How an AI Utopia Would Work

Curated on Posted on by Stefaan Verhulst

Sami Mahroum at Project Syndicate: “…It is more than 500 years since Sir Thomas More found inspiration for the “Kingdom of Utopia” while strolling the streets of Antwerp. So, when I traveled there from Dubai in May to speak about artificial intelligence (AI), I couldn’t help but draw parallels to Raphael Hythloday, the character in Utopia who regales sixteenth-century Englanders with tales of a better world.

As home to the world’s first Minister of AI, as well as museums, academies, and foundations dedicated to studying the future, Dubai is on its own Hythloday-esque voyage. Whereas Europe, in general, has grown increasingly anxious about technological threats to employment, the United Arab Emirates has enthusiastically embraced the labor-saving potential of AI and automation.

There are practical reasons for this. The ratio of indigenous-to-foreign labor in the Gulf states is highly imbalanced, ranging from a high of 67% in Saudi Arabia to a low of 11% in the UAE. And because the region’s desert environment cannot support further population growth, the prospect of replacing people with machines has become increasingly attractive.

But there is also a deeper cultural difference between the two regions. Unlike Western Europe, the birthplace of both the Industrial Revolution and the “Protestant work ethic,” Arab societies generally do not “live to work,” but rather “work to live,” placing a greater value on leisure time. Such attitudes are not particularly compatible with economic systems that require squeezing ever more productivity out of labor, but they are well suited for an age of AI and automation….

Fortunately, AI and data-driven innovation could offer a way forward. In what could be perceived as a kind of AI utopia, the paradox of a bigger state with a smaller budget could be reconciled, because the government would have the tools to expand public goods and services at a very small cost.

The biggest hurdle would be cultural: As early as 1948, the German philosopher Joseph Pieper warned against the “proletarianization” of people and called for leisure to be the basis for culture. Westerners would have to abandon their obsession with the work ethic, as well as their deep-seated resentment toward “free riders.” They would have to start differentiating between work that is necessary for a dignified existence, and work that is geared toward amassing wealth and achieving status. The former could potentially be all but eliminated.

With the right mindset, all societies could start to forge a new AI-driven social contract, wherein the state would capture a larger share of the return on assets, and distribute the surplus generated by AI and automation to residents. Publicly-owned machines would produce a wide range of goods and services, from generic drugs, food, clothes, and housing, to basic research, security, and transportation….(More)”.

AI Ethics — Too Principled to Fail?

Curated on Posted on by Stefaan Verhulst

Paper by Brent Mittelstadt: “AI Ethics is now a global topic of discussion in academic and policy circles. At least 63 public-private initiatives have produced statements describing high-level principles, values, and other tenets to guide the ethical development, deployment, and governance of AI. According to recent meta-analyses, AI Ethics has seemingly converged on a set of principles that closely resemble the four classic principles of medical ethics.

Despite the initial credibility granted to a principled approach to AI Ethics by the connection to principles in medical ethics, there are reasons to be concerned about its future impact on AI development and governance. Significant differences exist between medicine and AI development that suggest a principled approach in the latter may not enjoy success comparable to the former. Compared to medicine, AI development lacks (1) common aims and fiduciary duties, (2) professional history and norms, (3) proven methods to translate principles into practice, and (4) robust legal and professional accountability mechanisms. These differences suggest we should not yet celebrate consensus around high-level principles that hide deep political and normative disagreement….(More)”.

AI & the sustainable development goals: The state of play

Curated on Posted on by Stefaan Verhulst

Report by 2030Vision: “…While the world is making progress in some areas, we are falling behind in delivering the SDGs overall. We need all actors – businesses, governments, academia, multilateral institutions, NGOs, and others – to accelerate and scale their efforts to deliver the SDGs, using every tool at their disposal, including artificial intelligence (AI).

In December 2017, 2030Vision published its first report, Uniting to Deliver Technology for the Global Goals, which addressed the role of digital technology – big data, robotics, internet of things, AI, and other technologies – in achieving the SDGs.

In this paper, we focus on AI for the SDGs. AI extends and amplifies the capacity of human beings to understand and solve complex, dynamic, and interconnected systems challenges like the SDGs. Our main objective was to survey the landscape of research and initiatives on AI and the SDGs to identify key themes and questions in need of further exploration. We also reviewed the state of AI and the SDGs in two sectors – food and agriculture and healthcare – to understand if and how AI is being deployed to address the SDGs and the challenges and opportunities in doing so….(More)”.

