most researchers now needing to pay to download useful amounts of data on X, Reddit is another option to survey the Internet hivemind. Although changes made last year threaten researchers’ ability to pull data as easily as they once did, Reddit says access to its data continues to be free for non-commercial researchers and academics.

    “As the social-media landscape started changing, we really started thinking about the other spaces besides Twitter that people are using,” says Nicholas Proferes, a social media researcher at Arizona State University’s School of Social and Behavioral Sciences in Phoenix, who co-authored reviews on the use of Reddit for research1,2. Here, Nature reports on how Reddit is providing scientists with continued avenues for connecting with other researchers, gathering data and engaging with the public.

    Networking and collaboration

    Yvette Cendes’s journey on Reddit began in 2014. Cendes, who is currently a postdoctoral scholar at the Harvard–Smithsonian Center for Astrophysics in Cambridge, Massachusetts, found herself with some downtime during her PhD studies in astronomy, and started poking around on the platform. She came across a thread in which users were panicking over how imminent γ-ray bursts from supernovae were going to wreak havoc and kill people — something that she knew to be untrue. She resolved to jump into the comments and clear things up, and this was the start of her science-communication career.

    Since then, Cendes has made a name for herself on Reddit and even created her own subreddit, with nearly 17,000 members. “It’s a very good way to get good knowledge out there,” she says.

    Scientists also use Reddit to get tips and tricks from other scientists. The r/biotech subreddit features news about biotechnology innovations and career advice; r/datascience is a community specifically for data-science professionals. There’s even a subreddit devoted to electron microscopy, from which users can seek guidance on the technology.

    Portrait of of Yvette Cendes

    Yvette Cendes discusses astronomy as a science and a career on Reddit.Credit: Floris Looijesteijn

    Not everyone is as forthcoming with their names and credentials on Reddit, which can make networking a bit more challenging than on other sites, says Cendes. But the pseudoanonymity can also be beneficial. Groups such as r/labrats offer safe spaces for scientists to discuss their research or dilemmas with others of similar backgrounds (and these groups are sometimes used by science journalists looking for article ideas). The anonymity provides some protection for people to post without fear of retaliation, and to seek counsel. In one discussion, for instance, a user laments how their principal investigator published a paper based on their research without giving credit, and considers hiring legal support.

    Reddit can also be a great jumping-off point for early-career scientists or those trying to pivot between specialties. Kevin Ortiz Ceballos, a graduate student at Harvard University’s Department of Astronomy, happened upon one of Cendes’ posts about how to become an astronomer back when he was in secondary school. He credits it with helping him to switch from literature to physics and eventually astrophysics. Engaging in conversations about professional astronomy before entering the field himself was a huge asset.

    “The fact that Yvette made it so accessible gave me the tools I needed to take the necessary steps to study and prepare what I needed to get into astronomy grad school,” he says. The two have since connected in person, and even collaborated on a project that was recently submitted for publication.

    With all of its subspaces, Reddit can be overwhelming at first. Cendes encourages potential users to take it slowly, find the communities they are most interested in and go from there — putting keywords in the search function and perusing the different subreddits that come up.

    Research and analysis

    The information embedded in posts and comments from Reddit’s millions of users can also be a treasure trove for researchers studying online behaviours. In 2022, NASA collaborated with master’s students at the University of British Columbia in Vancouver, Canada, to use Reddit data to locate landslides (see go.nature.com/3tlum6t). The team scraped the site for mentions of ‘landslide’, before analysing and validating relevant mentions to add to the NASA landslides database. According to the team, this verification was needed because a Reddit post about the song ‘Landslide’ by the rock band Fleetwood Mac might “give us insight about the changes and challenges of life, but it doesn’t do much for global disaster detection”.

    TikTok for physics: influencers aim to spark interest in science

    A 2021 review2 in Social Media + Society, co-authored by Proferes, chronicled 727 manuscripts published between 2010 and 2020, that made use of Reddit data. These studies spanned all sorts of disciplines — from computer science to medicine to social science.

    One reason that Reddit is ripe for research is that there are few bureaucratic hurdles to clear compared with what’s required for other studies involving human beings. “It is a publicly accessible web forum in the US and so is not considered to be human-subjects research,” says Proferes. Institutional review boards view Reddit research as “exempt from ethical review”, he says.

    However, Proferes and his co-authors emphasize the need for intentionality and sensitivity when collecting data from the site. Consider a subreddit such as r/opiates. Data on substance use are often difficult to procure from in-person interviews or other social science methods, but because of Reddit’s anonymity, people are more open to sharing such information on the platform. However, using the subreddit for research could be seen as invasive by a community that considers itself a semi-private anonymous support network. Certain communities on Reddit are also wary of scientific researchers.

    The 2024 review co-authored by Proferes1 lists some of these considerations and suggests steps such as obfuscating usernames in published work and collaborating with moderators.

