Big Data and the Law of War

Curated on Posted on by Stefaan Verhulst

Essay by Paul Stephan: “Big data looms large in today’s world. Much of the tech sector regards the building up of large sets of searchable data as part (sometimes the greater part) of its business model. Surveillance-oriented states, of which China is the foremost example, use big data to guide and bolster monitoring of their own people as well as potential foreign threats. Many other states are not far behind in the surveillance arms race, notwithstanding the attempts of the European Union to put its metaphorical finger in the dike. Finally, ChatGPT has revived popular interest in artificial intelligence (AI), which uses big data as a means of optimizing the training and algorithm design on which it depends, as a cultural, economic, and social phenomenon. 

If big data is growing in significance, might it join territory, people, and property as objects of international conflict, including armed conflict? So far it has not been front and center in Russia’s invasion of Ukraine, the war that currently consumes much of our attention. But future conflicts could certainly feature attacks on big data. China and Taiwan, for example, both have sophisticated technological infrastructures that encompass big data and AI capabilities. The risk that they might find themselves at war in the near future is larger than anyone would like. What, then, might the law of war have to say about big data? More generally, if existing law does not meet our needs,  how might new international law address the issue?

In a recent essay, part of an edited volume on “The Future Law of Armed Conflict,” I argue that big data is a resource and therefore a potential target in an armed conflict. I address two issues: Under the law governing the legality of war (jus ad bellum), what kinds of attacks on big data might justify an armed response, touching off a bilateral (or multilateral) armed conflict (a war)? And within an existing armed conflict, what are the rules (jus in bello, also known as international humanitarian law, or IHL) governing such attacks?

The distinction is meaningful. If cyber operations rise to the level of an armed attack, then the targeted state has, according to Article 51 of the U.N. Charter, an “inherent right” to respond with armed force. Moreover, the target need not confine its response to a symmetrical cyber operation. Once attacked, a state may use all forms of armed force in response, albeit subject to the restrictions imposed by IHL. If the state regards, say, a takedown of its financial system as an armed attack, it may respond with missiles…(More)”.

Ready, set, share: Researchers brace for new data-sharing rules

Curated on Posted on by Stefaan Verhulst

Jocelyn Kaiser and Jeffrey Brainard in Science: “…By 2025, new U.S. requirements for data sharing will extend beyond biomedical research to encompass researchers across all scientific disciplines who receive federal research funding. Some funders in the European Union and China have also enacted data-sharing requirements. The new U.S. moves are feeding hopes that a worldwide movement toward increased sharing is in the offing. Supporters think it could speed the pace and reliability of science.

Some scientists may only need to make a few adjustments to comply with the policies. That’s because data sharing is already common in fields such as protein crystallography and astronomy. But in other fields the task could be weighty, because sharing is often an afterthought. For example, a study involving 7750 medical research papers found that just 9% of those published from 2015 to 2020 promised to make their data publicly available, and authors of just 3% actually shared, says lead author Daniel Hamilton of the University of Melbourne, who described the finding at the International Congress on Peer Review and Scientific Publication in September 2022. Even when authors promise to share their data, they often fail to follow through. Out of 21,000 journal articles that included data-sharing plans, a study published in PLOS ONE in 2020 found, fewer than 21% provided links to the repository storing the data.

Journals and funders, too, have a mixed record when it comes to supporting data sharing. Research presented at the September 2022 peer-review congress found only about half of the 110 largest public, corporate, and philanthropic funders of health research around the world recommend or require grantees to share data…

“Health research is the field where the ethical obligation to share data is the highest,” says Aidan Tan, a clinician-researcher at the University of Sydney who led the study. “People volunteer in clinical trials and put themselves at risk to advance medical research and ultimately improve human health.”

Across many fields of science, researchers’ support for sharing data has increased during the past decade, surveys show. But given the potential cost and complexity, many are apprehensive about the NIH policy, and other requirements to follow. “How we get there is pretty messy right now,” says Parker Antin, a developmental biologist and associate vice president for research at the University of Arizona. “I’m really not sure whether the total return will justify the cost. But I don’t know of any other way to find out than trying to do it.”

