Use of AI in social sciences could mean humans will no longer be needed in data collection

Curated on Posted on by Stefaan Verhulst

Article by Michael Lee: A team of researchers from four Canadian and American universities say artificial intelligence could replace humans when it comes to collecting data for social science research.

Researchers from the University of Waterloo, University of Toronto, Yale University and the University of Pennsylvania published an article in the journal Science on June 15 about how AI, specifically large language models (LLMs), could affect their work.

“AI models can represent a vast array of human experiences and perspectives, possibly giving them a higher degree of freedom to generate diverse responses than conventional human participant methods, which can help to reduce generalizability concerns in research,” Igor Grossmann, professor of psychology at Waterloo and a co-author of the article, said in a news release.

Philip Tetlock, a psychology professor at UPenn and article co-author, goes so far as to say that LLMs will “revolutionize human-based forecasting” in just three years.

In their article, the authors pose the question: “How can social science research practices be adapted, even reinvented, to harness the power of foundational AI? And how can this be done while ensuring transparent and replicable research?”

The authors say the social sciences have traditionally relied on methods such as questionnaires and observational studies.

But with the ability of LLMs to pore over vast amounts of text data and generate human-like responses, the authors say this presents a “novel” opportunity for researchers to test theories about human behaviour at a faster rate and on a much larger scale.

Scientists could use LLMs to test theories in a simulated environment before applying them in the real world, the article says, or gather differing perspectives on a complex policy issue and generate potential solutions.

“It won’t make sense for humans unassisted by AIs to venture probabilistic judgments in serious policy debates. I put an 90 per cent chance on that,” Tetlock said. “Of course, how humans react to all of that is another matter.”

One issue the authors identified, however, is that LLMs often learn to exclude sociocultural biases, raising the question of whether models are correctly reflecting the populations they study…(More)”

Three approaches to re-design digital public spaces 

Curated on Posted on by Stefaan Verhulst

Article by  Gianluca Sgueo: “The underlying tenet of so-called “human centred-design” is a public administration capable of delivering a satisfactory (even gratifying) digital experience to every user. Public services, however, are still marked by severe qualitative asymmetries, both nationally and supranationally. In this article we discuss the key shortcomings of digital public spaces, and we explore three approaches to re-design such spaces with the aim to widen the existing gaps separating the ideal from the actual rendering of human-centred digital government…(More)”.

Better Government Tech Is Possible

Curated on Posted on by Stefaan Verhulst

Article by Beth Noveck: “In the first four months of the Covid-19 pandemic, government leaders paid $100 million for management consultants at McKinsey to model the spread of the coronavirus and build online dashboards to project hospital capacity.

It’s unsurprising that leaders turned to McKinsey for help, given the notorious backwardness of government technology. Our everyday experience with online shopping and search only highlights the stark contrast between user-friendly interfaces and the frustrating inefficiencies of government websites—or worse yet, the ongoing need to visit a government office to submit forms in person. The 2016 animated movie Zootopia depicts literal sloths running the DMV, a scene that was guaranteed to get laughs given our low expectations of government responsiveness.

More seriously, these doubts are reflected in the plummeting levels of public trust in government. From early Healthcare.gov failures to the more recent implosions of state unemployment websites, policymaking without attention to the technology that puts the policy into practice has led to disastrous consequences.

The root of the problem is that the government, the largest employer in the US, does not keep its employees up-to-date on the latest tools and technologies. When I served in the Obama White House as the nation’s first deputy chief technology officer, I had to learn constitutional basics and watch annual training videos on sexual harassment and cybersecurity. But I was never required to take a course on how to use technology to serve citizens and solve problems. In fact, the last significant legislation about what public professionals need to know was the Government Employee Training Act, from 1958, well before the internet was invented.

In the United States, public sector awareness of how to use data or human-centered design is very low. Out of 400-plus public servants surveyed in 2020, less than 25 percent received training in these more tech-enabled ways of working, though 70 percent said they wanted such training…(More)”.

