Stefaan Verhulst
Paper by Greg Elmer, Sabrina Ward-Kimola and Anthony Glyn Burton: “This article performs a digital methods analysis on a sample of online crowdfunding campaigns seeking financial support for COVID related financial challenges. Building upon the crowdfunding literature this paper performs an international comparison of the goals of COVID related campaigns during the early spread of the pandemic. The paper seeks to determine the extent to which crowdfunding campaigns reflect current failures of governments to supress the COVID pandemic and support the financial challenges of families, communities and small businesses….(More)”.
Foreword of a Report by the Australian Human Rights Commission: “Artificial intelligence (AI) promises better, smarter decision making.
Governments are starting to use AI to make decisions in welfare, policing and law enforcement, immigration, and many other areas. Meanwhile, the private sector is already using AI to make decisions about pricing and risk, to determine what sorts of people make the ‘best’ customers… In fact, the use cases for AI are limited only by our imagination.
However, using AI carries with it the risk of algorithmic bias. Unless we fully understand and address this risk, the promise of AI will be hollow.
Algorithmic bias is a kind of error associated with the use of AI in decision making, and often results in unfairness. Algorithmic bias can arise in many ways. Sometimes the problem is with the design of the AI-powered decision-making tool itself. Sometimes the problem lies with the data set that was used to train the AI tool, which could replicate or even make worse existing problems, including societal inequality.
Algorithmic bias can cause real harm. It can lead to a person being unfairly treated, or even suffering unlawful discrimination, on the basis of characteristics such as their race, age, sex or disability.
This project started by simulating a typical decision-making process. In this technical paper, we explore how algorithmic bias can ‘creep in’ to AI systems and, most importantly, how this problem can be addressed.
To ground our discussion, we chose a hypothetical scenario: an electricity retailer uses an AI-powered tool to decide how to offer its products to customers, and on what terms. The general principles and solutions for mitigating the problem, however, will be relevant far beyond this specific situation.
Because algorithmic bias can result in unlawful activity, there is a legal imperative to address this risk. However, good businesses go further than the bare minimum legal requirements, to ensure they always act ethically and do not jeopardise their good name.
Rigorous design, testing and monitoring can avoid algorithmic bias. This technical paper offers some guidance for companies to ensure that when they use AI, their decisions are fair, accurate and comply with human rights….(More)”
Peter Yeung at The Guardian: “Angela Brito was driving back to her home in the Parisian suburb of Seine-et-Marne one day in September 2019 when the phone rang. The 47-year-old caregiver, accustomed to emergency calls, pulled over in her old Renault Megane to answer. The voice on the other end of the line informed her she had been randomly selected to take part in a French citizens’ convention on climate. Would she, the caller asked, be interested?
“I thought it was a real prank,” says Brito, a single mother of four who was born in the south of Portugal. “I’d never heard anything about it before. But I said yes, without asking any details. I didn’t believe it.’”
Brito received a letter confirming her participation but she still didn’t really take it seriously. On 4 October, the official launch day, she got up at 7am as usual and, while driving to meet her first patient of the day, heard a radio news item on how 150 ordinary citizens had been randomly chosen for this new climate convention. “I said to myself, ah, maybe it was true,” she recalls.
At the home of her second patient, a good-humoured old man in a wheelchair, the TV news was on. Images of the grand Art Déco-style Palais d’Iéna, home of the citizens’ gathering, filled the screen. “I looked at him and said, ‘I’m supposed to be one of those 150,’” says Brito. “He told me, ‘What are you doing here then? Leave, get out, go there!’”
Brito had two hours to get to the Palais d’Iéna. “I arrived a little late, but I arrived!” she says.
Over the next nine months, Brito would take part in the French citizens’ convention for the climate, touted by Emmanuel Macron as an “unprecedented democratic experiment”, which would bring together 150 people aged 16 upwards, from all over France and all walks of French life – to learn, debate and then propose measures to reduce greenhouse gas emissions by at least 40% by 2030. By the end of the process, Brito and her fellow participants had convinced Macron to pledge an additional €15bn (£13.4bn) to the climate cause and to accept all but three of the group’s 149 recommendations….(More)”.
Paper by Nora Madison and Mathias Klang: “This paper argues for the importance and value of digital activism. We first outline the arguments against digitally mediated activism and then address the counter-arguments against its derogatory criticisms. The low threshold for participating in technologically mediated activism seems to irk its detractors. Indeed, the term used to downplay digital activism is slacktivism, a portmanteau of slacker and activism. The use of slacker is intended to stress the inaction, low effort, and laziness of the person and thereby question their dedication to the cause. In this work we argue that digital activism plays a vital role in the arsenal of the activist and needs to be studied on its own terms in order to be more fully understood….(More)”
Editorial in Nature: “…As Nature reports in a series of Features on facial recognition this week, many in the field are rightly worried about how the technology is being used. They know that their work enables people to be easily identified, and therefore targeted, on an unprecedented scale. Some scientists are analysing the inaccuracies and biases inherent in facial-recognition technology, warning of discrimination, and joining the campaigners calling for stronger regulation, greater transparency, consultation with the communities that are being monitored by cameras — and for use of the technology to be suspended while lawmakers reconsider where and how it should be used. The technology might well have benefits, but these need to be assessed against the risks, which is why it needs to be properly and carefully regulated.Is facial recognition too biased to be let loose?