Science offers this guide as researchers prepare to plunge in….(More)”.

How can health data be used for public benefit? 3 uses that people agree on

Curated on Posted on by Stefaan Verhulst

Article by Alison Papricia et al: “Health data can include information about health-care services, health status and behaviours, medications and genetic data, in addition to demographic information like age, education and neighbourhood.

These facts and statistics are valuable because they offer insights and information about population health and well-being. However, they can also be sensitive, and there are legitimate public concerns about how these data are used, and by whom. The term “social licence” describes uses of health data that have public support.

Studies performed in Canada, the United Kingdom and internationally have all found public support and social licence for uses of health data that produce public benefits.

However, this support is conditional. Public concerns related to privacy, commercial motives, equity and fairness must be addressed.

Our team of health policy researchers set out to build upon prior studies with actionable advice from a group of 20 experienced public and patient advisers. Studies have shown that health data use, sharing and reuse is a complex topic. So we recruited people who already had some knowledge of potential uses of health data through their roles advising research institutions, hospitals, community organizations and governments.

We asked these experienced advisers to exchange views about uses of health data that they supported or opposed. We also gathered participants’ views about requirements for social licence, such as privacy, security and transparency.

Consensus views: After hours of facilitated discussion and weeks of reflection, all 20 participants agreed on some applications and uses of health data that are within social licence, and some that are not.

Participants agreed it is within social licence for health data to be used by:

  • health-care practitioners — to directly improve the health-care decisions and services provided to a patient.
  • governments, health-care facilities and health-system administrators — to understand and improve health care and the health-care system.
  • university-based researchers — to understand the drivers of disease and well-being.

Participants agreed that it is not within social licence for:

  • an individual or organization to sell (or re-sell) another person’s identified health data.
  • health data to be used for a purpose that has no patient, public or societal benefit.

Points of disagreement: Among other topics, the participants discussed uses of health data about systemically marginalized populations and companies using health data. Though some participants saw benefits from both practices, there was not consensus support for either.

For example, participants were concerned that vulnerable populations could be exploited, and that companies would put profit ahead of public benefits. Participants also worried that if harms were done by companies or to marginalized populations, they could not be “undone.” Several participants expressed skepticism about whether risks could be managed, even if additional safeguards are in place.

The participants also had different views about what constitutes an essential requirement for social licence. This included discussions about benefits, governance, patient consent and involvement, equity, privacy and transparency.

Collectively, they generated a list of 85 essential requirements, but 38 of those requirements were only seen as essential by one person. There were also cases where some participants actively opposed a requirement that another participant thought was essential…(More)”

Social media is too important to be so opaque with its data

Curated on Posted on by Stefaan Verhulst

Article by Alex González Ormerod: “Over 50 people were killed by the police during demonstrations in Peru. Brazil is reeling from a coup attempt in its capital city. The residents of Culiacán, a city in northern Mexico, still cower in their houses after the army swooped in to arrest a cartel kingpin. Countries across Latin America have kicked off the year with turmoil. 

It is almost a truism to say that the common factor in these events has been the role of social media. Far-right radicals in Brazil were seen to be openly organizing and spreading fake news about electoral fraud on Twitter. Peruvians used TikTok to bear witness to police brutality, preserving it for posterity.

Dealing with the aftermath of the crises, in Culiacán, Sinaloans shared crucial info as to where roadblocks continued to burn, and warned about shootouts in certain neighborhoods. Brazilians opened up Instagram and other social channels to compile photos and other evidence that might help the police bring the Brasília rioters to justice.

These events could be said to have happened online as much as they did offline, yet we know next to nothing about the inner workings of the platforms they occurred on.

People covering these platforms face a common refrain: After reaching out for basic social media data, they will often get a reply saying, “Unfortunately we do not have the information you need at this time.” (This particular quote came from Alberto de Golin, a PR agency representative for TikTok Mexico)…(More)”

How Smart Are the Robots Getting?

Curated on Posted on by Stefaan Verhulst

Cade Metz at The New York Times: “…These are not systems that anyone can properly evaluate with the Turing test — or any other simple method. Their end goal is not conversation.