The language we use to describe data can also help us fix its problems

Curated on Posted on by Stefaan Verhulst

Luke Stark & Anna Lauren Hoffmann at Quartz: “Data is, apparently, everything.

It’s the “new oil” that fuels online business. It comes in floods or tsunamis. We access it via “streams” or “fire hoses.” We scrape it, mine it, bank it, and clean it. (Or, if you prefer your buzzphrases with a dash of ageism and implicit misogyny, big data is like “teenage sex,” while working with it is “the sexiest job” of the century.)

These data metaphors can seem like empty cliches, but at their core they’re efforts to come to grips with the continuing onslaught of connected devices and the huge amounts of data they generate.

In a recent article, we—an algorithmic-fairness researcher at Microsoft and a data-ethics scholar at the University of Washington—push this connection one step further. More than simply helping us wrap our collective heads around data-fueled technological change, we set out to learn what these metaphors can teach us about the real-life ethics of collecting and handling data today.

Instead of only drawing from the norms and commitments of computer science, information science, and statistics, what if we looked at the ethics of the professions evoked by our data metaphors instead?…(More)”.

Developing Artificially Intelligent Justice

Curated on Posted on by Stefaan Verhulst

Paper by Richard M. Re and Alicia Solow-Niederman: “Artificial intelligence, or AI, promises to assist, modify, and replace human decision-making, including in court. AI already supports many aspects of how judges decide cases, and the prospect of “robot judges” suddenly seems plausible—even imminent. This Article argues that AI adjudication will profoundly affect the adjudicatory values held by legal actors as well as the public at large. The impact is likely to be greatest in areas, including criminal justice and appellate decision-making, where “equitable justice,” or discretionary moral judgment, is frequently considered paramount. By offering efficiency and at least an appearance of impartiality, AI adjudication will both foster and benefit from a turn toward “codified justice,” an adjudicatory paradigm that favors standardization above discretion. Further, AI adjudication will generate a range of concerns relating to its tendency to make the legal system more incomprehensible, data-based, alienating, and disillusioning. And potential responses, such as crafting a division of labor between human and AI adjudicators, each pose their own challenges. The single most promising response is for the government to play a greater role in structuring the emerging market for AI justice, but auspicious reform proposals would borrow several interrelated approaches. Similar dynamics will likely extend to other aspects of government, such that choices about how to incorporate AI in the judiciary will inform the future path of AI development more broadly….(More)”.

Data & Policy: A new venue to study and explore policy–data interaction

Curated on Posted on by Stefaan Verhulst

Opening editorial by Stefaan G. Verhulst, Zeynep Engin and Jon Crowcroft: “…Policy–data interactions or governance initiatives that use data have been the exception rather than the norm, isolated prototypes and trials rather than an indication of real, systemic change. There are various reasons for the generally slow uptake of data in policymaking, and several factors will have to change if the situation is to improve. ….

  • Despite the number of successful prototypes and small-scale initiatives, policy makers’ understanding of data’s potential and its value proposition generally remains limited (Lutes, 2015). There is also limited appreciation of the advances data science has made the last few years. This is a major limiting factor; we cannot expect policy makers to use data if they do not recognize what data and data science can do.
  • The recent (and justifiable) backlash against how certain private companies handle consumer data has had something of a reverse halo effect: There is a growing lack of trust in the way data is collected, analyzed, and used, and this often leads to a certain reluctance (or simply risk-aversion) on the part of officials and others (Engin, 2018).
  • Despite several high-profile open data projects around the world, much (probably the majority) of data that could be helpful in governance remains either privately held or otherwise hidden in silos (Verhulst and Young, 2017b). There remains a shortage not only of data but, more specifically, of high-quality and relevant data.
  • With few exceptions, the technical capacities of officials remain limited, and this has obviously negative ramifications for the potential use of data in governance (Giest, 2017).
  • It’s not just a question of limited technical capacities. There is often a vast conceptual and values gap between the policy and technical communities (Thompson et al., 2015; Uzochukwu et al., 2016); sometimes it seems as if they speak different languages. Compounding this difference in world views is the fact that the two communities rarely interact.
  • Yet, data about the use and evidence of the impact of data remain sparse. The impetus to use more data in policy making is stymied by limited scholarship and a weak evidential basis to show that data can be helpful and how. Without such evidence, data advocates are limited in their ability to make the case for more data initiatives in governance.
  • Data are not only changing the way policy is developed, but they have also reopened the debate around theory- versus data-driven methods in generating scientific knowledge (Lee, 1973; Kitchin, 2014; Chivers, 2018; Dreyfuss, 2017) and thus directly questioning the evidence base to utilization and implementation of data within policy making. A number of associated challenges are being discussed, such as: (i) traceability and reproducibility of research outcomes (due to “black box processing”); (ii) the use of correlation instead of causation as the basis of analysis, biases and uncertainties present in large historical datasets that cause replication and, in some cases, amplification of human cognitive biases and imperfections; and (iii) the incorporation of existing human knowledge and domain expertise into the scientific knowledge generation processes—among many other topics (Castelvecchi, 2016; Miller and Goodchild, 2015; Obermeyer and Emanuel, 2016; Provost and Fawcett, 2013).
  • Finally, we believe that there should be a sound under-pinning a new theory of what we call Policy–Data Interactions. To date, in reaction to the proliferation of data in the commercial world, theories of data management,1 privacy,2 and fairness3 have emerged. From the Human–Computer Interaction world, a manifesto of principles of Human–Data Interaction (Mortier et al., 2014) has found traction, which intends reducing the asymmetry of power present in current design considerations of systems of data about people. However, we need a consistent, symmetric approach to consideration of systems of policy and data, how they interact with one another.

