We Need to Take Back Our Privacy

Curated on Posted on by Stefaan Verhulst

Zeynep Tufekci in The New York Times: “…Congress, and states, should restrict or ban the collection of many types of data, especially those used solely for tracking, and limit how long data can be retained for necessary functions — like getting directions on a phone.

Selling, trading and merging personal data should be restricted or outlawed. Law enforcement could obtain it subject to specific judicial oversight.

Researchers have been inventing privacy-preserving methods for analyzing data sets when merging them is in the public interest but the underlying data is sensitive — as when health officials are tracking a disease outbreak and want to merge data from multiple hospitals. These techniques allow computation but make it hard, if not impossible, to identify individual records. Companies are unlikely to invest in such methods, or use end-to-end encryption as appropriate to protect user data, if they could continue doing whatever they want. Regulation could make these advancements good business opportunities, and spur innovation.

I don’t think people like things the way they are. When Apple changed a default option from “track me” to “do not track me” on its phones, few people chose to be tracked. And many who accept tracking probably don’t realize how much privacy they’re giving up, and what this kind of data can reveal. Many location collectors get their data from ordinary apps — could be weather, games, or anything else — that often bury that they will share the data with others in vague terms deep in their fine print.

Under these conditions, requiring people to click “I accept” to lengthy legalese for access to functions that have become integral to modern life is a masquerade, not informed consent.

Many politicians have been reluctant to act. The tech industry is generous, cozy with power, and politicians themselves use data analysis for their campaigns. This is all the more reason to press them to move forward…(More)”.

The Frontlines of Artificial Intelligence Ethics

Curated on Posted on by Stefaan Verhulst

Book edited by Andrew J. Hampton, and Jeanine A. DeFalco: “This foundational text examines the intersection of AI, psychology, and ethics, laying the groundwork for the importance of ethical considerations in the design and implementation of technologically supported education, decision support, and leadership training.

AI already affects our lives profoundly, in ways both mundane and sensational, obvious and opaque. Much academic and industrial effort has considered the implications of this AI revolution from technical and economic perspectives, but the more personal, humanistic impact of these changes has often been relegated to anecdotal evidence in service to a broader frame of reference. Offering a unique perspective on the emerging social relationships between people and AI agents and systems, Hampton and DeFalco present cutting-edge research from leading academics, professionals, and policy standards advocates on the psychological impact of the AI revolution. Structured into three parts, the book explores the history of data science, technology in education, and combatting machine learning bias, as well as future directions for the emerging field, bringing the research into the active consideration of those in positions of authority.

Exploring how AI can support expert, creative, and ethical decision making in both people and virtual human agents, this is essential reading for students, researchers, and professionals in AI, psychology, ethics, engineering education, and leadership, particularly military leadership…(More)”.

Mobile phone data reveal the effects of violence on internal displacement in Afghanistan

Curated on Posted on by Stefaan Verhulst

Paper by Nearly 50 million people globally have been internally displaced due to conflict, persecution and human rights violations. However, the study of internally displaced persons—and the design of policies to assist them—is complicated by the fact that these people are often underrepresented in surveys and official statistics. We develop an approach to measure the impact of violence on internal displacement using anonymized high-frequency mobile phone data. We use this approach to quantify the short- and long-term impacts of violence on internal displacement in Afghanistan, a country that has experienced decades of conflict. Our results highlight how displacement depends on the nature of violence. High-casualty events, and violence involving the Islamic State, cause the most displacement. Provincial capitals act as magnets for people fleeing violence in outlying areas. Our work illustrates the potential for non-traditional data sources to facilitate research and policymaking in conflict settings….(More)”.

Automating the Analysis of Online Deliberation? Comparing computational analyses of polarized discussions on climate change to established content analysis

Curated on Posted on by Stefaan Verhulst

Paper by Lisa Oswald: “High­-quality discussions can help people acquire an adequate understanding of issues and alleviate mechanisms of opinion polarization. However, the extent to which the quality of the online public discourse contributes is contested. Facing the importance and the sheer volume of online discussions, reliable computational approaches to assess the deliberative quality of online discussions at scale would open a new era of deliberation research. But is it possible to automate the assessment of deliberative quality? I compare structural features of discussion threads and sim­ple text­-based measures to established manual content analysis by applying all measures to online discussions on ‘Reddit’ that deal with the 2020 wildfires in Australia and California. I further com­ pare discussions between two ideologically opposite online communities, one featuring discussions in line with the scientific consensus and one featuring climate change skepticism. While no single computational measure can capture the multidimensional concept of deliberative quality, I find that (1) measures of structural complexity capture engagement and participation as preconditions for deliberation, (2) the length of comments is correlated with manual measures of argumentation, and (3) automated toxicity scores are correlated with manual measures of respect. While the presented computational approaches cannot replace in­depth content coding, the findings imply that selected automated measures can be useful, scalable additions to the measurement repertoire for specific dimensions of online deliberation. I discuss implications for communication research and platform regulation and suggest interdisciplinary research to synthesize past content coding efforts using machine learning….(More)”.