Researchers at Google and DeepMind, which is owned by Google’s parent company, are developing tests meant to evaluate chatbots and systems like DALL-E, to judge what they do well, where they lack reason and common sense, and more. One test shows videos to artificial intelligence systems and asks them to explain what has happened. After watching someone tinker with an electric shaver, for instance, the A.I. must explain why the shaver did not turn on.

These tests feel like academic exercises — much like the Turing test. We need something that is more practical, that can really tell us what these systems do well and what they cannot, how they will replace human labor in the near term and how they will not.

We could also use a change in attitude. “We need a paradigm shift — where we no longer judge intelligence by comparing machines to human behavior,” said Oren Etzioni, professor emeritus at the University of Washington and founding chief executive of the Allen Institute for AI, a prominent lab in Seattle….

At the same time, there are many ways these bots are superior to you and me. They do not get tired. They do not let emotion cloud what they are trying to do. They can instantly draw on far larger amounts of information. And they can generate text, images and other media at speeds and volumes we humans never could.

Their skills will also improve considerably in the coming years.

Researchers can rapidly hone these systems by feeding them more and more data. The most advanced systems, like ChatGPT, require months of training, but over those months, they can develop skills they did not exhibit in the past.

“We have found a set of techniques that scale effortlessly,” said Raia Hadsell, senior director of research and robotics at DeepMind. “We have a simple, powerful approach that continues to get better and better.”

The exponential improvement we have seen in these chatbots over the past few years will not last forever. The gains may soon level out. But even then, multimodal systems will continue to improve — and master increasingly complex skills involving images, sounds and computer code. And computer scientists will combine these bots with systems that can do things they cannot. ChatGPT failed Turing’s chess test. But we knew in 1997 that a computer could beat the best humans at chess. Plug ChatGPT into a chess program, and the hole is filled.

In the months and years to come, these bots will help you find information on the internet. They will explain concepts in ways you can understand. If you like, they will even write your tweets, blog posts and term papers.

They will tabulate your monthly expenses in your spreadsheets. They will visit real estate websites and find houses in your price range. They will produce online avatars that look and sound like humans. They will make mini-movies, complete with music and dialogue…

Certainly, these bots will change the world. But the onus is on you to be wary of what these systems say and do, to edit what they give you, to approach everything you see online with skepticism. Researchers know how to give these systems a wide range of skills, but they do not yet know how to give them reason or common sense or a sense of truth.

That still lies with you…(More)”.

Why Europe must embrace participatory policymaking

Curated on Posted on by Stefaan Verhulst

Article by Alberto Alemanno, Claire Davenport, and Laura Batalla: “Today, Europe faces many threats – from economic uncertainty and war on its eastern borders to the rise of illiberal democracies and popular reactionary politicians.

As Europe recovers from the pandemic and grapples with economic and social unrest, it is at an inflection point; it can either create new spaces to build trust and a sense of shared purpose between citizens and governments, or it can continue to let its democratic institutions erode and distrust grow. 

The scale of such problems requires novel problem-solving and new perspectives, including those from civil society and citizens. Increased opportunities for citizens to engage with policymakers can lend legitimacy and accountability to traditionally ‘opaque’ policymaking processes. The future of the bloc hinges on its ability to not only sustain democratic institutions but to do so with buy-in from constituents.

Yet policymaking in the EU is often understood as a technocratic process that the public finds difficult, if not impossible, to navigate. The Spring 2022 Eurobarometer found that just 53% of respondents believed their voice counts in the EU. The issue is compounded by a lack of political literacy coupled with a dearth of channels for participation or co-creation. 

In parallel, there is a strong desire from citizens to make their voices heard. A January 2022 Special Eurobarometer on the Future of Europe found that 90% of respondents agreed that EU citizens’ voices should be taken more into account during decision-making. The Russian war in Ukraine has strengthened public support for the EU as a whole. According to the Spring 2022 Eurobarometer, 65% of Europeans view EU membership as a good thing. 