Why picking citizens at random could be the best way to govern the A.I. revolution

Curated on Posted on by Stefaan Verhulst

Article by Hélène Landemore, Andrew Sorota, and Audrey Tang: “Testifying before Congress last month about the risks of artificial intelligence, Sam Altman, the OpenAI CEO behind the massively popular large language model (LLM) ChatGPT, and Gary Marcus, a psychology professor at NYU famous for his positions against A.I. utopianism, both agreed on one point: They called for the creation of a government agency comparable to the FDA to regulate A.I. Marcus also suggested scientific experts should be given early access to new A.I. prototypes to be able to test them before they are released to the public.

Strikingly, however, neither of them mentioned the public, namely the billions of ordinary citizens around the world that the A.I. revolution, in all its uncertainty, is sure to affect. Don’t they also deserve to be included in decisions about the future of this technology?

We believe a global, democratic approach–not an exclusively technocratic one–is the only adequate answer to what is a global political and ethical challenge. Sam Altman himself stated in an earlier interview that in his “dream scenario,” a global deliberation involving all humans would be used to figure out how to govern A.I.

There are already proofs of concept for the various elements that a global, large-scale deliberative process would require in practice. By drawing on these diverse and complementary examples, we can turn this dream into a reality.

Deliberations based on random selection have grown in popularity on the local and national levels, with close to 600 cases documented by the OECD in the last 20 years. Their appeal lies in capturing a unique array of voices and lived experiences, thereby generating policy recommendations that better track the preferences of the larger population and are more likely to be accepted. Famous examples include the 2012 and 2016 Irish citizens’ assemblies on marriage equality and abortion, which led to successful referendums and constitutional change, as well as the 2019 and 2022 French citizens’ conventions on climate justice and end-of-life issues.

Taiwan has successfully experimented with mass consultations through digital platforms like Pol.is, which employs machine learning to identify consensus among vast numbers of participants. Digitally engaged participation has helped aggregate public opinion on hundreds of polarizing issues in Taiwan–such as regulating Uber–involving half of its 23.5 million people. Digital participation can also augment other smaller-scale forms of citizen deliberations, such as those taking place in person or based on random selection…(More)”.

How existential risk became the biggest meme in AI

Curated on Posted on by Stefaan Verhulst

Article by Will Douglas Heaven: “Who’s afraid of the big bad bots? A lot of people, it seems. The number of high-profile names that have now made public pronouncements or signed open letters warning of the catastrophic dangers of artificial intelligence is striking.

Hundreds of scientists, business leaders, and policymakers have spoken up, from deep learning pioneers Geoffrey Hinton and Yoshua Bengio to the CEOs of top AI firms, such as Sam Altman and Demis Hassabis, to the California congressman Ted Lieu and the former president of Estonia Kersti Kaljulaid.

The starkest assertion, signed by all those figures and many more, is a 22-word statement put out two weeks ago by the Center for AI Safety (CAIS), an agenda-pushing research organization based in San Francisco. It proclaims: “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.”

The wording is deliberate. “If we were going for a Rorschach-test type of statement, we would have said ‘existential risk’ because that can mean a lot of things to a lot of different people,” says CAIS director Dan Hendrycks. But they wanted to be clear: this was not about tanking the economy. “That’s why we went with ‘risk of extinction’ even though a lot of us are concerned with various other risks as well,” says Hendrycks.

We’ve been here before: AI doom follows AI hype. But this time feels different. The Overton window has shifted. What were once extreme views are now mainstream talking points, grabbing not only headlines but the attention of world leaders. “The chorus of voices raising concerns about AI has simply gotten too loud to be ignored,” says Jenna Burrell, director of research at Data and Society, an organization that studies the social implications of technology.

What’s going on? Has AI really become (more) dangerous? And why are the people who ushered in this tech now the ones raising the alarm?   

It’s true that these views split the field. Last week, Yann LeCun, chief scientist at Meta and joint recipient with Hinton and Bengio of the 2018 Turing Award, called the doomerism “preposterously ridiculous.” Aidan Gomez, CEO of the AI firm Cohere, said it was “an absurd use of our time.”

