Counting Crimes: An Obsolete Paradigm

Curated on Posted on by Stefaan Verhulst

Paul Wormeli at The Criminologist: “To the extent that a paradigm is defined as the way we view things, the crime statistics paradigm in the United States is inadequate and requires reinvention….The statement—”not all crime is reported to the police”—lies at the very heart of why our current crime data are inherently incomplete. It is a direct reference to the fact that not all “street crime” is reported and that state and local law enforcement are not the only entities responsible for overseeing violations of societally established norms (“street crime” or otherwise). Two significant gaps exist, in that: 1) official reporting of crime from state and local law enforcement agencies cannot provide insight into unreported incidents, and 2) state and local law enforcement may not have or acknowledge jurisdiction over certain types of matters, such as cybercrime, corruption, environmental crime, or terrorism, and therefore cannot or do not report on those incidents…

All of these gaps in crime reporting mask the portrait of crime in the U.S. If there was a complete accounting of crime that could serve as the basis of policy formulation, including the distribution of federal funds to state and local agencies, there could be a substantial impact across the nation. Such a calculation would move the country toward a more rational basis for determining federal support for communities based on a comprehensive measure of community wellness.

In its deliberations, the NAS Panel recognized that it is essential to consider both the concepts of classification and the rules of counting as we seek a better and more practical path to describing crime in the U.S. and its consequences. The panel postulated that a meaningful classification of incidents found to be crimes would go beyond the traditional emphasis on street crime and include all crime categories.

The NAS study identified the missing elements of a national crime report as including more complete data on crimes involving drugrelated offenses, criminal acts where juveniles are involved, so-called white-collar crimes such as fraud and corruption, cybercrime, crime against businesses, environmental crimes, and crimes against animals. Just as one example, it is highly unlikely that we will know the full extent of fraudulent claims against all federal, state, and local governments in the face of the massive influx of funding from recent and forthcoming Congressional action.

In proposing a set of crime classifications, the NAS panel recommended 11 major categories, 5 of which are not addressed in our current crime data collection systems. While there are parallel data systems that collect some of the missing data within these five crime categories, it remains unclear which federal agency, if any, has the authority to gather the information and aggregate it to give us anywhere near a complete estimate of crime in the United States. No federal or national entity has the assignment of estimating the total amount of crime that takes place in the United States. Without such leadership, we are left with an uninformed understanding of the health and wellness of communities throughout the country…(More)”

Artificial intelligence searches for the human touch

Curated on Posted on by Stefaan Verhulst

Madhumita Murgia at the Financial Times: “For many outside the tech world, “data” means soulless numbers. Perhaps it causes their eyes to glaze over with boredom. Whereas for computer scientists, data means rows upon rows of rich raw matter, there to be manipulated.

Yet the siren call of “big data” has been more muted recently. There is a dawning recognition that, in tech such as artificial intelligence, “data” equals human beings.

AI-driven algorithms are increasingly impinging upon our everyday lives. They assist in making decisions across a spectrum that ranges from advertising products to diagnosing medical conditions. It’s already clear that the impact of such systems cannot be understood simply by examining the underlying code or even the data used to build them. We must look to people for answers as well.

Two recent studies do exactly that. The first is an Ipsos Mori survey of more than 19,000 people across 28 countries on public attitudes to AI, the second a University of Tokyo study investigating Japanese people’s views on the morals and ethics of AI usage. By inviting those with lived experiences to participate, both capture the mood among those researching the impact of artificial intelligence.

The Ipsos Mori survey found that 60 per cent of adults expect that products and services using AI will profoundly change their daily lives in the next three to five years. Latin Americans in particular think AI will trigger changes in social needs such as education and employment, while Chinese respondents were most likely to believe it would change transportation and their homes.

