Rewiring The Web: The future of personal data

Curated on Posted on by Stefaan Verhulst

Paper by Jon Nash and Charlie Smith: “In this paper, we argue that the widespread use of personal information online represents a fundamental flaw in our digital infrastructure that enables staggeringly high levels of fraud, undermines our right to privacy, and limits competition.Charlie Smith

To realise a web fit for the twenty-first century, we need to fundamentally rethink the ways in which we interact with organisations online.

If we are to preserve the founding values of an open, interoperable web in the face of such profound change, we must update the institutions, regulatory regimes, and technologies that make up this network of networks.

Many of the problems we face stem from the vast amounts of personal information that currently flow through the internet—and fixing this fundamental flaw would have a profound effect on the quality of our lives and the workings of the web…(More)”

Detecting Human Rights Violations on Social Media during Russia-Ukraine War

Curated on Posted on by Stefaan Verhulst

Paper by Poli Nemkova, et al: “The present-day Russia-Ukraine military conflict has exposed the pivotal role of social media in enabling the transparent and unbridled sharing of information directly from the frontlines. In conflict zones where freedom of expression is constrained and information warfare is pervasive, social media has emerged as an indispensable lifeline. Anonymous social media platforms, as publicly available sources for disseminating war-related information, have the potential to serve as effective instruments for monitoring and documenting Human Rights Violations (HRV). Our research focuses on the analysis of data from Telegram, the leading social media platform for reading independent news in post-Soviet regions. We gathered a dataset of posts sampled from 95 public Telegram channels that cover politics and war news, which we have utilized to identify potential occurrences of HRV. Employing a mBERT-based text classifier, we have conducted an analysis to detect any mentions of HRV in the Telegram data. Our final approach yielded an F2 score of 0.71 for HRV detection, representing an improvement of 0.38 over the multilingual BERT base model. We release two datasets that contains Telegram posts: (1) large corpus with over 2.3 millions posts and (2) annotated at the sentence-level dataset to indicate HRVs. The Telegram posts are in the context of the Russia-Ukraine war. We posit that our findings hold significant implications for NGOs, governments, and researchers by providing a means to detect and document possible human rights violations…(More)” See also Data for Peace and Humanitarian Response? The Case of the Ukraine-Russia War

The Prediction Society: Algorithms and the Problems of Forecasting the Future

Curated on Posted on by Stefaan Verhulst

Paper by Hideyuki Matsumi and Daniel J. Solove: “Predictions about the future have been made since the earliest days of humankind, but today, we are living in a brave new world of prediction. Today’s predictions are produced by machine learning algorithms that analyze massive quantities of personal data. Increasingly, important decisions about people are being made based on these predictions.

Algorithmic predictions are a type of inference. Many laws struggle to account for inferences, and even when they do, the laws lump all inferences together. But as we argue in this Article, predictions are different from other inferences. Predictions raise several unique problems that current law is ill-suited to address. First, algorithmic predictions create a fossilization problem because they reinforce patterns in past data and can further solidify bias and inequality from the past. Second, algorithmic predictions often raise an unfalsiability problem. Predictions involve an assertion about future events. Until these events happen, predictions remain unverifiable, resulting in an inability for individuals to challenge them as false. Third, algorithmic predictions can involve a preemptive intervention problem, where decisions or interventions render it impossible to determine whether the predictions would have come true. Fourth, algorithmic predictions can lead to a self-fulfilling prophecy problem where they actively shape the future they aim to forecast.

More broadly, the rise of algorithmic predictions raises an overarching concern: Algorithmic predictions not only forecast the future but also have the power to create and control it. The increasing pervasiveness of decisions based on algorithmic predictions is leading to a prediction society where individuals’ ability to author their own future is diminished while the organizations developing and using predictive systems are gaining greater power to shape the future…(More)”

Digital Freedoms in French-Speaking African Countries

Curated on Posted on by Stefaan Verhulst

Report by AFD: “As digital penetration increases in countries across the African continent, its citizens face growing risks and challenges. Indeed, beyond facilitated access to knowledge such as the online encyclopedia Wikipedia, to leisure-related tools such as Youtube, and to sociability such as social networks, digital technology offers an unprecedented space for democratic expression. 

However, these online civic spaces are under threat. Several governments have enacted vaguely-defined laws, allowing for random arrests.

Several countries have implemented repressive practices restricting freedom of expression and access to information. This is what is known as “digital authoritarianism”, which is on the rise in many countries.

This report takes stock of digital freedoms in 26 French-speaking African countries, and proposes concrete actions to improve citizen participation and democracy…(More)”

From LogFrames to Logarithms – A Travel Log

Curated on Posted on by Stefaan Verhulst

Article by Karl Steinacker and Michael Kubach: “..Today, authorities all over the world are experimenting with predictive algorithms. That sounds technical and innocent but as we dive deeper into the issue, we realise that the real meaning is rather specific: fraud detection systems in social welfare payment systems. In the meantime, the hitherto banned terminology had it’s come back: welfare or social safety nets are, since a couple of years, en vogue again. But in the centuries-old Western tradition, welfare recipients must be monitored and, if necessary, sanctioned, while those who work and contribute must be assured that there is no waste. So it comes at no surprise that even today’s algorithms focus on the prime suspect, the individual fraudster, the undeserving poor.

