The AI That Could Heal a Divided Internet

Curated on Posted on by Stefaan Verhulst

Article by Billy Perrigo: “In the 1990s and early 2000s, technologists made the world a grand promise: new communications technologies would strengthen democracy, undermine authoritarianism, and lead to a new era of human flourishing. But today, few people would agree that the internet has lived up to that lofty goal. 

Today, on social media platforms, content tends to be ranked by how much engagement it receives. Over the last two decades politics, the media, and culture have all been reshaped to meet a single, overriding incentive: posts that provoke an emotional response often rise to the top.

Efforts to improve the health of online spaces have long focused on content moderation, the practice of detecting and removing bad content. Tech companies hired workers and built AI to identify hate speech, incitement to violence, and harassment. That worked imperfectly, but it stopped the worst toxicity from flooding our feeds. 

There was one problem: while these AIs helped remove the bad, they didn’t elevate the good. “Do you see an internet that is working, where we are having conversations that are healthy or productive?” asks Yasmin Green, the CEO of Google’s Jigsaw unit, which was founded in 2010 with a remit to address threats to open societies. “No. You see an internet that is driving us further and further apart.”

What if there were another way? 

Jigsaw believes it has found one. On Monday, the Google subsidiary revealed a new set of AI tools, or classifiers, that can score posts based on the likelihood that they contain good content: Is a post nuanced? Does it contain evidence-based reasoning? Does it share a personal story, or foster human compassion? By returning a numerical score (from 0 to 1) representing the likelihood of a post containing each of those virtues and others, these new AI tools could allow the designers of online spaces to rank posts in a new way. Instead of posts that receive the most likes or comments rising to the top, platforms could—in an effort to foster a better community—choose to put the most nuanced comments, or the most compassionate ones, first…(More)”.

Mass Data Sharing in Smart Cities

Curated on Posted on by Stefaan Verhulst

Report by Berenika Drazewska and Mark Findlay: “There are at least two ways of understanding the importance of this Report and its implications. The essential research purpose was to examine the nature of mass data sharing between private and public agencies in the commerce and administration of certain smart cities. With this knowledge the research speculated on and selectively exposed the governance challenges posed by this sharing for stakeholders, citizen/residents in particular, in various data relationships and arrangements. Predicting that good data governance policy and practices can address these challenges, the Report proposes a model strategy that grows from commitments where stakeholders will employ trusted data spaces to create respectful and responsible data relationships, where the benefits of data sharing can also be achieved without compromising any stakeholder interests…(More)”.

United against algorithms: a primer on disability-led struggles against algorithmic injustice

Curated on Posted on by Stefaan Verhulst

Report by Georgia van Toorn: “Algorithmic decision-making (ADM) poses urgent concerns regarding the rights and entitlements of people with disability from all walks of life. As ADM systems become increasingly embedded in government decision-making processes, there is a heightened risk of harm, such as unjust denial of benefits or inadequate support, accentuated by the expanding reach of state surveillance.

ADM systems have far reaching impacts on disabled lives and life chances. Despite this, they are often designed without the input of people with lived experience of disability, for purposes that do not align with the goals of full rights, participation, and justice for disabled people.

This primer explores how people with disability are collectively responding to the threats posed by algorithmic, data-driven systems – specifically their public sector applications. It provides an introductory overview of the topic, exploring the approaches, obstacles, and actions taken by people with disability in their ‘algoactivist’ struggles…(More)”.

The CFPB wants to rein in data brokers

Curated on Posted on by Stefaan Verhulst

Article by Gaby Del Valle: “The Consumer Financial Protection Bureau wants to propose new regulations that would require data brokers to comply with the Fair Credit Reporting Act. In a speech at the White House earlier this month, CFPB Director Rohit Chopra said the agency is looking into policies to “ensure greater accountability” for companies that buy and sell consumer data, in keeping with an executive order President Joe Biden issued in late February.