    “Academia and data populations have a very sore history of, frankly, academics coming in and just taking,” says Proferes. The online community “is not getting any benefit whatsoever. It is very exploitative. There’s some real historical reasons, too, why folks may be highly suspicious or dubious about researchers coming in, even in these digital spaces.”

    Portrait of Sarah Gilbert

    Research findings derived from Reddit posts should be shared with users, says Sarah Gilbert.Credit: Steven Shea

    “It’s really easy when you’re working with these large data sets to just think of the data points in them as literal data,” says Sarah Gilbert, research director of the Citizens and Technology Lab at Cornell University in Ithaca, New York, and a co-author of the review. “Spending time in the community and learning the norms and actually reading it, it turns that data into people. It gives a better sense of who is going to be included, more like human-subject research.”

    Gilbert also recommends sharing whatever published research comes out of trawling through Reddit data with those who provided the information. “Hopefully what you learnt is beneficial to the community so they can see data is used for something,” she says.

    Connecting with non-scientists

    Reddit can be a way for scientists to use their expertise to answer any questions the general public might have, says Cendes. She is a regular on r/space, educating users about topics such as the James Webb Space Telescope.

    Kelly Zimmerman, a PhD candidate in ecology at Montclair State University in New Jersey, has connected with and educated other users on Reddit. When she started on the platform about 12 years ago, she mostly used it to find journal articles of interest on r/ecology and r/biology. But, like Cendes, she noticed how curious users were about scientific topics that were in her area of expertise, and she now often engages in discussions on subreddits such as r/whatisthisbug.

    Thousands of scientists are cutting back on Twitter, seeding angst and uncertainty

    Although she previously used X, Zimmerman thinks that Reddit provides a more engaging experience. “I felt like I was just talking into a void — there wasn’t a lot of response on Twitter,” she says.

    One way for scientists to try their hand at science communication on Reddit is through ‘ask me anything’ (AMA) sessions, in which researchers answer users’ questions in their own time. Moderators pull in verified researchers to provide responses — even renowned theoretical physicist Stephen Hawking participated. (To schedule an AMA with r/askscience, you can e-mail the moderators.)

    With both AMAs and general discussion forums, there is an art to making sure that information is communicated effectively and succinctly. “We’re trying to keep it as scientific as possible, but in layman’s terms, so that non-scientists can understand cutting-edge science that’s coming out right now,” says Zimmerman, who also moderates some science subreddits.

    Nathan Allen, a synthetic chemist based in Milwaukee, Wisconsin, and a former moderator at r/science, likens it to writing a persuasive e-mail. “On Reddit, you have got to convince the general public that this has some general interest to them, and you’ve got to develop it and build the message and make sure people stay on point,” he says. “You get a lot of practice writing concise explanations of complicated things that people who aren’t necessarily scientists are able to digest and understand.”

    When using Reddit in any capacity, Zimmerman encourages scientists to make sure to read the rules before making a post or comment, and to mind their manners, just as they would on any other social-media platform. “Be polite,” she says. “Just because you’re an anonymous username doesn’t mean you should be rude to other people.”

    Jennifer Cole, a biologist and anthropologist at Royal Holloway University of London, notes that using Reddit for scientific communication is not without its problems. Moderators do a lot of work behind the scenes and often face a torrent of abuse for trying to maintain standards, says Cole. And although using people’s real names can help with credibility, it can also make academics and experts targets for harassment and abuse. Although the site does not provide support for users who experience abuse, a spokesperson for Reddit noted that the platform has policies to prohibit both harassment and the sharing of personal or confidential information, and that these policies are enforced by the internal safety teams.

    It can also be used to spread falsehoods. R/conspiracy has repeatedly posted misinformation about COVID-19 and vaccines. Climate deniers are also present on the platform, although a decade ago the science forum specifically banned climate change deniers. Asked about misinformation, the Reddit spokesperson said that because Reddit is governed by upvotes and downvotes, quality and accurate information tend to rise to the top.

    Interviewees agree that Reddit is at its core a social media platform, and social media has the potential to be toxic. But when scientists engage, there’s also a lot of great scientific communication and debunking of misinformation. “Don’t be afraid to talk to the people,” Zimmerman says. Those “who are not scientists are just as curious as we are. There’s nothing special about being a scientist. We are like everybody else, and sometimes folks forget that.”

    [ad_2]

    Source link

  • A global timekeeping problem postponed by global warming

    [ad_1]

  • McCarthy, D. D. & Seidelmann, P. K. Time: From Earth Rotation to Atomic Physics (Cambridge Univ. Press, 2018).

  • Levine, J., Tavella, P. & Milton, M. Towards a consensus on a continuous coordinated universal time. Metrologia 60, 014001 (2023).

    Article 
    ADS 

    Google Scholar     

  • Nelson, R. A. et al. The leap second: its history and possible future. Metrologia 38, 509–529 (2001).

    Article 
    ADS 

    Google Scholar     

  • Loomis, B. D., Rachlin, K. E. & Luthcke, S. B. Improved Earth oblateness rate reveals increased ice sheet losses and mass-driven sea level rise. Geophys. Res. Lett. 46, 6910–6917 (2019).

