Society won’t trust A.I. until business earns that trust

Curated on Posted on by Stefaan Verhulst

Article by François Candelon, Rodolphe Charme di Carlo and Steven D. Mills: “…The concept of a social license—which was born when the mining industry, and other resource extractors, faced opposition to projects worldwide—differs from the other rules governing A.I.’s use. Academics such as Leeora Black and John Morrison, in the book The Social License: How to Keep Your Organization Legitimate,define the social license as “the negotiation of equitable impacts and benefits in relation to its stakeholders over the near and longer term. It can range from the informal, such as an implicit contract, to the formal, like a community benefit agreement.” 

The social license isn’t a document like a government permit; it’s a form of acceptance that companies must gain through consistent and trustworthy behavior as well as stakeholder interactions. Thus, a social license for A.I. will be a socially constructed perception that a company has secured the right to use the technology for specific purposes in the markets in which it operates. 

Companies cannot award themselves social licenses; they will have to win them by proving they can be trusted. As Morrison argued in 2014, akin to the capability to dig a mine, the fact that an A.I.-powered solution is technologically feasible doesn’t mean that society will find its use morally and ethically acceptable. And losing the social license will have dire consequences, as natural resource companies, such as Shell and BP, have learned in the past…(More)”

The Political Philosophy of AI: An Introduction

Curated on Posted on by Stefaan Verhulst

Book by Mark Coeckelbergh: “Political issues people care about such as racism, climate change, and democracy take on new urgency and meaning in the light of technological developments such as AI. How can we talk about the politics of AI while moving beyond mere warnings and easy accusations?

This is the first accessible introduction to the political challenges related to AI. Using political philosophy as a unique lens through which to explore key debates in the area, the book shows how various political issues are already impacted by emerging AI technologies: from justice and discrimination to democracy and surveillance. Revealing the inherently political nature of technology, it offers a rich conceptual toolbox that can guide efforts to deal with the challenges raised by what turns out to be not only artificial intelligence but also artificial power.

This timely and original book will appeal to students and scholars in philosophy of technology and political philosophy, as well as tech developers, innovation leaders, policy makers, and anyone interested in the impact of technology on society…(More)”.

The Use Of Digitalisation and Artificial Intelligence in Migration Management

Curated on Posted on by Stefaan Verhulst

Joint EMN-OECD inform: “…In view of the dynamic nature of the migration policy landscape and in the context of the new Pact on Migration and Asylum, this series explores existing trends, innovative methods and approaches in migration management and will be used as a basis for further policy reflection at EU level. 

This inform builds on trends identified in the EMN-OECD series on migration management informs on COVID-19 in the migration area. Its scope includes EU Member States, EMN observer countries as well as OECD countries. This inform aims to explore the role of new digital technologies in the management of migration and asylum. It focuses on a number of specific areas in migration, acquisition of citizenship, asylum procedures and border control management where digital technologies may be used (e.g. digitalisation of application processes, use of video conferencing for remote interviews, use of artificial intelligence (AI) to assist decision making processes, use of blockchain technology). It also considers the implications of using these types of technologies on fundamental rights…(More)”.

Artificial Intelligence Bias and Discrimination: Will We Pull the Arc of the Moral Universe Towards Justice?

Curated on Posted on by Stefaan Verhulst

Paper by Emile Loza de Siles: “In 1968, the Reverend Martin Luther King Jr. foresaw the inevitability of society’s eventual triumph over the deep racism of his time and the stain that continues to cast its destructive oppressive pall today. From the pulpit of the nation’s church, Dr King said, “We shall overcome because the arc of the moral universe is long but it bends toward justice”. More than 40 years later, Eric Holder, the first African American United States Attorney General, agreed, but only if people acting with conviction exert to pull that arc towards justice.

With artificial intelligence (AI) bias and discrimination rampant, the need to pull the moral arc towards algorithmic justice is urgent. This article offers empowering clarity by conceptually bifurcating AI bias problems into AI bias engineering and organisational AI governance problems, revealing proven legal development pathways to protect against the corrosive harms of AI bias and discrimination…(More)”.

Facial Recognition Plan from IRS Raises Big Concerns

Curated on Posted on by Stefaan Verhulst

Article by James Hendler: “The U.S. Internal Revenue Service is planning to require citizens to create accounts with a private facial recognition company in order to file taxes online. The IRS is joining a growing number of federal and state agencies that have contracted with ID.me to authenticate the identities of people accessing services.

