The Power and Perils of the “Artificial Hand”: Considering AI Through the Ideas of Adam Smith

Curated on Posted on by Stefaan Verhulst

Speech by Gita Gopinath: “…Nowadays, it’s almost impossible to talk about economics without invoking Adam Smith. We take for granted many of his concepts, such as the division of labor and the invisible hand. Yet, at the time when he was writing, these ideas went against the grain. He wasn’t afraid to push boundaries and question established thinking.

Smith grappled with how to advance well-being and prosperity at a time of great change. The Industrial Revolution was ushering in new technologies that would revolutionize the nature of work, create winners and losers, and potentially transform society. But their impact wasn’t yet clear. The Wealth of Nations, for example, was published the same year James Watt unveiled his steam engine.

Today, we find ourselves at a similar inflection point, where a new technology, generative artificial intelligence, could change our lives in spectacular—and possibly existential—ways. It could even redefine what it means to be human.

Given the parallels between Adam Smith’s time and ours, I’d like to propose a thought experiment: If he were alive today, how would Adam Smith have responded to the emergence of this new “artificial hand”?…(More)”.

The Datapreneurs

Curated on Posted on by Stefaan Verhulst

Book by Bob Muglia: “Advances in information technology tend to come at a rapid pace. The computer revolution is barely 80 years old, yet we now have computers in our smartphones that are many times more powerful than machines that once filled rooms. Now, in early 2023, the changes are coming so fast that it is literally dizzying. It seems like nearly every day there is an article about some new Artificial Intelligence (AI) capability—be it ChatGPT, Dall-e 2, or Stable Diffusion. It appears that humanity is on the cusp of creating super-intelligent machines that are smarter than all of us combined. 

This did not really happen overnight. Bob Muglia, a long-time Microsoft executive, former CEO of Snowflake, and currently a tech investor, explains what brought us to this moment in his new book, The Datapreneurs: The Inventors and Innovations that Enable the AI Future. Muglia takes the reader on a journey from the early days of computing and describes how a succession of data management and analytics technologies have helped build today’s economy and society. Muglia tells his story in part by describing some of the inventors he has known, including famous ones like Bill Gates and Sam Altman, the CEO of OpenAI, the creator of ChatGPT, and others that are not household names—yet, anyway—but could help change the world. 

Muglia calls this journey through time and technology “The Arc of Data Innovation.” Each advance on the arc, which he lays out in graphic form in the book, represents one of the key data innovations that have led us to today, where we seem to be on the verge of achieving artificial general intelligence (AGI), producing machines that will be as intelligent as people. He expects this monumental shift to come within the next ten years. Further on, futurists are predicting mass deployment of machines possessing AGI throughout society, when we will be able to harness universal computer intelligence, accelerate innovation, and rapidly address many of today’s seemingly intractable challenges. Muglia believes that the combination of human and machine intelligence could create an era of progress and prosperity where all the people on Earth can have what they need and want without destroying our natural environment…(More)”.

There’s a model for governing AI. Here it is.

Curated on Posted on by Stefaan Verhulst

Article by Jacinda Ardern: “…On March 15, 2019, a terrorist took the lives of 51 members of New Zealand’s Muslim community in Christchurch. The attacker livestreamed his actions for 17 minutes, and the images found their way onto social media feeds all around the planet. Facebook alone blocked or removed 1.5 million copies of the video in the first 24 hours; in that timeframe, YouTube measured one upload per second.

Afterward, New Zealand was faced with a choice: accept that such exploitation of technology was inevitable or resolve to stop it. We chose to take a stand.

We had to move quickly. The world was watching our response and that of social media platforms. Would we regulate in haste? Would the platforms recognize their responsibility to prevent this from happening again?

New Zealand wasn’t the only nation grappling with the connection between violent extremism and technology. We wanted to create a coalition and knew that France had started to work in this space — so I reached out, leader to leader. In my first conversation with President Emmanuel Macron, he agreed there was work to do and said he was keen to join us in crafting a call to action.

We asked industry, civil society and other governments to join us at the table to agree on a set of actions we could all commit to. We could not use existing structures and bureaucracies because they weren’t equipped to deal with this problem.

Within two months of the attack, we launched the Christchurch Call to Action, and today it has more than 120 members, including governments, online service providers and civil society organizations — united by our shared objective to eliminate terrorist and other violent extremist content online and uphold the principle of a free, open and secure internet.

The Christchurch Call is a large-scale collaboration, vastly different from most top-down approaches. Leaders meet annually to confirm priorities and identify areas of focus, allowing the project to act dynamically. And the Call Secretariat — made up of officials from France and New Zealand — convenes working groups and undertakes diplomatic efforts throughout the year. All members are invited to bring their expertise to solve urgent online problems.