    Article 
    ADS 

    Google Scholar     

  • Cheng, M. & Ries, J. C20 and C30 variations from SLR for GRACE/GRACE-FO science applications. J. Geophys. Res. Solid Earth 128, e2022JB025459 (2023).

    Article 
    ADS 

    Google Scholar     

  • Lombardi, M. A., Novick, A. N., Neville-Neil, G. & Cooke, B. Accurate, traceable, and verifiable time synchronization for world financial markets. J. Res. Natl Inst. Stand. Technol. 121, 436–463 (2016).

  • Addomine, M. in A General History of Horology (eds Turner, A. et al.) 137–152 (Oxford Univ. Press, 2022).

  • Glennie, P. & Thrift, N. Shaping the Day: A History of Timekeeping in England and Wales 1300–1800 (Oxford Univ. Press, 2009).

  • Kinns, R. Visual time signals for mariners between their introduction and 1947: a new perspective. J. Astron. Hist. Herit. 25, 601–713 (2022).

    Article 
    ADS 

    Google Scholar     

  • Ellis, W. Lecture on the Greenwich system of time signals. Horol. J. 7, 85–91, 97–102, 109–114, 121–124 (1865).


    Google Scholar     

  • Nye, J. & Rooney, D. in A General History of Horology (eds Turner, A. et al.) 495–531 (Oxford Univ. Press, 2022).

  • Nelson, G. K., Lombardi, M. A. & Okayama, D. T. NIST time and frequency radio stations: Www, WWVH, and WWVB. NIST Special Publication 250-67 (U.S. Department of Commerce, 2005).

  • Agnew, D. C. Time marks and clock corrections: a century of seismological timekeeping. Seismol. Res. Let. 91, 1417–1429 (2020).

    Article 

    Google Scholar     

  • Bullard, E. C. An atomic standard of frequency and time interval: definition of the second of time. Nature 176, 282 (1955).

    Article 
    ADS 

    Google Scholar     

  • Guinot, B. & Arias, E. F. Atomic time-keeping from 1955 to the present. Metrologia 42, S20–S30 (2005).

    Article 

    Google Scholar     

  • Leschiutta, S. The definition of the ‘atomic’ second. Metrologia 42, S10–S19 (2005).

    Article 
    ADS 

    Google Scholar     

  • Munk, W. H. & McDonald, G. The Rotation of the Earth: A Geophysical Discussion (Cambridge Univ. Press, 1960).

  • Ray, R. D. & Erofeeva, S. Y. Long-period tidal variations in the length of day. J. Geophys. Res. 119, 1498–1509 (2014).

    Article 
    ADS 

    Google Scholar     

  • Gross, R. S. in Treatise on Geophysics: Geodesy (ed. Herring, T. A.) 215–261 (Elsevier, 2015).

  • Haigh, I. D. et al. The tides they are a-changin’: a comprehensive review of past and future nonastronomical changes in tides, their driving mechanisms, and future implications. Rev. Geophys. 58, e2018RG000636 (2020).

    Article 
    ADS 

    Google Scholar     

  • Williams, J. G. & Boggs, D. H. Secular tidal changes in lunar orbit and Earth rotation. Celest. Mech. Dyn. Astron. 126, 89–129 (2016).

    Article 
    ADS 
    MathSciNet 

    Google Scholar     

  • Whitehouse, P. L. Glacial isostatic adjustment modelling: historical perspectives, recent advances, and future directions. Earth Surf. Dynam. 6, 401–429 (2018).

    Article 
    ADS 

    Google Scholar     

  • Peltier, W. R., Wu, P. P.-C., Argus, D. F., Li, T. & Velay-Vitow, J. Glacial isostatic adjustment: physical models and observational constraints. Rep. Prog. Phys. 85, 096801 (2022).

    Article 
    ADS 
    MathSciNet 

    Google Scholar     

  • Mitrovica, J. et al. Reconciling past changes in Earth’s rotation with 20th century global sea-level rise: Resolving Munk’s enigma. Sci. Adv. 1, e1500679 (2015).

    Article 
    ADS 
    PubMed 

    Google Scholar     

  • Kim, A. J. et al. Ice age effects on the satellite-derived \({\dot{J}}_{2}\) datum: mapping the sensitivity to 3D variations in mantle viscosity. Earth Planet. Sci. Lett. 581, 117372 (2022).

    Article 
    CAS 

    Google Scholar     

  • Seago, J. H. in Requirements for UTC and Civil Timekeeping on Earth (eds Seago, J. H. et al.) 107–125 (Univelt, American Astronautical Society, 2013).

  • Cheng, M., Tapley, B. & Ries, J. Deceleration in the Earth’s oblateness. J. Geophys. Res. Solid Earth 118, 740–747 (2013).

    Article 
    ADS 

    Google Scholar     

  • Lau, H. C. P. et al. Inferences of mantle viscosity based on ice age data sets: Radial structure. J. Geophys. Res. Solid Earth 121, 6991–7012 (2016).