The geographic and demographic differences in both surveys are revealing. Globally, about half said AI technology has more benefits than drawbacks, while two-thirds felt gloomy about its impact on their individual freedom and legal rights. But figures for different countries show a significant split within this. Citizens from the “global south”, a catch-all term for non-western countries, were much more likely to “have a positive outlook on the impact of AI-powered products and services in their lives”. Large majorities in China (76 per cent) and India (68 per cent) said they trusted AI companies. In contrast, only 35 per cent in the UK, France and US expressed similar trust.

In the University of Tokyo study, researchers discovered that women, older people and those with more subject knowledge were most wary of the risks of AI, perhaps an indicator of their own experiences with these systems. The Japanese mathematician Noriko Arai has, for instance, written about sexist and gender stereotypes encoded into “female” carer and receptionist robots in Japan.

The surveys underline the importance of AI designers recognising that we don’t all belong to one homogenous population, with the same understanding of the world. But they’re less insightful about why differences exist….(More)”.

Tech is finally killing long lines

Curated on Posted on by Stefaan Verhulst

Erica Pandey at Axios: “Startups and big corporations alike are releasing technology to put long lines online.

Why it matters: Standing in lines has always been a hassle, but the pandemic has made lines longer, slower and even dangerous. Now many of those lines are going virtual.

What’s happening: Physical lines are disappearing at theme parks, doctor’s offices, clothing stores and elsewhere, replaced by systems that let you book a slot online and then wait to be notified that it’s your turn.

Whyline, an Argentinian company that was just acquired by the biometric ID company CLEAR, is an app that lets users do just that — it will keep you up to date on your wait time and let you know when you need to show up.

  • Whyline’s list of clients — mostly in Latin America — includes banks, retail stores, the city of Lincoln, Nebraska, and Los Angeles International Airport.
  • “The same way you make a reservation at a restaurant, Whyline software does the waiting for you in banks, in DMVs, in airports,” CLEAR CEO Caryn Seidman-Becker said on CNBC.

Another app called Safe Queue was born from the pandemic and aims to make in-store shopping safer for customers and workers by spacing out shoppers’ visits.

  • The app uses GPS technology to detect when you’re within 1,000 feet of a participating store and automatically puts you in a virtual line. Then you can wait in your car or somewhere nearby until it’s your turn to shop.

Many health clinics around the country are also putting their COVID test lines online..

The rub: While virtual queuing tech may be gaining ground, lines are still more common than not. And in the age of social distancing, expect wait times to remain high and lines to remain long…(More)”.

Privacy Is Power: How Tech Policy Can Bolster Democracy

Curated on Posted on by Stefaan Verhulst

Essay by Andrew Imbrie, Daniel Baer, Andrew Trask, Anna Puglisi, Erik Brattberg, and Helen Toner: “…History is rarely forgiving, but as we adopt the next phase of digital tools, policymakers can avoid the errors of the past. Privacy-enhancing technologies, or PETs, are a collection of technologies with applications ranging from improved medical diagnostics to secure voting systems and messaging platforms. PETs allow researchers to harness big data to solve problems affecting billions of people while also protecting privacy. …

PETs are ripe for coordination among democratic allies and partners, offering a way for them to jointly develop standards and practical applications that benefit the public good. At an AI summit last July, U.S. Secretary of State Antony Blinken noted the United States’ interest in “increasing access to shared public data sets for AI training and testing, while still preserving privacy,” and National Security Adviser Jake Sullivan pointed to PETs as a promising area “to overcome data privacy challenges while still delivering the value of big data.” Given China’s advantages in scale, the United States and like-minded partners should foster emerging technologies that play to their strengths in medical research and discovery, energy innovation, trade facilitation, and reform around money laundering. Driving innovation and collaboration within and across democracies is important not only because it will help ensure those societies’ success but also because there will be a first-mover advantage in the adoption of PETs for governing the world’s private data–sharing networks.

Accelerating the development of PETs for the public good will require an international approach. Democratic governments will not be the trendsetters on PETs; instead, policymakers for these governments should focus on nurturing the ecosystems these technologies need to flourish. The role for policymakers is not to decide the fate of specific protocols or techniques but rather to foster a conducive environment for researchers to experiment widely and innovate responsibly.    