Responsible studies
Some scientists are urging a rethink of ethics in the field of facial-recognition research, too. They are arguing, for example, that scientists should not be doing certain types of research. Many are angry about academic studies that sought to study the faces of people from vulnerable groups, such as the Uyghur population in China, whom the government has subjected to surveillance and detained on a mass scale.
Others have condemned papers that sought to classify faces by scientifically and ethically dubious measures such as criminality….One problem is that AI guidance tends to consist of principles that aren’t easily translated into practice. Last year, the philosopher Brent Mittelstadt at the University of Oxford, UK, noted that at least 84 AI ethics initiatives had produced high-level principles on both the ethical development and deployment of AI (B. Mittelstadt Nature Mach. Intell. 1, 501–507; 2019). These tended to converge around classical medical-ethics concepts, such as respect for human autonomy, the prevention of harm, fairness and explicability (or transparency). But Mittelstadt pointed out that different cultures disagree fundamentally on what principles such as ‘fairness’ or ‘respect for autonomy’ actually mean in practice. Medicine has internationally agreed norms for preventing harm to patients, and robust accountability mechanisms. AI lacks these, Mittelstadt noted. Specific case studies and worked examples would be much more helpful to prevent ethics guidance becoming little more than window-dressing….(More)”.
Book by Sun-ha Hong: “What counts as knowledge in the age of big data and smart machines? In its pursuit of better knowledge, technology is reshaping what counts as knowledge in its own image – and demanding that the rest of us catch up to new machinic standards for what counts as suspicious, informed, employable. In the process, datafication often generates speculation as much as it does information. The push for algorithmic certainty sets loose an expansive array of incomplete archives, speculative judgments and simulated futures where technology meets enduring social and political problems.
Technologies of Speculation traces this technological manufacturing of speculation as knowledge. It shows how unprovable predictions, uncertain data and black-boxed systems are upgraded into the status of fact – with lasting consequences for criminal justice, public opinion, employability, and more. It tells the story of vast dragnet systems constructed to predict the next terrorist, and how familiar forms of prejudice seep into the data by the back door. In software placeholders like ‘Mohammed Badguy’, the fantasy of pure data collides with the old spectre of national purity. It tells the story of smart machines for ubiquitous and automated self-tracking, manufacturing knowledge that paradoxically lies beyond the human senses. Such data is increasingly being taken up by employers, insurers and courts of law, creating imperfect proxies through which my truth can be overruled.
The book situates ongoing controversies over AI and algorithms within a broader societal faith in objective truth and technological progress. It argues that even as datafication leverages this faith to establish its dominance, it is dismantling the longstanding link between knowledge and human reason, rational publics and free individuals. Technologies of Speculation thus emphasises the basic ethical problem underlying contemporary debates over privacy, surveillance and algorithmic bias: who, or what, has the right to the truth of who I am and what is good for me? If data promises objective knowledge, then we must ask in return: knowledge by and for whom, enabling what forms of life for the human subject?…(More)”.
Paper by Huaxiong Jiang, Stan Geertman & Patrick Witte: “This paper argues for a specific urban planning perspective on smart governance that we call “smart urban governance,” which represents a move away from the technocratic way of governing cities often found in smart cities. A framework on smart urban governance is proposed on the basis of three intertwined key components, namely spatial, institutional, and technological components. To test the applicability of the framework, we conducted an international questionnaire survey on smart city projects. We then identified and discursively analyzed two smart city projects—Smart Nation Singapore and Helsinki Smart City—to illustrate how this framework works in practice. The questionnaire survey revealed that smart urban governance varies remarkably: As urban issues differ in different contexts, the governance modes and relevant ICT functionalities applied also differ considerably. Moreover, the case analysis indicates that a focus on substantive urban challenges helps to define appropriate modes of governance and develop dedicated technologies that can contribute to solving specific smart city challenges. The analyses of both cases highlight the importance of context (cultural, political, economic, etc.) in analyzing interactions between the components. In this, smart urban governance promotes a sociotechnical way of governing cities in the “smart” era by starting with the urban issue at stake, promoting demand-driven governance modes, and shaping technological intelligence more socially, given the specific context….(More)”.
” The Governance Lab (The GovLab) at the NYU Tandon School of Engineering, with support from the Henry Luce Foundation, today released guidance to inform decision-making in the responsible re-use of data — re-purposing data for a use other than that for which it was originally intended — to address COVID-19. The findings, recommendations, and a new Responsible Data Re-Use framework stem from The Data Assembly initiative in New York City. An effort to solicit diverse, actionable public input on data re-use for crisis response in the United States, the Data Assembly brought together New York City-based stakeholders from government, the private sector, civic rights and advocacy organizations, and the general public to deliberate on innovative, though potentially risky, uses of data to inform crisis response in New York City. The findings and guidance from the initiative will inform policymaking and practice regarding data re-use in New York City, as well as free data literacy training offerings.