    Article 
    ADS 

    Google Scholar     

  • Mitrovica, J. X. & Peltier, W. R. Present-day secular variations in the zonal harmonics of Earth’s geopotential. J. Geophys. Res. Solid Earth 98, 4509–4526 (1993).

    Article 

    Google Scholar     

  • Cox, C. M. & Chao, B. F. Detection of a large-scale mass redistribution in the terrestrial system since 1998. Science 297, 831–833 (2002).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Roy, K. & Peltier, W. R. GRACE era secular trends in Earth rotation parameters: a global scale impact of the global warming process? Geophys. Res. Lett. L10306 (2011).

  • Fox-Kemper, B. et al. in Climate Change 2021: The Physical Science Basis. Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) 1211–1362 (Cambridge Univ. Press, 2021).

  • Barnoud, A. et al. Revisiting the global mean ocean mass budget over 2005–2020. Ocean Sci. 19, 321–334 (2023).

    Article 
    ADS 

    Google Scholar     

  • Wilson, C. The Hill-Brown Theory of the Moon’s Motion: Its Coming-to-Be and Short-Lived Ascendancy (1877–1984) (Springer, 2010).

  • Kono, M. (ed.) Treatise on Geophysics, Vol. 5: Geomagnetism (series ed. Schubert, G.) (Elsevier, 2015).

  • Olson, P. (ed.) Treatise on Geophysics, Vol. 8: Core Dynamics (series ed. Schubert, G.) (Elsevier, 2015).

  • Langbein, J. Methods for rapidly estimating velocity precision from GNSS time series in the presence of temporal correlation: A new method and comparison of existing methods. J. Geophys. Res. Solid Earth 125, e2019JB019132 (2020).

    Article 
    ADS 

    Google Scholar     

  • Constable, C. & Constable, S. A grand spectrum of the geomagnetic field. Phys. Earth Planet. Inter. 344, 107090 (2023).

    Article 

    Google Scholar     

  • Stephenson, F. R., Morrison, L. V. & Hohenkerk, C. Y. Measurement of the Earth’s rotation: 720 BC to AD 2015. Proc. R. Soc. A 472, 20160404 (2016).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar     

  • Huber, P. J. Modeling the length of day and extrapolating the rotation of the Earth. J. Geod. 80, 283–303 (2006).

    Article 
    ADS 

    Google Scholar     

  • Morrison, L. V., Stephenson, F. R., Hohenkerk, C. Y. & Zawilski, M. Addendum 2020 to ‘Measurement of the Earth’s rotation: 720 BC to AD 2015’. Proc. R. Soc. A 477, 20200776 (2021).

    Article 
    ADS 

    Google Scholar     

  • Bell, S. A., Bangert, J. A. & Kaplan, G. H. in The History of Celestial Navigation: Rise of the Royal Observatory and Nautical Almanacs (eds Seidelmann, P. K. & Hohenkerk, C. Y.) 263–311 (Springer, 2020).

  • Burnicki, M. in Requirements for UTC and Civil Timekeeping on Earth (eds Seago, J. H. et al.) 35–46 (Univelt, American Astronautical Society, 2013).

  • Seidelmann, P. K. & Seago, J. H. Time scales, their users, and leap seconds. Metrologia 48, S186–S194 (2011).

    Article 
    ADS 

    Google Scholar     

  • Seago, J. H., Seaman, R. L., Seidelmann, P. K. & Allen, S. L. (eds) Requirements for UTC and Civil Timekeeping on Earth (Univelt, American Astronautical Society, 2013).

  • Birth, K. in Law and Time (eds Benyon-Jones, S. M. & Grabham, E.) 196–211 (Routledge, 2018).

  • Cleveland, R. B., Cleveland, W. S., McRae, J. E. & Terpenning, I. STL: a seasonal-trend decomposition procedure based on loess. J. Off. Stat. 6, 3–73 (1990).


    Google Scholar     

  • Johnson, H. & Agnew, D. C. Monument motion and measurements of crustal velocities. Geophys. Res. Lett. 22, 2905–2908 (1995).

    Article 
    ADS 

    Google Scholar     

[ad_2]

Source link

  • AI image generators often give racist and sexist results: can they be fixed?

    AI image generators often give racist and sexist results: can they be fixed?

    [ad_1]

    A conceptual illustration featuring a collage of faces.

    Illustration by Ada Zielińska

    In 2022, Pratyusha Ria Kalluri, a graduate student in artificial intelligence (AI) at Stanford University in California, found something alarming in image-generating AI programs. When she prompted a popular tool for ‘a photo of an American man and his house’, it generated an image of a pale-skinned person in front of a large, colonial-style home. When she asked for ‘a photo of an African man and his fancy house’, it produced an image of a dark-skinned person in front of a simple mud house — despite the word ‘fancy’.