Democracies should identify shared priorities and promote basic research to mature the technological foundations of PETs. The underlying technologies require greater investment in algorithmic development and hardware to optimize the chips and mitigate the costs of network overhead. To support the computational requirements for PETs, for example, the National Science Foundation could create an interface through CloudBank and provide cloud compute credits to researchers without access to these resources. The United States could also help incubate an international network of research universities collaborating on these technologies.

Second, science-funding agencies in democracies should host competitions to incentivize new PETs protocols and standards—the collaboration between the United States and the United Kingdom announced in early December is a good example. The goal should be to create free, open-source protocols and avoid the fragmentation of the market and the proliferation of proprietary standards. The National Institute of Standards and Technology and other similar bodies should develop standards and measurement tools for PETs; governments and companies should form public-private partnerships to fund open-source protocols over the long term. Open-source protocols are especially important in the early days of PET development, because closed-source PET implementations by profit-seeking actors can be leveraged to build data monopolies. For example, imagine a scenario where all U.S. cancer data could be controlled by a single company because all the hospitals are running their proprietary software. And you have to become a customer to join the network…(More)”.

The Attack of Zombie Science

Curated on Posted on by Stefaan Verhulst

Article by Natalia Pasternak, Carlos Orsi, Aaron F. Mertz, & Stuart Firestein: “When we think about how science is distorted, we usually think about concepts that have ample currency in public discourse, such as pseudoscience and junk science. Practices like astrology and homeopathy come wrapped in scientific concepts and jargon that can’t meet the methodological requirements of actual sciences. During the COVID-19 pandemic, pseudoscience has had a field day. Bleach, anyone? Bear bile? Yet the pandemic has brought a newer, more subtle form of distortion to light. To the philosophy of science, we humbly submit a new concept: “zombie science.”

We think of zombie science as mindless science. It goes through the motions of scientific research without a real research question to answer, it follows all the correct methodology, but it doesn’t aspire to contribute to advance knowledge in the field. Practically all the information about hydroxychloroquine during the pandemic falls into that category, including not just the living dead found in preprint repositories, but also papers published in journals that ought to have been caught by a more discerning eye. Journals, after all, invest their reputation in every piece they choose to publish. And every investment in useless science is a net loss.

From a social and historical stance, it seems almost inevitable that the penchant for productivism in the academic and scientific world would end up encouraging zombie science. If those who do not publish perish, then publishing—even nonsense or irrelevancies—is a matter of life or death. The peer-review process and the criteria for editorial importance are filters, for sure, but they are limited. Not only do they get clogged and overwhelmed due to excess submissions, they have to deal with the weaknesses of the human condition, including feelings of personal loyalty, prejudice, and vanity. Additionally, these filters fail, as the proliferation of predatory journals shows us all too well…(More)”.

Making data for good better

Curated on Posted on by Stefaan Verhulst

Article by Caroline Buckee, Satchit Balsari, and Andrew Schroeder: “…Despite the long standing excitement about the potential for digital tools, Big Data and AI to transform our lives, these innovations–with some exceptions–have so far had little impact on the greatest public health emergency of our time.

Attempts to use digital data streams to rapidly produce public health insights that were not only relevant for local contexts in cities and countries around the world, but also available to decision makers who needed them, exposed enormous gaps across the translational pipeline. The insights from novel data streams which could help drive precise, impactful health programs, and bring effective aid to communities, found limited use among public health and emergency response systems. We share here our experience from the COVID-19 Mobility Data Network (CMDN), now Crisis Ready (crisisready.io), a global collaboration of researchers, mostly infectious disease epidemiologists and data scientists, who served as trusted intermediaries between technology companies willing to share vast amounts of digital data, and policy makers, struggling to incorporate insights from these novel data streams into their decision making. Through our experience with the Network, and using human mobility data as an illustrative example, we recognize three sets of barriers to the successful application of large digital datasets for public good.