Chopra said the agency is considering proposals that would define data brokers that sell certain types of data as “consumer reporting agencies,” thereby requiring those companies to comply with the Fair Credit Reporting Act (FCRA). The statute bans sharing certain kinds of data (e.g., your credit report) with entities unless they serve a specific purpose outlined in the law (e.g., if the report is used for employment purposes or to extend a line of credit to someone).

The CFBP views the buying and selling of consumer data as a national security issue, not just a matter of privacy. Chopra mentioned three massive data breaches — the 2015 Anthem leak, the 2017 Equifax hack, and the 2018 Marriott breach — as examples of foreign adversaries illicitly obtaining Americans’ personal data. “When Americans’ health information, financial information, and even their travel whereabouts can be assembled into detailed dossiers, it’s no surprise that this raises risks when it comes to safety and security,” Chopra said. But the focus on high-profile hacks obscures a more pervasive, totally legal phenomenon: data brokers’ ability to sell detailed personal information to anyone who’s willing to pay for it…(More)”.

Measuring the mobile body

Curated on Posted on by Stefaan Verhulst

Article by Laura Jung: “…While nation states have been collecting data on citizens for the purposes of taxation and military recruitment for centuries, its indexing, organization in databases and classification for particular governmental purposes – such as controlling the mobility of ‘undesirable’ populations – is a nineteenth-century invention. The French historian and philosopher Michel Foucault describes how, in the context of growing urbanization and industrialization, states became increasingly preoccupied with the question of ‘circulation’. Persons and goods, as well as pathogens, circulated further than they had in the early modern period. While states didn’t seek to suppress or control these movements entirely, they sought means to increase what was seen as ‘positive’ circulation and minimize ‘negative’ circulation. They deployed the novel tools of a positivist social science for this purpose: statistical approaches were used in the field of demography to track and regulate phenomena such as births, accidents, illness and deaths. The emerging managerial nation state addressed the problem of circulation by developing a very particular toolkit amassing detailed information about the population and developing standardized methods of storage and analysis.

One particularly vexing problem was the circulation of known criminals. In the nineteenth century, it was widely believed that if a person offended once, they would offend again. However, the systems available for criminal identification were woefully inadequate to the task.

As criminologist Simon Cole explains, identifying an unknown person requires a ‘truly unique body mark’. Yet before the advent of modern systems of identification, there were only two ways to do this: branding or personal recognition. While branding had been widely used in Europe and North America on convicts, prisoners and enslaved people, evolving ideas around criminality and punishment largely led to the abolition of physical marking in the early nineteenth century. The criminal record was established in its place: a written document cataloguing the convict’s name and a written description of their person, including identifying marks and scars…(More)”.

AI-driven public services and the privacy paradox: do citizens really care about their privacy?

Curated on Posted on by Stefaan Verhulst

Paper by Based on privacy calculus theory, we derive hypotheses on the role of perceived usefulness and privacy risks of artificial intelligence (AI) in public services. In a representative vignette experiment (n = 1,048), we asked citizens whether they would download a mobile app to interact in an AI-driven public service. Despite general concerns about privacy, we find that citizens are not susceptible to the amount of personal information they must share, nor to a more anthropomorphic interface. Our results confirm the privacy paradox, which we frame in the literature on the government’s role to safeguard ethical principles, including citizens’ privacy…(More)”.

The impact of generative artificial intelligence on socioeconomic inequalities and
policy making

Curated on Posted on by Stefaan Verhulst

Paper by Valerio Capraro et al: “Generative artificial intelligence, including chatbots like ChatGPT, has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the probable impacts of generative AI on four critical domains: work, education, health, and information. Our goal is to warn about how generative AI could worsen existing inequalities while illuminating directions for using AI to resolve pervasive social problems. Generative AI in the workplace can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning but may widen the digital divide. In healthcare, it improves diagnostics and accessibility but could deepen pre-existing inequalities. For information, it democratizes content creation and access but also dramatically expands the production and proliferation of misinformation. Each section covers a specific topic, evaluates existing research, identifies critical gaps, and recommends research directions. We conclude with a section highlighting the role of policymaking to maximize generative AI’s potential to reduce inequalities while
mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We contend that these policies should promote shared prosperity through the advancement of generative AI. We suggest several concrete policies to encourage further research and debate. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI…(More)”.