    After some digging, Kalluri and her colleagues found that images generated by the popular tools Stable Diffusion, released by the firm Stability AI, and DALL·E, from OpenAI, overwhelmingly resorted to common stereotypes, such as associating the word ‘Africa’ with poverty, or ‘poor’ with dark skin tones. The tools they studied even amplified some biases. For example, in images generated from prompts asking for photos of people with certain jobs, the tools portrayed almost all housekeepers as people of colour and all flight attendants as women, and in proportions that are much greater than the demographic reality (see ‘Amplified stereotypes’)1. Other researchers have found similar biases across the board: text-to-image generative AI models often produce images that include biased and stereotypical traits related to gender, skin colour, occupations, nationalities and more.

    Amplified stereotypes. Chart showing the difference between self-identification of people working in different professions and AI model output.

    Source: Ref. 1

    Perhaps this is unsurprising, given that society is full of such stereotypes. Studies have shown that images used by media outlets2, global health organizations3 and Internet databases such as Wikipedia4often have biased representations of gender and race. AI models are being trained on online pictures that are not only biased but that also sometimes contain illegal or problematic imagery, such as photographs of child abuse or non-consensual nudity. They shape what the AI creates: in some cases, the images created by image generators are even less diverse than the results of a Google image search, says Kalluri. “I think lots of people should find that very striking and concerning.”

    This problem matters, researchers say, because the increasing use of AI to generate images will further exacerbate stereotypes. Although some users are generating AI images for fun, others are using them to populate websites or medical pamphlets. Critics say that this issue should be tackled now, before AI becomes entrenched. Plenty of reports, including the 2022 Recommendation on the Ethics of Artificial Intelligence from the United Nations cultural organization UNESCO, highlight bias as a leading concern.

    Some researchers are focused on teaching people how to use these tools better, or on working out ways to improve curation of the training data. But the field is rife with difficulty, including uncertainty about what the ‘right’ outcome should be. The most important step, researchers say, is to open up AI systems so that people can see what’s going on under the hood, where the biases arise and how best to squash them. “We need to push for open sourcing. If a lot of the data sets are not open source, we don’t even know what problems exist,” says Abeba Birhane, a cognitive scientist at the Mozilla Foundation in Dublin.

    Make me a picture

    Image generators first appeared in 2015, when researchers built alignDRAW, an AI model that could generate blurry images based on text input5. It was trained on a data set containing around 83,000 images with captions. Today, a swathe of image generators of varying abilities are trained on data sets containing billions of images. Most tools are proprietary, and the details of which images are fed into these systems are often kept under wraps, along with exactly how they work.

    An AI-generated image showing a Black man in a long tunic with a disconnected leg standing in front of a small mud hut with a grass roof

    This image, generated from a prompt for “an African man and his fancy house”, shows some of the typical associations between ‘African’ and ‘poverty’ in many generated images.Credit: P. Kalluri et al. generated using Stable Diffusion XL

    In general, these generators learn to connect attributes such as colour, shape or style to various descriptors. When a user enters a prompt, the generator builds new visual depictions on the basis of attributes that are close to those words. The results can be both surprisingly realistic and, often, strangely flawed (hands sometimes have six fingers, for example).

    The captions on these training images — written by humans or automatically generated, either when they are first uploaded to the Internet or when data sets are put together — are crucial to this process. But this information is often incomplete, selective and thus biased itself. A yellow banana, for example, would probably be labelled simply as ‘a banana’, but a description for a pink banana would be likely to include the colour. “The same thing happens with skin colour. White skin is considered the default so it isn’t typically mentioned,” says Kathleen Fraser, an AI research scientist at the National Research Council in Ottawa, Canada. “So the AI models learn, incorrectly in this case, that when we use the phrase ‘skin colour’ in our prompts, we want dark skin colours,” says Fraser.

    The difficulty with these AI systems is that they can’t just leave out ambiguous or problematic details in their generated images. “If you ask for a doctor, they can’t leave out the skin tone,” says Kalluri. And if a user asks for a picture of a kind person, the AI system has to visualize that somehow. “How they fill in the blanks leaves a lot of room for bias to creep in,” she says. This is a problem that is unique to image generation — by contrast, an AI text generator could create a language-based description of a doctor without ever mentioning gender or race, for instance; and for a language translator, the input text would be sufficient.

    Do it yourself

    One commonly proposed approach to generating diverse images is to write better prompts. For instance, a 2022 study found that adding the phrase “if all individuals can be [X], irrespective of gender” to a prompt helps to reduce gender bias in the images produced6.

    But this doesn’t always work as intended. A 2023 study by Fraser and her colleagues found that such intervention sometimes exacerbated biases7. Adding the phrase “if all individuals can be felons irrespective of skin colour”, for example, shifted the results from mostly dark-skinned people to all dark-skinned people. Even explicit counter-prompts can have unintended effects: adding the word ‘white’ to a prompt for ‘a poor person’, for example, sometimes resulted in images in which commonly associated features of whiteness, such as blue eyes, were added to dark-skinned faces.