Others scoff too. “There’s no more evidence now than there was in 1950 that AI is going to pose these existential risks,” says Signal president Meredith Whittaker, who is cofounder and former director of the AI Now Institute, a research lab that studies the social and policy implications of artificial intelligence. “Ghost stories are contagious—it’s really exciting and stimulating to be afraid.”

“It is also a way to skim over everything that’s happening in the present day,” says Burrell. “It suggests that we haven’t seen real or serious harm yet.”…(More)”.

An algorithm intended to reduce poverty in Jordan disqualifies people in need

Curated on Posted on by Stefaan Verhulst

Article by Tate Ryan-Mosley: “An algorithm funded by the World Bank to determine which families should get financial assistance in Jordan likely excludes people who should qualify, according to an investigation published this morning by Human Rights Watch. 

The algorithmic system, called Takaful, ranks families applying for aid from least poor to poorest using a secret calculus that assigns weights to 57 socioeconomic indicators. Applicants say that the calculus is not reflective of reality, however, and oversimplifies people’s economic situation, sometimes inaccurately or unfairly. Takaful has cost over $1 billion, and the World Bank is funding similar projects in eight other countries in the Middle East and Africa. 

Human Rights Watch identified several fundamental problems with the algorithmic system that resulted in bias and inaccuracies. Applicants are asked how much water and electricity they consume, for example, as two of the indicators that feed into the ranking system. The report’s authors conclude that these are not necessarily reliable indicators of poverty. Some families interviewed believed the fact that they owned a car affected their ranking, even if the car was old and necessary for transportation to work. 

The report reads, “This veneer of statistical objectivity masks a more complicated reality: the economic pressures that people endure and the ways they struggle to get by are frequently invisible to the algorithm.”..(More)”.

Systems Thinking, Big Data and Public Policy

Curated on Posted on by Stefaan Verhulst

Article by Mauricio Covarrubias: “Systems thinking and big data analysis are two fundamental tools in the formulation of public policies due to their potential to provide a more comprehensive and evidence-based understanding of the problems and challenges that a society faces.

Systems thinking is important in the formulation of public policies because it allows for a holistic and integrated approach to addressing the complex challenges and issues that a society faces. According to Ilona Kickbusch and David Gleicher, “Addressing wicked problems requires a high level of systems thinking. If there is a single lesson to be drawn from the first decade of the 21st century, it is that surprise, instability and extraordinary change will continue to be regular features of our lives.”

Public policies often involve multiple stakeholders, interrelated factors and unintended consequences, which require a deep understanding of how the system as a whole operates. Systems thinking enables policymakers to identify the key factors that influence a problem and how they relate to each other, enabling them to develop solutions that more effectively address the issues. Instead of trying to address a problem in isolation, systems thinking considers the problem as part of a whole and seeks solutions that address the root causes.

Additionally, systems thinking helps policymakers anticipate the unintended consequences of their decisions and actions. By understanding how different components of the system interact, they can predict the possible side effects of a policy in other areas. This can help avoid decisions that have unintended consequences…(More)”.

Augmented Reality Is Coming for Cities

Curated on Posted on by Stefaan Verhulst

Article by Greg Lindsay: “It’s still early in the metaverse, however — no killer app has yet emerged, and the financial returns on disruption are falling as interest rates rise.

Already, a handful of companies have come forward to partner with cities instead of fighting them. For example, InCitu uses AR to visualize the building envelopes of planned projects in New York City, Buffalo, and beyond in hopes of winning over skeptical communities through seeing-is-believing. The startup recently partnered with Washington, DC’s Department of Buildings to aid its civic engagement efforts. Another of its partners is Snap, the Gen Z social media giant currently currying favor with cities and civic institutions as it pivots to AR for its next act…

For cities to gain the metaverse they want tomorrow, they will need to invest the scarce staff time and resources today. That means building a coalition of the willing among Apple, Google, Niantic, Snap and others; throwing their weight behind open standards through participation in umbrella groups such as the Metaverse Standards Forum; and becoming early, active participants in each of the major platforms in order to steer traffic toward designated testbeds and away from highly trafficked areas.