The IRS’s move is aimed at cutting down on identity theft, a crime that affects millions of Americans. The IRS, in particular, has reported a number of tax filings from people claiming to be others, and fraud in many of the programs that were administered as part of the American Relief Plan has been a major concern to the government.

The IRS decision has prompted a backlash, in part over concerns about requiring citizens to use facial recognition technology and in part over difficulties some people have had in using the system, particularly with some state agencies that provide unemployment benefits. The reaction has prompted the IRS to revisit its decision.

As a computer science researcher and the chair of the Global Technology Policy Council of the Association for Computing Machinery, I have been involved in exploring some of the issues with government use of facial recognition technology, both its use and its potential flaws. There have been a great number of concerns raised over the general use of this technology in policing and other government functions, often focused on whether the accuracy of these algorithms can have discriminatory affects. In the case of ID.me, there are other issues involved as well….(More)”.

The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Paper by Erik Brynjolfsson: “In 1950, Alan Turing proposed an “imitation game” as the ultimate test of whether a machine was intelligent: could a machine imitate a human so well that its answers to questions are indistinguishable from those of a human. Ever since, creating intelligence that matches human intelligence has implicitly or explicitly been the goal of thousands of researchers, engineers and entrepreneurs. The benefits of human-like artificial intelligence (HLAI) include soaring productivity, increased leisure, and perhaps most profoundly, a better understanding of our own minds.

But not all types of AI are human-like—in fact, many of the most powerful systems are very different from humans —and an excessive focus on developing and deploying HLAI can lead us into a trap. As machines become better substitutes for human labor, workers lose economic and political bargaining power and become increasingly dependent on those who control the technology. In contrast, when AI is focused on augmenting humans rather than mimicking them, then humans retain the power to insist on a share of the value created. What’s more, augmentation creates new capabilities and new products and services, ultimately generating far more value than merely human-like AI. While both types of AI can be enormously beneficial, there are currently excess incentives for automation rather than augmentation among technologists, business executives, and policymakers…(More)”

Artificial intelligence searches for the human touch

Curated on Posted on by Stefaan Verhulst

Madhumita Murgia at the Financial Times: “For many outside the tech world, “data” means soulless numbers. Perhaps it causes their eyes to glaze over with boredom. Whereas for computer scientists, data means rows upon rows of rich raw matter, there to be manipulated.

Yet the siren call of “big data” has been more muted recently. There is a dawning recognition that, in tech such as artificial intelligence, “data” equals human beings.

AI-driven algorithms are increasingly impinging upon our everyday lives. They assist in making decisions across a spectrum that ranges from advertising products to diagnosing medical conditions. It’s already clear that the impact of such systems cannot be understood simply by examining the underlying code or even the data used to build them. We must look to people for answers as well.

Two recent studies do exactly that. The first is an Ipsos Mori survey of more than 19,000 people across 28 countries on public attitudes to AI, the second a University of Tokyo study investigating Japanese people’s views on the morals and ethics of AI usage. By inviting those with lived experiences to participate, both capture the mood among those researching the impact of artificial intelligence.

The Ipsos Mori survey found that 60 per cent of adults expect that products and services using AI will profoundly change their daily lives in the next three to five years. Latin Americans in particular think AI will trigger changes in social needs such as education and employment, while Chinese respondents were most likely to believe it would change transportation and their homes.

The geographic and demographic differences in both surveys are revealing. Globally, about half said AI technology has more benefits than drawbacks, while two-thirds felt gloomy about its impact on their individual freedom and legal rights. But figures for different countries show a significant split within this. Citizens from the “global south”, a catch-all term for non-western countries, were much more likely to “have a positive outlook on the impact of AI-powered products and services in their lives”. Large majorities in China (76 per cent) and India (68 per cent) said they trusted AI companies. In contrast, only 35 per cent in the UK, France and US expressed similar trust.

In the University of Tokyo study, researchers discovered that women, older people and those with more subject knowledge were most wary of the risks of AI, perhaps an indicator of their own experiences with these systems. The Japanese mathematician Noriko Arai has, for instance, written about sexist and gender stereotypes encoded into “female” carer and receptionist robots in Japan.

The surveys underline the importance of AI designers recognising that we don’t all belong to one homogenous population, with the same understanding of the world. But they’re less insightful about why differences exist….(More)”.