    An AI-generated image in a photo-realistic style showing a white man in a white doctor's coat sitting beside three Black children

    In a Lancet study of global health images, the prompt “Black African doctor is helping poor and sick white children, photojournalism” produced this image, which reproduced the ‘white saviour’ trope they were explicitly trying to counteract.Credit: A. Alenichev et al. generated using Midjourney

    Another common fix is for users to direct results by feeding in a handful of images that are more similar to what they’re looking for. The generative AI program Midjourney, for instance, allows users to add image URLs in the prompt. “But it really feels like every time institutions do this they are really playing whack-a-mole,” says Kalluri. “They are responding to one very specific kind of image that people want to have produced and not really confronting the underlying problem.”

    These solutions also unfairly put the onus on the users, says Kalluri, especially those who are under-represented in the data sets. Furthermore, plenty of users might not be thinking about bias, and are unlikely to pay to run multiple queries to get more-diverse imagery. “If you don’t see any diversity in the generated images, there’s no financial incentive to run it again,” says Fraser.

    Some companies say they add something to their algorithms to help counteract bias without user intervention: OpenAI, for example, says that DALL·E2 uses a “new technique” to create more diversity from prompts that do not specify race or gender. But it’s unclear how such systems work and they, too, could have unintended impacts. In early February, Google released an image generator that had been tuned to avoid some typical image-generator pitfalls. A media frenzy ensued when user prompts requesting a picture of a ‘1943 German soldier’ created images of Black and Asian Nazis — a diverse but historically inaccurate result. Google acknowledged the mistake and temporarily stopped its generator creating images of people.

    Data clean-up

    Alongside such efforts lie attempts to improve curation of training data sets, which is time-consuming and expensive for those containing billions of images. That means companies resort to automated filtering mechanisms to remove unwanted data.

    AI-generated images and video are here: how could they shape research?

    However, automated filtering based on keywords doesn’t catch everything. Researchers including Birhane have found, for example, that benign keywords such as ‘daughter’ and ‘nun’ have been used to tag sexually explicit images in some cases, and that images of schoolgirls are sometimes tagged with terms searched for by sexual predators8. And filtering, too, can have unintended effects. For example, automated attempts to clean large, text-based data sets have removed a disproportionate amount of content created by and for individuals from minority groups9. And OpenAI discovered that its broad filters for sexual and violent imagery in DALL·E2 had the unintended effect of creating a bias against the generation of images of women, because women were disproportionately represented in those images.

    The best curation “requires human involvement”, says Birhane. But that’s slow and expensive, and looking at many such images takes a deep emotional toll, as she well knows. “Sometimes it just gets too much.”

    Independent evaluations of the curation process are impeded by the fact that these data sets are often proprietary. To help overcome this problem, LAION, a non-profit organization in Hamburg, Germany, has created publicly available machine-learning models and data sets that link to images and their captions, in an attempt to replicate what goes on behind the closed doors of AI companies. “What they are doing by putting together the LAION data sets is giving us a glimpse into what data sets inside big corporations and companies like OpenAI look like,” says Birhane. Although intended for research use, these data sets have been used to train models such as Stable Diffusion.

    Citations show gender bias — and the reasons are surprising

    Researchers have learnt from interrogating LAION data that bigger isn’t always better. AI researchers often assume that the bigger the training data set, the more likely that biases will disappear, says Birhane. “People often claim that scale cancels out noise,” she says. “In fact, the good and the bad don’t balance out.” In a 2023 study, Birhane and her team compared the data set LAION-400M, which has 400 million image links, with LAION-2B-en, which has 2 billion, and found that hate content in the captions increased by around 12% in the larger data set10, probably because more low-quality data had slipped through.

    An investigation by another group found that the LAION-5B data set contained child sexual abuse material. Following this, LAION took down the data sets. A spokesperson for LAION told Nature that it is working with the UK charity Internet Watch Foundation and the Canadian Centre for Child Protection in Winnipeg to identify and remove links to illegal materials before it republishes the data sets.

    Open or shut

    If LAION is bearing the brunt of some bad press, that’s perhaps because it’s one of the few open data sources. “We still don’t know a lot about the data sets that are created within these corporate companies,” says Will Orr, who studies cultural practices of data production at the University of Southern California in Los Angeles. “They say that it’s to do with this being proprietary knowledge, but it’s also a way to distance themselves from accountability.”

    In response to Nature’s questions about which measures are in place to remove harmful or biased content from DALL·E’s training data set, OpenAI pointed to publicly available reports that outline its work to reduce gender and racial bias, without providing exact details on how that’s accomplished. Stability AI and Midjourney did not respond to Nature’s e-mails.

    Orr interviewed some data set creators from technology companies, universities and non-profit organizations, including LAION, to understand their motivations and the constraints. “Some of these creators had feelings that they were not able to present all the limitations of the data sets,” he says, because that might be perceived as critical weaknesses that undermine the value of their work.