This is not to say that the EU has no existing models for citizen engagement. The European Citizens Initiative – a mechanism for petitioning the Commission to propose new laws – is one example of existing infrastructure. There is also an opportunity to build on the success of The Conference on the Future of Europe, a gathering held this past spring that gave citizens the opportunity to contribute policy recommendations and justifications alongside traditional EU policymakers…(More)”

The Autocrat in Your iPhone

Curated on Posted on by Stefaan Verhulst

Article by Ronald J. Deibert: “In the summer of 2020, a Rwandan plot to capture exiled opposition leader Paul Rusesabagina drew international headlines. Rusesabagina is best known as the human rights defender and U.S. Presidential Medal of Freedom recipient who sheltered more than 1,200 Hutus and Tutsis in a hotel during the 1994 Rwandan genocide. But in the decades after the genocide, he also became a prominent U.S.-based critic of Rwandan President Paul Kagame. In August 2020, during a layover in Dubai, Rusesabagina was lured under false pretenses into boarding a plane bound for Kigali, the Rwandan capital, where government authorities immediately arrested him for his affiliation with an opposition group. The following year, a Rwandan court sentenced him to 25 years in prison, drawing the condemnation of international human rights groups, the European Parliament, and the U.S. Congress. 

Less noted at the time, however, was that this brazen cross-border operation may also have employed highly sophisticated digital surveillance. After Rusesabagina’s sentencing, Amnesty International and the Citizen Lab at the University of Toronto, a digital security research group I founded and direct, discovered that smartphones belonging to several of Rusesabagina’s family members who also lived abroad had been hacked by an advanced spyware program called Pegasus. Produced by the Israel-based NSO Group, Pegasus gives an operator near-total access to a target’s personal data. Forensic analysis revealed that the phone belonging to Rusesabagina’s daughter Carine Kanimba had been infected by the spyware around the time her father was kidnapped and again when she was trying to secure his release and was meeting with high-level officials in Europe and the U.S. State Department, including the U.S. special envoy for hostage affairs. NSO Group does not publicly identify its government clients and the Rwandan government has denied using Pegasus, but strong circumstantial evidence points to the Kagame regime.

In fact, the incident is only one of dozens of cases in which Pegasus or other similar spyware technology has been found on the digital devices of prominent political opposition figures, journalists, and human rights activists in many countries. Providing the ability to clandestinely infiltrate even the most up-to-date smartphones—the latest “zero click” version of the spyware can penetrate a device without any action by the user—Pegasus has become the digital surveillance tool of choice for repressive regimes around the world. It has been used against government critics in the United Arab Emirates (UAE) and pro-democracy protesters in Thailand. It has been deployed by Mohammed bin Salman’s Saudi Arabia and Viktor Orban’s Hungary…(More)”.

Recentring the demos in the measurement of democracy

Curated on Posted on by Stefaan Verhulst

Article by Seema Shah: “Rethinking how we measure and evaluate democratic performance is vital to reversing a longstanding negative trend in global democracy. We must confront the past, including democracy’s counter-intuitively intrinsic inequality. This is key to revitalising institutions in a way that allows democratic practice to live up to its potential…

In the global democracy assessment space, teams like the one I lead at International IDEA compete to provide the most rigorous, far-reaching and understandable set of democracy measurements in the world. Alexander Hudson explains how critical these indicators are, providing important benchmarks for democratic growth and decline to policymakers, governments, international organisations, and journalists.

Yet in so many ways, the core of what these datasets measure and help assess are largely the same. This redundancy is no doubt at least partially a product of wealthy donors’ prioritisation of liberal democracy as an ideal. It is compounded by how the measures are calculated. As Adam Przeworksi recently stated, reliance on expert coders runs the risk of measuring little other than those experts’ biases.

But if that is the case, and quantitative measurements continue to be necessary for democracy assessment, shouldn’t we rethink exactly what we are measuring and how we are measuring it?..

Democracy assessment indices do not typically measure ordinary people’s evaluations of the state of democracy. Instead, other specialised ‘barometers’ often take on this task. See, for example, Afrobarometer, Eurobarometer, Asian Barometer, and Latinobarometro. Surveys of public perceptions on a range of issues also exist, including, but not limited to democracy. The problem is, however, that these do not systematically make it into overall democracy assessments or onto policymakers’ desks. This means that policymakers and others do not consistently prioritise or consider lived experiences as they make decisions about democracy and human rights-related funding and interventions…(More)”.