Octagon Measurement: Public Attitudes toward AI Ethics

Curated on Posted on by Stefaan Verhulst

Paper by Yuko Ikkatai, Tilman Hartwig, Naohiro Takanashi & Hiromi M. Yokoyama: “Artificial intelligence (AI) is rapidly permeating our lives, but public attitudes toward AI ethics have only partially been investigated quantitatively. In this study, we focused on eight themes commonly shared in AI guidelines: “privacy,” “accountability,” “safety and security,” “transparency and explainability,” “fairness and non-discrimination,” “human control of technology,” “professional responsibility,” and “promotion of human values.” We investigated public attitudes toward AI ethics using four scenarios in Japan. Through an online questionnaire, we found that public disagreement/agreement with using AI varied depending on the scenario. For instance, anxiety over AI ethics was high for the scenario where AI was used with weaponry. Age was significantly related to the themes across the scenarios, but gender and understanding of AI differently related depending on the themes and scenarios. While the eight themes need to be carefully explained to the participants, our Octagon measurement may be useful for understanding how people feel about the risks of the technologies, especially AI, that are rapidly permeating society and what the problems might be…(More)”.

From Poisons to Antidotes: Algorithms as Democracy Boosters

Curated on Posted on by Stefaan Verhulst

Paper by Paolo Cavaliere and Graziella Romeo: “Under what conditions can artificial intelligence contribute to political processes without undermining their legitimacy? Thanks to the ever-growing availability of data and the increasing power of decision-making algorithms, the future of political institutions is unlikely to be anything similar to what we have known throughout the last century, possibly with Parliaments deprived of their traditional authority and public decision-making processes largely unaccountable. This paper discusses and challenges these concerns by suggesting a theoretical framework under which algorithmic decision-making is compatible with democracy and, most relevantly, can offer a viable solution to counter the rise of populist rhetoric in the governance arena. Such a framework is based on three pillars: a. understanding the civic issues that are subjected to automated decision-making; b. controlling the issues that are assigned to AI; and c. evaluating and challenging the outputs of algorithmic decision-making….(More)”.

Trove of unique health data sets could help AI predict medical conditions earlier

Curated on Posted on by Stefaan Verhulst

Madhumita Murgia at the Financial Times: “…Ziad Obermeyer, a physician and machine learning scientist at the University of California, Berkeley, launched Nightingale Open Science last month — a treasure trove of unique medical data sets, each curated around an unsolved medical mystery that artificial intelligence could help to solve.

The data sets, released after the project received $2m of funding from former Google chief executive Eric Schmidt, could help to train computer algorithms to predict medical conditions earlier, triage better and save lives.

The data include 40 terabytes of medical imagery, such as X-rays, electrocardiogram waveforms and pathology specimens, from patients with a range of conditions, including high-risk breast cancer, sudden cardiac arrest, fractures and Covid-19. Each image is labelled with the patient’s medical outcomes, such as the stage of breast cancer and whether it resulted in death, or whether a Covid patient needed a ventilator.

Obermeyer has made the data sets free to use and mainly worked with hospitals in the US and Taiwan to build them over two years. He plans to expand this to Kenya and Lebanon in the coming months to reflect as much medical diversity as possible.

“Nothing exists like it,” said Obermeyer, who announced the new project in December alongside colleagues at NeurIPS, the global academic conference for artificial intelligence. “What sets this apart from anything available online is the data sets are labelled with the ‘ground truth’, which means with what really happened to a patient and not just a doctor’s opinion.”…

The Nightingale data sets were among dozens proposed this year at NeurIPS.

Other projects included a speech data set of Mandarin and eight subdialects recorded by 27,000 speakers in 34 cities in China; the largest audio data set of Covid respiratory sounds, such as breathing, coughing and voice recordings, from more than 36,000 participants to help screen for the disease; and a data set of satellite images covering the entire country of South Africa from 2006 to 2017, divided and labelled by neighbourhood, to study the social effects of spatial apartheid.

Elaine Nsoesie, a computational epidemiologist at the Boston University School of Public Health, said new types of data could also help with studying the spread of diseases in diverse locations, as people from different cultures react differently to illnesses.

She said her grandmother in Cameroon, for example, might think differently than Americans do about health. “If someone had an influenza-like illness in Cameroon, they may be looking for traditional, herbal treatments or home remedies, compared to drugs or different home remedies in the US.”

Computer scientists Serena Yeung and Joaquin Vanschoren, who proposed that research to build new data sets should be exchanged at NeurIPS, pointed out that the vast majority of the AI community still cannot find good data sets to evaluate their algorithms. This meant that AI researchers were still turning to data that were potentially “plagued with bias”, they said. “There are no good models without good data.”…(More)”.