All these challenges are real, and they are sticky. We are under no illusions that they will be overcome easily or quickly….

During the past four conferences, we have hosted an incredibly diverse range of dialogues and examinations by key global thought leaders, opinion leaders, practitioners, and the scientific community (Data for Policy, 2015, 2016, 2017, 2019). What became increasingly obvious was the need for a dedicated venue to deepen and sustain the conversations and deliberations beyond the limitations of an annual conference. This leads us to today and the launch of Data & Policy, which aims to confront and mitigate the barriers to greater use of data in policy making and governance.

Data & Policy is a venue for peer-reviewed research and discussion about the potential for and impact of data science on policy. Our aim is to provide a nuanced and multistranded assessment of the potential and challenges involved in using data for policy and to bridge the “two cultures” of science and humanism—as CP Snow famously described in his lecture on “Two Cultures and the Scientific Revolution” (Snow, 1959). By doing so, we also seek to bridge the two other dichotomies that limit an examination of datafication and is interaction with policy from various angles: the divide between practice and scholarship; and between private and public…

So these are our principles: scholarly, pragmatic, open-minded, interdisciplinary, focused on actionable intelligence, and, most of all, innovative in how we will share insight and pushing at the boundaries of what we already know and what already exists. We are excited to launch Data & Policy with the support of Cambridge University Press and University College London, and we’re looking for partners to help us build it as a resource for the community. If you’re reading this manifesto it means you have at least a passing interest in the subject; we hope you will be part of the conversation….(More)”.

Introducing ‘AI Commons’: A framework for collaboration to achieve global impact

Curated on Posted on by Stefaan Verhulst

Press Release: “Last week’s 3rd annual AI for Good Global Summit once again showcased the growing number of Artificial Intelligence (AI) projects with promise to advance the United Nations Sustainable Development Goals (SDGs).

Now, using the Summit’s momentum, AI innovators and humanitarian leaders are prepared to take the ‘AI for Good’ movement to the next level.

They are working together to launch an ‘AI Commons’ that aims to scale AI for Good projects and maximize their impact across the world.

The AI Commons will enable AI adopters to connect with AI specialists and data owners to align incentives for innovation and develop AI solutions to precisely defined problems.

“The concept of AI Commons has developed over three editions of the Summit and is now motivating implementation,” said ITU Secretary-General Houlin Zhao in closing remarks to the summit. “AI and data need to be a shared resource if we are serious about scaling AI for good. The community supporting the Summit is creating infrastructure to scale-up their collaboration − to convert the principles underlying the Summit into global impact.”…

The AI Commons will provide an open framework for collaboration, a decentralized system to democratize problem solving with AI.

It aims to be a “knowledge space”, says Banifatemi, answering a key question: “How can problem solving with AI become common knowledge?”

“The goal is to be an open initiative, like a Linux effort, like an open-source network, where everyone can participate and we jointly share and we create an abundance of knowledge, knowledge of how we can solve problems with AI,” said Banifatemi.