    How journals are fighting back against a wave of questionable images

    Specialists feel that the field still lacks standardized practices for annotating their work, which would help to make it more open to scrutiny and investigation. “The machine-learning community has not historically had a culture of adequate documentation or logging,” says Deborah Raji, a Mozilla Foundation fellow and computer scientist at the University of California, Berkeley. In 2018, AI ethics researcher Timnit Gebru — a strong proponent of responsible AI and co-founder of the community group Black in AI — and her team released a datasheet to standardize the documentation process for machine-learning data sets11. The datasheet has more than 50 questions to guide documentation about the content, collection process, filtering, intended uses and more.

    The datasheet “was a really critical intervention”, says Raji. Although many academics are increasingly adopting such documentation practices, there’s no incentive for companies to be open about their data sets. Only regulations can mandate this, says Birhane.

    One example is the European Union’s AI Act, which was endorsed by the European Parliament on 13 March. Once it becomes law, it will require that developers of high-risk AI systems provide technical documentation, including datasheets describing the training data and techniques, as well as details about the expected output quality and potential discriminatory impacts, among other information. But which models will come under the high-risk classification remains unclear. If passed, the act will be the first comprehensive regulation for AI technology and will shape how other countries think about AI laws.

    Specialists such as Birhane, Fraser and others think that explicit and well-informed regulations will push companies to be more cognizant of how they build and release AI tools. “A lot of the policy focus for image-generation work has been oriented around minimizing misinformation, misrepresentation and fraud through the use of these images, and there has been very little, if any, focus on bias, functionality or performance,” says Raji.

    Even with a focus on bias, however, there’s still the question of what the ideal output of AI should be, researchers say — a social question with no simple answer. “There is not necessarily agreement on what the so-called right answer should look like,” says Fraser. Do we want our AI systems to reflect reality, even if the reality is unfair? Or should it represent characteristics such as gender and race in an even-handed, 50:50 way? “Someone has to decide what that distribution should be,” she says.

    [ad_2]

    Source link

  • Why scientists trust AI too much — and what to do about it

    Why scientists trust AI too much — and what to do about it

    [ad_1]

    A robotic arm moves through an automated AI-run laboratory

    AI-run labs have arrived — such as this one in Suzhou, China.Credit: Qilai Shen/Bloomberg/Getty

    Scientists of all stripes are embracing artificial intelligence (AI) — from developing ‘self-driving’ laboratories, in which robots and algorithms work together to devise and conduct experiments, to replacing human participants in social-science experiments with bots1.

    Many downsides of AI systems have been discussed. For example, generative AI such as ChatGPT tends to make things up, or ‘hallucinate’ — and the workings of machine-learning systems are opaque.

    Artificial intelligence and illusions of understanding in scientific research

    In a Perspective article2 published in Nature this week, social scientists say that AI systems pose a further risk: that researchers envision such tools as possessed of superhuman abilities when it comes to objectivity, productivity and understanding complex concepts. The authors argue that this put researchers in danger of overlooking the tools’ limitations, such as the potential to narrow the focus of science or to lure users into thinking they understand a concept better than they actually do.

    Scientists planning to use AI “must evaluate these risks now, while AI applications are still nascent, because they will be much more difficult to address if AI tools become deeply embedded in the research pipeline”, write co-authors Lisa Messeri, an anthropologist at Yale University in New Haven, Connecticut, and Molly Crockett, a cognitive scientist at Princeton University in New Jersey.

    The peer-reviewed article is a timely and disturbing warning about what could be lost if scientists embrace AI systems without thoroughly considering such hazards. It needs to be heeded by researchers and by those who set the direction and scope of research, including funders and journal editors. There are ways to mitigate the risks. But these require that the entire scientific community views AI systems with eyes wide open.

    ChatGPT is a black box: how AI research can break it open

    To inform their article, Messeri and Crockett examined around 100 peer-reviewed papers, preprints, conference proceedings and books, published mainly over the past five years. From these, they put together a picture of the ways in which scientists see AI systems as enhancing human capabilities.

    In one ‘vision’, which they call AI as Oracle, researchers see AI tools as able to tirelessly read and digest scientific papers, and so survey the scientific literature more exhaustively than people can. In both Oracle and another vision, called AI as Arbiter, systems are perceived as evaluating scientific findings more objectively than do people, because they are less likely to cherry-pick the literature to support a desired hypothesis or to show favouritism in peer review. In a third vision, AI as Quant, AI tools seem to surpass the limits of the human mind in analysing vast and complex data sets. In the fourth, AI as Surrogate, AI tools simulate data that are too difficult or complex to obtain.

    Informed by anthropology and cognitive science, Messeri and Crockett predict risks that arise from these visions. One is the illusion of explanatory depth3, in which people relying on another person — or, in this case, an algorithm — for knowledge have a tendency to mistake that knowledge for their own and think their understanding is deeper than it actually is.

    How to stop AI deepfakes from sinking society — and science

    Another risk is that research becomes skewed towards studying the kinds of thing that AI systems can test — the researchers call this the illusion of exploratory breadth. For example, in social science, the vision of AI as Surrogate could encourage experiments involving human behaviours that can be simulated by an AI — and discourage those on behaviours that cannot, such as anything that requires being embodied physically.