First, in the absence of pre-established working relationships with technology companies and data brokers, the data remain primarily confined within private circuits of ownership and control. During the pandemic, data sharing agreements between large technology companies and researchers were hastily cobbled together, often without the right kind of domain expertise in the mix. Second, the lack of standardization, interoperability and information on the uncertainty and biases associated with these data, necessitated complex analytical processing by highly specialized domain experts. And finally, local public health departments, understandably unfamiliar with these novel data streams, had neither the bandwidth nor the expertise to sift noise from signal. Ultimately, most efforts did not yield consistently useful information for decision making, particularly in low resource settings, where capacity limitations in the public sector are most acute…(More)”.

Trove of unique health data sets could help AI predict medical conditions earlier

Curated on Posted on by Stefaan Verhulst

Madhumita Murgia at the Financial Times: “…Ziad Obermeyer, a physician and machine learning scientist at the University of California, Berkeley, launched Nightingale Open Science last month — a treasure trove of unique medical data sets, each curated around an unsolved medical mystery that artificial intelligence could help to solve.

The data sets, released after the project received $2m of funding from former Google chief executive Eric Schmidt, could help to train computer algorithms to predict medical conditions earlier, triage better and save lives.

The data include 40 terabytes of medical imagery, such as X-rays, electrocardiogram waveforms and pathology specimens, from patients with a range of conditions, including high-risk breast cancer, sudden cardiac arrest, fractures and Covid-19. Each image is labelled with the patient’s medical outcomes, such as the stage of breast cancer and whether it resulted in death, or whether a Covid patient needed a ventilator.

Obermeyer has made the data sets free to use and mainly worked with hospitals in the US and Taiwan to build them over two years. He plans to expand this to Kenya and Lebanon in the coming months to reflect as much medical diversity as possible.

“Nothing exists like it,” said Obermeyer, who announced the new project in December alongside colleagues at NeurIPS, the global academic conference for artificial intelligence. “What sets this apart from anything available online is the data sets are labelled with the ‘ground truth’, which means with what really happened to a patient and not just a doctor’s opinion.”…

The Nightingale data sets were among dozens proposed this year at NeurIPS.

Other projects included a speech data set of Mandarin and eight subdialects recorded by 27,000 speakers in 34 cities in China; the largest audio data set of Covid respiratory sounds, such as breathing, coughing and voice recordings, from more than 36,000 participants to help screen for the disease; and a data set of satellite images covering the entire country of South Africa from 2006 to 2017, divided and labelled by neighbourhood, to study the social effects of spatial apartheid.

Elaine Nsoesie, a computational epidemiologist at the Boston University School of Public Health, said new types of data could also help with studying the spread of diseases in diverse locations, as people from different cultures react differently to illnesses.

She said her grandmother in Cameroon, for example, might think differently than Americans do about health. “If someone had an influenza-like illness in Cameroon, they may be looking for traditional, herbal treatments or home remedies, compared to drugs or different home remedies in the US.”

Computer scientists Serena Yeung and Joaquin Vanschoren, who proposed that research to build new data sets should be exchanged at NeurIPS, pointed out that the vast majority of the AI community still cannot find good data sets to evaluate their algorithms. This meant that AI researchers were still turning to data that were potentially “plagued with bias”, they said. “There are no good models without good data.”…(More)”.

Economists Pin More Blame on Tech for Rising Inequality

Curated on Posted on by Stefaan Verhulst

Steve Lohr at the New York Times: “Daron Acemoglu, an influential economist at the Massachusetts Institute of Technology, has been making the case against what he describes as “excessive automation.”

The economywide payoff of investing in machines and software has been stubbornly elusive. But he says the rising inequality resulting from those investments, and from the public policy that encourages them, is crystal clear.