Data-Driven Innovation in the Creative Industries

Curated on Posted on by Stefaan Verhulst

Open Access Book edited by Melissa Terras, Vikki Jones, Nicola Osborne, and Chris Speed: “The creative industries – the place where art, business, and technology meet in economic activity – have been hugely affected by the relatively recent digitalisation (and often monetisation) of work, home, relationships, and leisure. Such trends were accelerated by the global COVID-19 pandemic. This edited collection examines how the creative industries can be supported to make best use of opportunities in digital technology and data-driven innovation.

Since digital markets and platforms are now essential for revenue generation and audience engagement, there is a vital need for improved data and digital skills in the creative and cultural sectors. Taking a necessarily global perspective, this book explores the challenges and opportunities of data-driven approaches to creativity in different contexts across the arts, cultural, and heritage sectors. Chapters reach beyond the platforms and approaches provided by the technology sector to delve into the collaborative work that supports innovation around the interdisciplinary and cross-sectoral issues that emerge where data infrastructures and approaches meet creativity.

A novel intervention that uniquely centres the role of data in the theory and practice of creative industries’ innovation, this book is valuable reading for those researching and studying the creative economy as well for those who drive investment for the creative industries in a digitalised society…(More)”.

The tech industry can’t agree on what open-source AI means. That’s a problem.

Curated on Posted on by Stefaan Verhulst

Article by Edd Gent: “Suddenly, “open source” is the latest buzzword in AI circles. Meta has pledged to create open-source artificial general intelligence. And Elon Musk is suing OpenAI over its lack of open-source AI models.

Meanwhile, a growing number of tech leaders and companies are setting themselves up as open-source champions. 

But there’s a fundamental problem—no one can agree on what “open-source AI” means. 

On the face of it, open-source AI promises a future where anyone can take part in the technology’s development. That could accelerate innovation, boost transparency, and give users greater control over systems that could soon reshape many aspects of our lives. But what even is it? What makes an AI model open source, and what disqualifies it?

The answers could have significant ramifications for the future of the technology. Until the tech industry has settled on a definition, powerful companies can easily bend the concept to suit their own needs, and it could become a tool to entrench the dominance of today’s leading players.

Entering this fray is the Open Source Initiative (OSI), the self-appointed arbiters of what it means to be open source. Founded in 1998, the nonprofit is the custodian of the Open Source Definition, a widely accepted set of rules that determine whether a piece of software can be considered open source. 

Now, the organization has assembled a 70-strong group of researchers, lawyers, policymakers, activists, and representatives from big tech companies like Meta, Google, and Amazon to come up with a working definition of open-source AI…(More)”.

New Jersey is turning to AI to improve the job search process

Curated on Posted on by Stefaan Verhulst

Article by Beth Simone Noveck: “Americans are experiencing some conflicting feelings about AI.

While people are flocking to new roles like prompt engineer and AI ethicist, the technology is also predicted to put many jobs at risk, including computer programmers, data scientists, graphic designers, writers, lawyers.

Little wonder, then, that a national survey by the Heldrich Center for Workforce Development found an overwhelming majority of Americans (66%) believe that they “will need more technological skills to achieve their career goals.” One thing is certain: Workers will need to train for change. And in a world of misinformation-filled social media platforms, it is increasingly important for trusted public institutions to provide reliable, data-driven resources.

In New Jersey, we’ve tried doing just that by collaborating with workers, including many with disabilities, to design technology that will support better decision-making around training and career change. Investing in similar public AI-powered tools could help support better consumer choice across various domains. When a public entity designs, controls and implements AI, there is a far greater likelihood that this powerful technology will be used for good.

In New Jersey, the public can find reliable, independent, unbiased information about training and upskilling on the state’s new MyCareer website, which uses AI to make personalized recommendations about your career prospects, and the training you will need to be ready for a high-growth, in-demand job…(More)”.