How games can make behavioural science better

Curated on Posted on by Stefaan Verhulst

Article by Bria Long et al: “When US cognitive scientist Joshua Hartshorne was investigating how people around the world learn English, he needed to get tens of thousands of people to take a language test. He designed ‘Which English?’, a grammar game that presented a series of tough word problems and then guessed where in the world the player learnt the language. Participants shared their results — whether accurate or not — on social media, creating a snowball effect for recruitment. The findings, based on data from almost 670,000 people, revealed that there is a ‘critical period’ for second-language learning that extends into adolescence.

This sort of ‘gamification’ is becoming a powerful research tool across fields that study humans, including psychology, neuroscience, economics and behavioural economics. By making research fun, the approach can help experiments to reach thousands or millions of participants. For instance, experiments embedded in a video game demonstrated that the layout of the city where a child lives shapes their future navigational ability. Data from a digital word search showed that people who are skilled at the game do not necessarily give better advice to those trying to learn it. And a dilemma game involving millions of people revealed that most individuals have reliable moral intuition.

Gamification can help to avoid the pitfalls of conventional laboratory-based experiments by allowing researchers to study diverse populations, to conduct more-sophisticated experiments and to observe human behaviour in naturalistic environments. It can improve statistical power and reproducibility, making research more robust. Technical advances are making gamification cheaper and more straightforward, and the COVID-19 pandemic has forced many labs to move their human experiments online. But despite these changes, most have not yet embraced the opportunities gamification affords.

To reach the full potential of this approach, researchers must dispel misconceptions, develop new gamification technologies, improve access to existing ones and apply the methods to productive research questions. We are researchers in psychology, linguistics, developmental science, data science and music who have run our own gamified experiments. We think it’s time for science to get serious about games…(More)”.

Building Trust with the Algorithms in Our Lives

Curated on Posted on by Stefaan Verhulst

Essay by Dylan Walsh: “Algorithms are omnipresent in our increasingly digital lives. They offer us new music and friends. They recommend books and clothing. They deliver information about the world. They help us find romantic partners one day, efficient commutes the next, cancer diagnoses the third.

And yet most people display an aversion to algorithms. They don’t fully trust the recommendations made by computer programs. When asked, they prefer human predictions to those put forward by algorithms.

“But given the growing prevalence of algorithms, it seems important we learn to trust and appreciate them,” says Taly Reich, associate professor at Yale SOM. “Is there an intervention that would help reduce this aversion?”

New research conducted by Reich and two colleagues, Alex Kaju of HEC Montreal and Sam Maglio of the University of Toronto, finds that clearly demonstrating an algorithm’s ability to learn from past mistakes increases the trust that people place in the algorithm. It also inclines people to prefer the predictions made by algorithms over those made by humans.

In arriving at this result, Reich drew on her foundational work on the value of mistakes. In a series of prior papers, Reich has established how mistakes, in the right context, can create benefits; people who make mistakes can come across as more knowledgeable and credible than people who don’t. Applying this insight to predictive models, Reich and her colleagues investigated whether framing algorithms as capable of learning from their mistakes enhanced trust in the recommendations that algorithms make.

In one of several experiments, for instance, participants were asked whether a trained psychologist or an algorithm would be better at evaluating somebody’s personality. Under one condition, no further information was provided. In another condition, identical performance data for both the psychologist and the algorithm explicitly demonstrated improvement over time. In the first three months, each one was correct 60% of the time, incorrect 40% of the time; by six months, they were correct 70% of the time; and over the course of the first year the rate moved up to 80% correct.

Absent information about the capacity to learn, participants chose a psychologist over an algorithm 75% of the time. But when shown how the algorithm improved over time, they chose it 66% of the time—more often than the human. Participants overcame any potential algorithm aversion and instead expressed what Reich and her colleagues term “algorithm appreciation,” or even “algorithm investment,” by choosing it at a higher rate than the human. These results held across several different cases, from selecting the best artwork to finding a well-matched romantic partner. In every instance, when the algorithm exhibited learning over time, it was trusted more often than its human counterpart…(More)”