Half or more of the increasing gap in wages among American workers over the last 40 years is attributable to the automation of tasks formerly done by human workers, especially men without college degrees, according to some of his recent research…

Mr. Acemoglu, a wide-ranging scholar whose research makes him one of most cited economists in academic journals, is hardly the only prominent economist arguing that computerized machines and software, with a hand from policymakers, have contributed significantly to the yawning gaps in incomes in the United States. Their numbers are growing, and their voices add to the chorus of criticism surrounding the Silicon Valley giants and the unchecked advance of technology.

Paul Romer, who won a Nobel in economic science for his work on technological innovation and economic growth, has expressed alarm at the runaway market power and influence of the big tech companies. “Economists taught: ‘It’s the market. There’s nothing we can do,’” he said in an interview last year. “That’s really just so wrong.”

Anton Korinek, an economist at the University of Virginia, and Joseph Stiglitz, a Nobel economist at Columbia University, have written a paper, “Steering Technological Progress,” which recommends steps from nudges for entrepreneurs to tax changes to pursue “labor-friendly innovations.”

Erik Brynjolfsson, an economist at Stanford, is a technology optimist in general. But in an essay to be published this spring in Daedalus, the journal of the American Academy of Arts and Sciences, he warns of “the Turing trap.” …(More)”

Nudges: Four reasons to doubt popular technique to shape people’s behavior

Curated on Posted on by Stefaan Verhulst

Article by Magda Osman: “Throughout the pandemic, many governments have had to rely on people doing the right thing to reduce the spread of the coronavirus – ranging from social distancing to handwashing. Many enlisted the help of psychologists for advice on how to “nudge” the public to do what was deemed appropriate.

Nudges have been around since the 1940s and originally were referred to as behavioural engineering. They are a set of techniques developed by psychologists to promote “better” behaviour through “soft” interventions rather than “hard” ones (mandates, bans, fines). In other words, people aren’t punished if they fail to follow them. The nudges are based on psychological and behavioural economic research into human behaviour and cognition.

The nudges can involve subtle as well as obvious methods. Authorities may set a “better” choice, such as donating your organs, as a default – so people have to opt out of a register rather than opt in. Or they could make a healthy option more attractive through food labelling.

But, despite the soft approach, many people aren’t keen on being nudged. During the pandemic, for example, scientists examined people’s attitudes to nudging in social and news media in the UK, and discovered that half of the sentiments expressed in social media posts were negative…(More)”.

Technology and the Global Struggle for Democracy

Curated on Posted on by Stefaan Verhulst

Essay by Manuel Muniz: “The commemoration of the first anniversary of the January 6, 2021, attack on the US Capitol by supporters of former President Donald Trump showed that the extreme political polarization that fueled the riot also frames Americans’ interpretations of it. It would, however, be gravely mistaken to view what happened as a uniquely American phenomenon with uniquely American causes. The disruption of the peaceful transfer of power that day was part of something much bigger.

As part of the commemoration, President Joe Biden said that a battle is being fought over “the soul of America.” What is becoming increasingly clear is that this is also true of the international order: its very soul is at stake. China is rising and asserting itself. Populism is widespread in the West and major emerging economies. And chauvinistic nationalism has re-emerged in parts of Europe. All signs point to increasing illiberalism and anti-democratic sentiment around the world.

Against this backdrop, the US hosted in December a (virtual) “Summit for Democracy” that was attended by hundreds of national and civil-society leaders. The message of the gathering was clear: democracies must assert themselves firmly and proactively. To that end, the summit devoted numerous sessions to studying the digital revolution and its potentially harmful implications for our political systems.

Emerging technologies pose at least three major risks for democracies. The first concerns how they structure public debate. Social networks balkanize public discourse by segmenting users into ever smaller like-minded communities. Algorithmically-driven information echo chambers make it difficult to build social consensus. Worse, social networks are not liable for the content they distribute, which means they can allow misinformation to spread on their platforms with impunity…(More)”.