It’s a tall order for cities grappling with a pandemic crisis, drug-and-mental-health crisis, and climate crisis all at once, but a necessary one to prevent the metaverse (of all things!) from becoming the next one…(More)”.

The A.I. Revolution Will Change Work. Nobody Agrees How.

Curated on Posted on by Stefaan Verhulst

Sarah Kessler in The New York Times: “In 2013, researchers at Oxford University published a startling number about the future of work: 47 percent of all United States jobs, they estimated, were “at risk” of automation “over some unspecified number of years, perhaps a decade or two.”

But a decade later, unemployment in the country is at record low levels. The tsunami of grim headlines back then — like “The Rich and Their Robots Are About to Make Half the World’s Jobs Disappear” — look wildly off the mark.

But the study’s authors say they didn’t actually mean to suggest doomsday was near. Instead, they were trying to describe what technology was capable of.

It was the first stab at what has become a long-running thought experiment, with think tanks, corporate research groups and economists publishing paper after paper to pinpoint how much work is “affected by” or “exposed to” technology.

In other words: If cost of the tools weren’t a factor, and the only goal was to automate as much human labor as possible, how much work could technology take over?

When the Oxford researchers, Carl Benedikt Frey and Michael A. Osborne, were conducting their study, IBM Watson, a question-answering system powered by artificial intelligence, had just shocked the world by winning “Jeopardy!” Test versions of autonomous vehicles were circling roads for the first time. Now, a new wave of studies follows the rise of tools that use generative A.I.

In March, Goldman Sachs estimated that the technology behind popular A.I. tools such as DALL-E and ChatGPT could automate the equivalent of 300 million full-time jobs. Researchers at Open AI, the maker of those tools, and the University of Pennsylvania found that 80 percent of the U.S. work force could see an effect on at least 10 percent of their tasks.

“There’s tremendous uncertainty,” said David Autor, a professor of economics at the Massachusetts Institute of Technology, who has been studying technological change and the labor market for more than 20 years. “And people want to provide those answers.”

But what exactly does it mean to say that, for instance, the equivalent of 300 million full-time jobs could be affected by A. I.?

It depends, Mr. Autor said. “Affected could mean made better, made worse, disappeared, doubled.”…(More)”.

Politicians love to appeal to common sense – but does it trump expertise?

Curated on Posted on by Stefaan Verhulst

Essay by Magda Osman: “Politicians love to talk about the benefits of “common sense” – often by pitting it against the words of “experts and elites”. But what is common sense? Why do politicians love it so much? And is there any evidence that it ever trumps expertise? Psychology provides a clue.

We often view common sense as an authority of collective knowledge that is universal and constant, unlike expertise. By appealing to the common sense of your listeners, you therefore end up on their side, and squarely against the side of the “experts”. But this argument, like an old sock, is full of holes.

Experts have gained knowledge and experience in a given speciality. In which case politicians are experts as well. This means a false dichotomy is created between the “them” (let’s say scientific experts) and “us” (non-expert mouthpieces of the people).

Common sense is broadly defined in research as a shared set of beliefs and approaches to thinking about the world. For example, common sense is often used to justify that what we believe is right or wrong, without coming up with evidence.

But common sense isn’t independent of scientific and technological discoveries. Common sense versus scientific beliefs is therefore also a false dichotomy. Our “common” beliefs are informed by, and inform, scientific and technology discoveries…

The idea that common sense is universal and self-evident because it reflects the collective wisdom of experience – and so can be contrasted with scientific discoveries that are constantly changing and updated – is also false. And the same goes for the argument that non-experts tend to view the world the same way through shared beliefs, while scientists never seem to agree on anything.

Just as scientific discoveries change, common sense beliefs change over time and across cultures. They can also be contradictory: we are told “quit while you are ahead” but also “winners never quit”, and “better safe than sorry” but “nothing ventured nothing gained”…(More)”