The Data Assembly’s Responsible Data Re-Use Framework provides clarity on a major element of the ongoing crisis. Though leaders throughout the world have relied on data to reduce uncertainty and make better decisions, expectations around the use and sharing of siloed data assets has remained unclear. This summer, along with the New York Public Library and Brooklyn Public Library, The GovLab co-hosted four months of remote deliberations with New York-based civil rights organizations, key data holders, and policymakers. Today’s release is a product of these discussions, to show how New Yorkers and their leaders think about the opportunities and risks involved in the data-driven response to COVID-19….(More)”
See: The Data Assembly Synthesis Report by y Andrew Young, Stefaan G. Verhulst, Nadiya Safonova, and Andrew J. Zahuranec
Richard Hughes Gibson at the Hedgehog Review: “In the last decade of the twentieth century, as we’ve seen, Howard Rheingold and William J. Mitchell imagined the Web as an “electronic agora” where netizens would roam freely, mixing business, pleasure, and politics. Al Gore envisioned it as an “information superhighway” system for which any computer could offer an onramp. Our current condition, by contrast, has been likened to shuffling between “walled gardens,” each platform—be it Facebook, Apple, Amazon, or Google—being its own tightly controlled ecosystem. Yet even this metaphor is perhaps too benign. As the cultural critic Alan Jacobs has observed, “they are not gardens; they are walled industrial sites, within which users, for no financial compensation, produce data which the owners of the factories sift and then sell.”
Harvard Business School professor Shoshanna Zuboff has dubbed the business model underlying these factories “surveillance capitalism.” Surveillance capitalism works by collecting information about you (your Internet activity, call history, app usage, your voice, your location, even your fitness level), which creates profiles of what you like, where you go, who you know, and who you are. That shadowy portrait makes a powerful tool for predicting what kinds of products and services you might like to purchase, and other companies are happy to pay for such finely-tuned targeted advertising. (Facebook alone generated $69 billion in ad revenue last year.)
The information-gathering can’t ever stop, however; the business model depends on a steady supply of new user data to inform the next round of predictions. This “extraction imperative,” as Zuboff calls it, is inherently monopolistic, rival companies being both a threat that must be eliminated and a potential gold mine from which more user data can be extracted (see Facebook’s acquisitions of competitors Whatsapp and Instagram). Equally worryingly, the big tech companies have begun moving into other sectors of the economy, as seen, for example, in Google’s quiet entry last year into the medical records business (unbeknownst to the patients and physicians whose data was mined).
There is growing consensus among legal scholars and social scientists that these practices are hazardous to democracy. Commentators worry over the consequences of putting so much wealth in so few hands so quickly (Zuboff calls it a “new Gilded Age”). They note the number of tech executives who’ve gone on to high-ranking government posts and vice versa. They point to the fact that—contrary to Mark Zuckerberg’s 2010 declaration that privacy is no longer a “social norm”—users are indeed worried about privacy. Scholars note, furthermore, that these platforms are not a genuine reflection of public opinion, though they are often treated as such. Social media can operate as echo chambers, only showing you what people like you read, think, do. Paradoxically, they can also become pressure cookers. As is now widely documented, many algorithms reward—and thereby amplify—the most divisive and thus most attention-grabbing content. Keeping us dialed in—whether for the next round of affirmation or outrage—is essential to their success….(More)”.
Steve Lohr at the New York Times: “L. Rafael Reif, the president of Massachusetts Institute of Technology, delivered an intellectual call to arms to the university’s faculty in November 2017: Help generate insights into how advancing technology has changed and will change the work force, and what policies would create opportunity for more Americans in the digital economy.
That issue, he wrote, is the “defining challenge of our time.”
Three years later, the task force assembled to address it is publishing its wide-ranging conclusions. The 92-page report, “The Work of the Future: Building Better Jobs in an Age of Intelligent Machines,” was released on Tuesday….
Here are four of the key findings in the report:
Most American workers have fared poorly.
It’s well known that those on the top rungs of the job ladder have prospered for decades while wages for average American workers have stagnated. But the M.I.T. analysis goes further. It found, for example, that real wages for men without four-year college degrees have declined 10 to 20 percent since their peak in 1980….
Robots and A.I. are not about to deliver a jobless future.
…The M.I.T. researchers concluded that the change would be more evolutionary than revolutionary. In fact, they wrote, “we anticipate that in the next two decades, industrialized countries will have more job openings than workers to fill them.”…
Worker training in America needs to match the market.
“The key ingredient for success is public-private partnerships,” said Annette Parker, president of South Central College, a community college in Minnesota, and a member of the advisory board to the M.I.T. project.
The schools, nonprofits and corporate-sponsored programs that have succeeded in lifting people into middle-class jobs all echo her point: the need to link skills training to business demand….
Workers need more power, voice and representation.The report calls for raising the minimum wage, broadening unemployment insurance and modifying labor laws to enable collective bargaining in occupations like domestic and home-care workers and freelance workers. Such representation, the report notes, could come from traditional unions or worker advocacy groups like the National Domestic Workers Alliance, Jobs With Justice and the Freelancers Union….(More)”