    There’s also the illusion of objectivity, in which researchers see AI systems as representing all possible viewpoints or not having a viewpoint. In fact, these tools reflect only the viewpoints found in the data they have been trained on, and are known to adopt the biases found in those data. “There’s a risk that we forget that there are certain questions we just can’t answer about human beings using AI tools,” says Crockett. The illusion of objectivity is particularly worrying given the benefits of including diverse viewpoints in research.

    Avoid the traps

    If you’re a scientist planning to use AI, you can reduce these dangers through a number of strategies. One is to map your proposed use to one of the visions, and consider which traps you are most likely to fall into. Another approach is to be deliberate about how you use AI. Deploying AI tools to save time on something your team already has expertise in is less risky than using them to provide expertise you just don’t have, says Crockett.

    Journal editors receiving submissions in which use of AI systems has been declared need to consider the risks posed by these visions of AI, too. So should funders reviewing grant applications, and institutions that want their researchers to use AI. Journals and funders should also keep tabs on the balance of research they are publishing and paying for — and ensure that, in the face of myriad AI possibilities, their portfolios remain broad in terms of the questions asked, the methods used and the viewpoints encompassed.

    All members of the scientific community must view AI use not as inevitable for any particular task, nor as a panacea, but rather as a choice with risks and benefits that must be carefully weighed. For decades, and long before AI was a reality for most people, social scientists have studied AI. Everyone — including researchers of all kinds — must now listen.

    [ad_2]

    Source link

  • More than 2 million research papers have disappeared from the Internet

    More than 2 million research papers have disappeared from the Internet

    [ad_1]

    Old documents and books stored on shelves in a library's archive.

    A study identified more than two million articles that did not appear in a major digital archive, despite having an active DOI.Credit: Anna Berkut/Alamy

    More than one-quarter of scholarly articles are not being properly archived and preserved, a study of more than seven million digital publications suggests. The findings, published in the Journal of Librarianship and Scholarly Communication on 24 January1, indicate that systems to preserve papers online have failed to keep pace with the growth of research output.

    “Our entire epistemology of science and research relies on the chain of footnotes,” explains author Martin Eve, a researcher in literature, technology and publishing at Birkbeck, University of London. “If you can’t verify what someone else has said at some other point, you’re just trusting to blind faith for artefacts that you can no longer read yourself.”

    Eve, who is also involved in research and development at digital-infrastructure organization Crossref, checked whether 7,438,037 works labelled with digital object identifiers (DOIs) are held in archives. DOIs — which consist of a string of numbers, letters and symbols — are unique fingerprints used to identify and link to specific publications, such as scholarly articles and official reports. Crossref is the largest DOI registration agency, allocating the identifiers to about 20,000 members, including publishers, museums and other institutions.

    The sample of DOIs included in the study was made up of a random selection of up to 1,000 registered to each member organization. Twenty-eight percent of these works — more than two million articles — did not appear in a major digital archive, despite having an active DOI. Only 58% of the DOIs referenced works that had been stored in at least one archive. The other 14% were excluded from the study because they were published too recently, were not journal articles or did not have an identifiable source.

    Preservation challenge

    Eve notes that the study has limitations: namely that it tracked only articles with DOIs, and that it did not search every digital repository for articles (he did not check whether items with a DOI were stored in institutional repositories, for example).

    Nevertheless, preservation specialists have welcomed the analysis. “It’s been hard to know the real extent of the digital preservation challenge faced by e-journals,” says William Kilbride, managing director of the Digital Preservation Coalition, headquartered in York, UK. The coalition publishes a handbook detailing good preservation practice.

    “Many people have the blind assumption that if you have a DOI, it’s there forever,” says Mikael Laakso, who studies scholarly publishing at the Hanken School of Economics in Helsinki. “But that doesn’t mean that the link will always work.” In 2021, Laakso and his colleagues reported2 that more than 170 open-access journals had disappeared from the Internet between 2000 and 2019.

    Kate Wittenberg, managing director of the digital archiving service Portico in New York City, warns that small publishers are at higher risk of failing to preserve articles than are large ones. “It costs money to preserve content,” she says, adding that archiving involves infrastructure, technology and expertise that many smaller organizations do not have access to.

    Eve’s study suggests some measures that could improve digital preservation, including stronger requirements at DOI registration agencies and better education and awareness of the issue among publishers and researchers.

    “Everybody thinks of the immediate gains they might get from having a paper out somewhere, but we really should be thinking about the long-term sustainability of the research ecosystem,” Eve says. “After you’ve been dead for 100 years, are people going to be able to get access to the things you’ve worked on?”

    [ad_2]

    Source link

  • Gender bias is more exaggerated in online images than in text

    Gender bias is more exaggerated in online images than in text

    [ad_1]

    • NEWS AND VIEWS

    A big-data analysis shows that men are starkly over-represented in online images, and that gender bias is stronger in images compared with text. Such images could influence enduring gender biases in our offline lives.

    [ad_2]

    Source link