How the Pandemic Made Algorithms Go Haywire

Curated on Posted on by Stefaan Verhulst

Article by Ravi Parikh and Amol Navathe: “Algorithms have always had some trouble getting things right—hence the fact that ads often follow you around the internet for something you’ve already purchased.

But since COVID upended our lives, more of these algorithms have misfired, harming millions of Americans and widening existing financial and health disparities facing marginalized groups. At times, this was because we humans weren’t using the algorithms correctly. More often it was because COVID changed life in a way that made the algorithms malfunction.

Take, for instance, an algorithm used by dozens of hospitals in the U.S. to identify patients with sepsis—a life-threatening consequence of infection. It was supposed to help doctors speed up transfer to the intensive care unit. But starting in spring of 2020, the patients that showed up to the hospital suddenly changed due to COVID. Many of the variables that went into the algorithm—oxygen levels, age, comorbid conditions—were completely different during the pandemic. So the algorithm couldn’t effectively discern sicker from healthier patients, and consequently it flagged more than twice as many patients as “sick” even though hospital capacity was 35 percent lower than normal. The result was presumably more instances of doctors and nurses being summoned to the patient bedside. It’s possible all of these alerts were necessary – after all, more patients were sick. However, it’s also possible that many of these alerts were false alarms because the type of patients showing up to the hospital were different. Either way, this threatened to overwhelm physicians and hospitals. This “alert overload” was discovered months into the pandemic and led the University of Michigan health system to shut down its use of the algorithm…(More)”.

More than just information: what does the public want to know about climate change?

Curated on Posted on by Stefaan Verhulst

Paper by Michael Murunga et all: “Public engagement on climate change is a vital concern for both science and society. Despite more people engaging with climate change science today, there remains a high-level contestation in the public sphere regarding scientific credibility and identifying information needs, interests, and concerns of the non-technical public. In this paper, we present our response to these challenges by describing the use of a novel “public-powered” approach to engaging the public through submitting questions of interest about climate change to climate researchers before a planned engagement activity. Employing thematic content analysis on the submitted questions, we describe how those people we engaged with are curious about understanding climate change science, including mitigating related risks and threats by adopting specific actions. We assert that by inviting the public to submit their questions of interest to researchers before an engagement activity, this step can inform why and transform how actors engage in reflexive dialogue…(More)”.

Building a Data Infrastructure for the Bioeconomy

Curated on Posted on by Stefaan Verhulst

Article by Gopal P. Sarma and Melissa Haendel: “While the development of vaccines for COVID-19 has been widely lauded, other successful components of the national response to the pandemic have not received as much attention. The National COVID Cohort Collaborative (N3C), for example, flew under the public’s radar, even though it aggregated crucial US public health data about the new disease through cross-institutional collaborations among government, private, and nonprofit health and research organizations. These data, which were made available to researchers via cutting-edge software tools, have helped in myriad ways: they led to identification of the clinical characteristics of acute COVID-19 for risk prediction, assisted in providing clinical care for immunocompromised adults, revealed how COVID infection affects children, and documented that vaccines appear to reduce the risk of developing long COVID.

N3C has created the largest national, publicly available patient-level dataset in US history. Through a unique public-private partnership, over 300 participating organizations quickly overcame privacy concerns and data silos to include 13 million patient records in the project. More than 3,000 participating scientists are now working to overcome the particular challenge faced in the United States—the lack of a national healthcare data infrastructure available in many other countries—to support public health and medical responses. N3C shows great promise for unraveling answers to questions related to COVID, but it could easily be expanded for many areas of public health, including pandemic preparedness and monitoring disease status across the population.

As public servants dedicated to improving public health and equity, we believe that to unite the nation’s fragmented public health system, the United States should establish a standing capacity to collect, harmonize, and sustain a wide range of data types and sources. The public health data collected by N3C would ultimately be but one component of a rich landscape of interoperable data systems that can guide public policy in an era of rapid environmental change, sophisticated biological threats, and an economy enabled by biotechnology. Such an effort will require new thinking about data collection, infrastructure, and regulation, but its benefits could be enormous—enabling policymakers to make decisions in an increasingly complex world. And as the interconnections between society, industry, and government continue to intensify, decisionmaking of all types and scales will be more efficient and responsive if it can rely on significantly expanded data collection and analysis capabilities…(More)”.