Fraud detection systems promise that the taxpayer will no longer fall victim to fraud and efficiency gains can be re-directed to serve more people. The true extent of welfare fraud is regularly exaggerated  while the costs of such systems is routinely underestimated. A comparison of the estimated losses and investments doesn’t take place. It is the principle to detect and punish the fraudsters that prevail. Other issues don’t rank high either, for example on how to distinguish between honest mistakes and deliberate fraud. And as case workers spent more time entering and analysing data and in front of a computer screen, the less they have time and inclination to talk to real people and to understand the context of their life at the margins of society.

Thus, it can be said that routinely hundreds of thousands of people are being scored. Example Denmark: Here, a system called Udbetaling Danmark was created in 2012 to streamline the payment of welfare benefits. Its fraud control algorithms can access the personal data of millions of citizens, not all of whom receive welfare payments. In contrast to the hundreds of thousands affected by this data mining, the number of cases referred to the Police for further investigation are minute. 

In the city of Rotterdam in the Netherlands every year, data of 30,000 welfare recipients is investigated in order to flag suspected welfare cheats. However, an analysis of its scoring system based on machine learning and algorithms showed systemic discrimination with regard to ethnicity, age, gender, and parenthood. It revealed evidence of other fundamental flaws making the system both inaccurate and unfair. What might appear to a caseworker as a vulnerability is treated by the machine as grounds for suspicion. Despite the scale of data used to calculate risk scores, the output of the system is not better than random guesses. However, the consequences of being flagged by the “suspicion machine” can be drastic, with fraud controllers empowered to turn the lives of suspects inside out.

As reported by the World Bank, the recent Covid-19 pandemic provided a great push to implement digital social welfare systems in the global South. In fact, for the World Bank the so-called Digital Public Infrastructure (DPI), enabling “Digitizing Government to Person Payments (G2Px)”, are as fundamental for social and economic development today as physical infrastructure was for previous generations. Hence, the World Bank is finances globally systems modelled after the Indian Aadhaar system, where more than a billion persons have been registered biometrically. Aadhaar has become, for all intents and purposes, a pre-condition to receive subsidised food and other assistance for 800 million Indian citizens.

Important international aid organisations are not behaving differently from states. The World Food Programme alone holds data of more than 40 million people on its Scope data base. Unfortunately, WFP like other UN organisations, is not subject to data protection laws and the jurisdiction of courts. This makes the communities they have worked with particularly vulnerable.

In most places, the social will become the metric, where logarithms determine the operational conduit for delivering, controlling and withholding assistance, especially welfare payments. In other places, the power of logarithms may go even further, as part of trust systems, creditworthiness, and social credit. These social credit systems for individuals are highly controversial as they require mass surveillance since they aim to track behaviour beyond financial solvency. The social credit score of a citizen might not only suffer from incomplete, or inaccurate data, but also from assessing political loyalties and conformist social behaviour…(More)”.

Death Glitch: How Techno-Solutionism Fails Us in This Life and Beyond

Curated on Posted on by Stefaan Verhulst

Book by Tamara Kneese: “Since the internet’s earliest days, people have died and mourned online. In quiet corners of past iterations of the web, the dead linger. But attempts at preserving the data of the dead are often ill-fated, for websites and devices decay and die, just as people do. Death disrupts technologists’ plans for platforms. It reveals how digital production is always collaborative, undermining the entrepreneurial platform economy and highlighting the flaws of techno-solutionism.
 
Big Tech has authority not only over people’s lives but over their experiences of death as well. Ordinary users and workers, though, advocate for changes to tech companies’ policies around death. Drawing on internet histories along with interviews with founders of digital afterlife startups, caretakers of illness blogs, and transhumanist tinkerers, the technology scholar Tamara Kneese takes readers on a vibrant tour of the ways that platforms and people work together to care for digital remains. What happens when commercial platforms encounter the messiness of mortality?..(More)”.

China’s new AI rules protect people — and the Communist Party’s power

Curated on Posted on by Stefaan Verhulst

Article by Johanna M. Costigan: “In April, in an effort to regulate rapidly advancing artificial intelligence technologies, China’s internet watchdog introduced draft rules on generative AI. They cover a wide range of issues — from how data is trained to how users interact with generative AI such as chatbots. 

Under the new regulations, companies are ultimately responsible for the “legality” of the data they use to train AI models. Additionally, generative AI providers must not share personal data without permission, and must guarantee the “veracity, accuracy, objectivity, and diversity” of their pre-training data. 

These strict requirements by the Cyberspace Administration of China (CAC) for AI service providers could benefit Chinese users, granting them greater protections from private companies than many of their global peers. Article 11 of the regulations, for instance, prohibits providers from “conducting profiling” on the basis of information gained from users. Any Instagram user who has received targeted ads after their smartphone tracked their activity would stand to benefit from this additional level of privacy.  