AI development and application will build on the state of the art, enabling AI solutions to scale with the help of shared datasets, testing and simulation environments, AI models and associated software, and storage and computing resources….(More)”.

AI and the Global South: Designing for Other Worlds

Curated on Posted on by Stefaan Verhulst

Chapter by Chinmayi Arun in Markus D. Dubber, Frank Pasquale, and Sunit Das (eds.), The Oxford Handbook of Ethics of AI: “This chapter is about the ways in which AI affects, and will continue to affect, the Global South. It highlights why the design and deployment of AI in the South should concern us. 

Towards this, it discusses what is meant by the South. The term has a history connected with the ‘Third World’ and has referred to countries that share post-colonial history and certain development goals. However scholars have expanded and refined on it to include different kinds of marginal, disenfranchised populations such that the South is now a plural concept – there are Souths. 

The risks of the ways in which AI affects Southern populations include concerns of discrimination, bias, oppression, exclusion and bad design. These can be exacerbated in the context of vulnerable populations, especially those without access to human rights law or institutional remedies. This Chapter outlines these risks as well as the international human rights law that is applicable. It argues that a human rights, centric, inclusive, empowering context-driven approach is necessary….(More)”.

The Tricky Ethics of Using YouTube Videos for Academic Research

Curated on Posted on by Stefaan Verhulst

Jane C.Hu in P/S Magazine: “…But just because something is legal doesn’t mean it’s ethical. That doesn’t mean it’s necessarily unethical, either, but it’s worth asking questions about how and why researchers use social media posts, and whether those uses could be harmful. I was once a researcher who had to obtain human-subjects approval from a university institutional review board, and I know it can be a painstaking application process with long wait times. Collecting data from individuals takes a long time too. If you could just sub in YouTube videos in place of collecting your own data, that saves time, money, and effort. But that could be at the expense of the people whose data you’re scraping.

But, you might say, if people don’t want to be studied online, then they shouldn’t post anything. But most people don’t fully understand what “publicly available” really means or its ramifications. “You might know intellectually that technically anyone can see a tweet, but you still conceptualize your audience as being your 200 Twitter followers,” Fiesler says. In her research, she’s found that the majority of people she’s polled have no clue that researchers study public tweets.

Some may disagree that it’s researchers’ responsibility to work around social media users’ ignorance, but Fiesler and others are calling for their colleagues to be more mindful about any work that uses publicly available data. For instance, Ashley Patterson, an assistant professor of language and literacy at Penn State University, ultimately decided to use YouTube videos in her dissertation work on biracial individuals’ educational experiences. That’s a decision she arrived at after carefully considering her options each step of the way. “I had to set my own levels of ethical standards and hold myself to it, because I knew no one else would,” she says. One of Patterson’s first steps was to ask herself what YouTube videos would add to her work, and whether there were any other ways to collect her data. “It’s not a matter of whether it makes my life easier, or whether it’s ‘just data out there’ that would otherwise go to waste. The nature of my question and the response I was looking for made this an appropriate piece [of my work],” she says.

Researchers may also want to consider qualitative, hard-to-quantify contextual cues when weighing ethical decisions. What kind of data is being used? Fiesler points out that tweets about, say, a television show are way less personal than ones about a sensitive medical condition. Anonymized written materials, like Facebook posts, could be less invasive than using someone’s face and voice from a YouTube video. And the potential consequences of the research project are worth considering too. For instance, Fiesler and other critics have pointed out that researchers who used YouTube videos of people documenting their experience undergoing hormone replacement therapy to train an artificial intelligence to identify trans people could be putting their unwitting participants in danger. It’s not obvious how the results of Speech2Face will be used, and, when asked for comment, the paper’s researchers said they’d prefer to quote from their paper, which pointed to a helpful purpose: providing a “representative face” based on the speaker’s voice on a phone call. But one can also imagine dangerous applications, like doxing anonymous YouTubers.

One way to get ahead of this, perhaps, is to take steps to explicitly inform participants their data is being used. Fiesler says that, when her team asked people how they’d feel after learning their tweets had been used for research, “not everyone was necessarily super upset, but most people were surprised.” They also seemed curious; 85 percent of participants said that, if their tweet were included in research, they’d want to read the resulting paper. “In human-subjects research, the ethical standard is informed consent, but inform and consent can be pulled apart; you could potentially inform people without getting their consent,” Fiesler suggests….(More)”.