(When) Do Open Budgets Transform Lives? Progress and Next Steps in Fiscal Openness Research

Curated on Posted on by Stefaan Verhulst

Paper by Xiao Hui Tai, Shikhar Mehra & Joshua E. Blumenstock: “This paper documents the rapidly growing empirical literature that can plausibly claim to identify causal effects of transparency or participation in budgeting in a variety of contexts. Recent studies convincingly demonstrate that the power of audits travels well beyond the context of initial field-defining studies, consider participatory budgeting beyond Brazil, where such practices were pioneered, and examine previously neglected outcomes, notably revenues and procurement. Overall, the study of the impacts of fiscal openness has become richer and more nuanced. The most well-documented causal effects are positive: lower corruption and enhanced accountability at the ballot box. Moreover, these impacts have been shown to apply across different settings. This research concludes that the empirical case for open government in this policy area is rapidly growing in strength. This paper sets out challenges related to studying national-level reforms; working directly with governments; evaluating systems as opposed to programs; clarifying the relationship between transparency and participation; and understanding trade-offs for reforms in this area….(More)”.

Canada is the first country to provide census data on transgender and non-binary people

Curated on Posted on by Stefaan Verhulst

StatsCan: “Prior to the 2021 Census, some individuals indicated that they were not able to see themselves in the two responses of male or female on the existing sex question in the census.

Following extensive consultation and countrywide engagement with the Canadian population, the census evolved—as it has for more than a century—to reflect societal changes, adding new content on gender in 2021.

Beginning in 2021, the precision of “at birth” was added to the sex question on the census questionnaire, and a new question on gender was included. As a result, the historical continuity of information on sex was maintained while allowing all cisgender, transgender and non-binary individuals to report their gender. This addressed an important information gap on gender diversity (see Filling the gaps: Information on gender in the 2021 Census and 2021 Census: Sex at birth and gender—the whole picture).

For many people, their gender corresponds to their sex at birth (cisgender men and cisgender women). For some, these do not align (transgender men and transgender women) or their gender is not exclusively “man” or “woman” (non-binary people).

The strength of the census is to provide reliable data for local communities throughout the country and for smaller populations such as the transgender and non-binary populations. Statistics Canada always protects privacy and confidentiality of respondents when disseminating detailed data.

These modifications reflect today’s reality in terms of the evolving acceptance and understanding of gender and sexual diversity and an emerging social and legislative recognition of transgender, non-binary and LGBTQ2+ people in general, that is, people who are lesbian, gay, bisexual, transgender, queer, Two-Spirit, or who use other terms related to gender or sexual diversity. In 2017, the Canadian government amended the Canadian Human Rights Act and the Canadian Criminal Code to protect individuals from discrimination and hate crimes based on gender identity and expression.

These data can be used by public decision makers, employers, and providers of health care, education, justice, and other services to better meet the needs of all men and women—including transgender men and women—and non-binary people in their communities….(More)”.

Why Democracy vs. Autocracy Misses the Point

Curated on Posted on by Stefaan Verhulst

Essay by Jean-Marie Guéhenno: “I have always been a contrarian. I was a contrarian in 1989 when I wrote my first book, criticizing the idea—then widely held—that democracy had triumphed once and for all. And today I find that I’m a contrarian again with my new book, because everybody is talking about the confrontation between democracies and autocracies and I think that’s missing the point.

Something much more important is happening: the revolution of data, the Internet, and artificial intelligence. I believe we are on the cusp of an earthquake in the history of humanity of a kind that happens only once in hundreds of years. The most recent comparison is the Renaissance, and the pace of change today is much quicker than back then.

The institutions we built in the pre-data age are soon going to be completely overwhelmed, and thinking in terms of the old categories of democracies versus autocracies misses all the new challenges that they will have to face. This is a time of great peril as well as great promise, as was the Renaissance—not only the era of Leonard da Vinci, but also a century of religious wars.

The current revolution of data and algorithms is redistributing power in a way that cannot be compared to any historical shift. Traditionally we think of power concentrating in the hands of the leaders of states or big industrial companies. But power, increasingly, is in the hands of algorithms that are tasked (initially by humans) with learning and changing themselves, and evolve in ways we do not predict.

That means the owners of Google or Facebook or Amazon are not the masters of our destiny in the same sense as previous corporate titans. Similarly, while it is true to some extent that data will give dictators unprecedented power to manipulate society, they may also come to be dominated by the evolution of the algorithms on which they depend.

We see already how algorithms are reshaping politics. Social media has created self-contained tribes which do not speak to each other. The most important thing in democracy is not the vote itself, but the process of deliberation before the vote, and social media is quickly fragmenting the common ground on which such deliberations have been built.

How can societies exert control over how algorithms manage data, and whether they foster hatred or harmony? Institutions that are able to control this new power are not yet really in place. What they should look like will be one of the great debates of the future.

I don’t have the answers: I believe no human mind can anticipate the extent of the transformations that are going to happen. Indeed, I think the very notion that you can know today what will be the right institutions for the future is hubristic. The best institutions (and people) will be those that are most adaptable.

However, I believe that one promising approach is to think in terms of the relationship between the logic of knowledge and the logic of democracy. Take central banks as an example. The average citizen does not have a clue about how monetary policy works. Instead we rely on politicians to task the experts at central banks to try achieve a certain goal—it could be full employment, or a stable currency….(More)”.