Another example is Article 10 — it requires providers to employ “appropriate measures to prevent users from excessive reliance on generated content,” which could help prevent addiction to new technologies and increase user safety in the long run. As companion chatbots such as Replika become more popular, companies should be responsible for managing software to ensure safe use. While some view social chatbots as a cure for loneliness, depression, and social anxiety, they also present real risks to users who become reliant on them…(More)”.

Gaming Public Opinion

Curated on Posted on by Stefaan Verhulst

Article by Albert Zhang , Tilla Hoja & Jasmine Latimore: “The Chinese Communist Party’s (CCP’s) embrace of large-scale online influence operations and spreading of disinformation on Western social-media platforms has escalated since the first major attribution from Silicon Valley companies in 2019. While Chinese public diplomacy may have shifted to a softer tone in 2023 after many years of wolf-warrior online rhetoric, the Chinese Government continues to conduct global covert cyber-enabled influence operations. Those operations are now more frequent, increasingly sophisticated and increasingly effective in supporting the CCP’s strategic goals. They focus on disrupting the domestic, foreign, security and defence policies of foreign countries, and most of all they target democracies.

Currently—in targeted democracies—most political leaders, policymakers, businesses, civil society groups and publics have little understanding of how the CCP currently engages in clandestine activities online in their countries, even though this activity is escalating and evolving quickly. The stakes are high for democracies, given the indispensability of the internet and their reliance on open online spaces, free from interference. Despite years of monitoring covert CCP cyber-enabled influence operations by social-media platforms, governments, and research institutes such as ASPI, definitive public attribution of the actors driving these activities is rare. Covert online operations, by design, are difficult to detect and attribute to state actors. 

Social-media platforms and governments struggle to devote adequate resources to identifying, preventing and deterring increasing levels of malicious activity, and sometimes they don’t want to name and shame the Chinese Government for political, economic and/or commercial reasons…(More)”.

Operationalizing digital self-determination

Curated on Posted on by Stefaan Verhulst

Paper by Stefaan G. Verhulst: “A proliferation of data-generating devices, sensors, and applications has led to unprecedented amounts of digital data. We live in an era of datafication, one in which life is increasingly quantified and transformed into intelligence for private or public benefit. When used responsibly, this offers new opportunities for public good. The potential of data is evident in the possibilities offered by open data and data collaboratives—both instances of how wider access to data can lead to positive and often dramatic social transformation. However, three key forms of asymmetry currently limit this potential, especially for already vulnerable and marginalized groups: data asymmetries, information asymmetries, and agency asymmetries. These asymmetries limit human potential, both in a practical and psychological sense, leading to feelings of disempowerment and eroding public trust in technology. Existing methods to limit asymmetries (such as open data or consent) as well as some alternatives under consideration (data ownership, collective ownership, personal information management systems) have limitations to adequately address the challenges at hand. A new principle and practice of digital self-determination (DSD) is therefore required. The study and practice of DSD remain in its infancy. The characteristics we have outlined here are only exploratory, and much work remains to be done so as to better understand what works and what does not. We suggest the need for a new research framework or agenda to explore DSD and how it can address the asymmetries, imbalances, and inequalities—both in data and society more generally—that are emerging as key public policy challenges of our era…(More)”.

LGBTQ+ data availability

Curated on Posted on by Stefaan Verhulst

Report by Beyond Deng and Tara Watson: “LGBTQ+ (Lesbian, Gay, Bisexual, Transgender, Queer/Questioning) identification has doubled over the past decade, yet data on the overall LGBTQ+ population remains limited in large, nationally representative surveys such as the American Community Survey. These surveys are consistently used to understand the economic wellbeing of individuals, but they fail to fully capture information related to one’s sexual orientation and gender identity (SOGI).[1]

Asking incomplete SOGI questions leaves a gap in research that, if left unaddressed, will continue to grow in importance with the increase of the LGBTQ+ population, particularly among younger cohorts. In this report, we provide an overview of four large, nationally representative, and publicly accessible datasets that include information relevant for economic analysis. These include the Behavioral Risk Factor Surveillance System (BRFSS), National Health Interview Survey (NHIS), the American Community Survey (ACS), and the Census Household Pulse Survey. Each survey varies by sample size, sample unit, periodicity, geography, and the SOGI information they collect.[2]

The difference in how these datasets collect SOGI information impacts the estimates of LGBTQ+ prevalence. While we find considerable difference in measured LGBT prevalence across datasets, each survey documents a substantial increase in non-straight identity over time. Figure 1 shows that this is largely driven by young adults, who are increasingly likely to identify as LGBT over almost the past ten years. Using data from NHIS, around 4% of 18–24-year-olds in 2013 identified as LGB, which increased to 9.5% in 2021. Because of the short time horizon in these surveys, it is unclear how the current young adult cohort will identify as they age. Despite this, an important takeaway is that younger age groups clearly represent a substantial portion of the LGB community and are important to incorporate in economic analyses…(More)”.