While this multi-stakeholder approach isn’t always easy, it has created change. We have bolstered the power of governments and communities to respond to attacks like the one New Zealand experienced. We have created new crisis-response protocols — which enabled companies to stop the 2022 Buffalo attack livestream within two minutes and quickly remove footage from many platforms. Companies and countries have enacted new trust and safety measures to prevent livestreaming of terrorist and other violent extremist content. And we have strengthened the industry-founded Global Internet Forum to Counter Terrorism with dedicated funding, staff and a multi-stakeholder mission.

We’re also taking on some of the more intransigent problems. The Christchurch Call Initiative on Algorithmic Outcomes, a partnership with companies and researchers, was intended to provide better access to the kind of data needed to design online safety measures to prevent radicalization to violence. In practice, it has much wider ramifications, enabling us to reveal more about the ways in which AI and humans interact.

From its start, the Christchurch Call anticipated the emerging challenges of AI and carved out space to address emerging technologies that threaten to foment violent extremism online. The Christchurch Call is actively tackling these AI issues.

Perhaps the most useful thing the Christchurch Call can add to the AI governance debate is the model itself. It is possible to bring companies, government officials, academics and civil society together not only to build consensus but also to make progress. It’s possible to create tools that address the here and now and also position ourselves to face an unknown future. We need this to deal with AI…(More)”.

The Prediction Society: Algorithms and the Problems of Forecasting the Future

Curated on Posted on by Stefaan Verhulst

Paper by Hideyuki Matsumi and Daniel J. Solove: “Predictions about the future have been made since the earliest days of humankind, but today, we are living in a brave new world of prediction. Today’s predictions are produced by machine learning algorithms that analyze massive quantities of personal data. Increasingly, important decisions about people are being made based on these predictions.

Algorithmic predictions are a type of inference. Many laws struggle to account for inferences, and even when they do, the laws lump all inferences together. But as we argue in this Article, predictions are different from other inferences. Predictions raise several unique problems that current law is ill-suited to address. First, algorithmic predictions create a fossilization problem because they reinforce patterns in past data and can further solidify bias and inequality from the past. Second, algorithmic predictions often raise an unfalsiability problem. Predictions involve an assertion about future events. Until these events happen, predictions remain unverifiable, resulting in an inability for individuals to challenge them as false. Third, algorithmic predictions can involve a preemptive intervention problem, where decisions or interventions render it impossible to determine whether the predictions would have come true. Fourth, algorithmic predictions can lead to a self-fulfilling prophecy problem where they actively shape the future they aim to forecast.

More broadly, the rise of algorithmic predictions raises an overarching concern: Algorithmic predictions not only forecast the future but also have the power to create and control it. The increasing pervasiveness of decisions based on algorithmic predictions is leading to a prediction society where individuals’ ability to author their own future is diminished while the organizations developing and using predictive systems are gaining greater power to shape the future…(More)”

How Indigenous Groups Are Leading the Way on Data Privacy

Curated on Posted on by Stefaan Verhulst

Article by Rina Diane Caballar: “Even as Indigenous communities find increasingly helpful uses for digital technology, many worry that outside interests could take over their data and profit from it, much like colonial powers plundered their physical homelands. But now some Indigenous groups are reclaiming control by developing their own data protection technologies—work that demonstrates how ordinary people have the power to sidestep the tech companies and data brokers who hold and sell the most intimate details of their identities, lives and cultures.

When governments, academic institutions or other external organizations gather information from Indigenous communities, they can withhold access to it or use it for other purposes without the consent of these communities.

“The threats of data colonialism are real,” says Tahu Kukutai, a professor at New Zealand’s University of Waikato and a founding member of Te Mana Raraunga, the Māori Data Sovereignty Network. “They’re a continuation of old processes of extraction and exploitation of our land—the same is being done to our information.”

To shore up their defenses, some Indigenous groups are developing new privacy-first storage systems that give users control and agency over all aspects of this information: what is collected and by whom, where it’s stored, how it’s used and, crucially, who has access to it.

Storing data in a user’s device—rather than in the cloud or in centralized servers controlled by a tech company—is an essential privacy feature of these technologies. Rudo Kemper is founder of Terrastories, a free and open-source app co-created with Indigenous communities to map their land and share stories about it. He recalls a community in Guyana that was emphatic about having an offline, on-premise installation of the Terrastories app. To members of this group, the issue was more than just the lack of Internet access in the remote region where they live. “To them, the idea of data existing in the cloud is almost like the knowledge is leaving the territory because it’s not physically present,” Kemper says.

Likewise, creators of Our Data Indigenous, a digital survey app designed by academic researchers in collaboration with First Nations communities across Canada, chose to store their database in local servers in the country rather than in the cloud. (Canada has strict regulations on disclosing personal information without prior consent.) In order to access this information on the go, the app’s developers also created a portable backpack kit that acts as a local area network without connections to the broader Internet. The kit includes a laptop, battery pack and router, with data stored on the laptop. This allows users to fill out surveys in remote locations and back up the data immediately without relying on cloud storage…(More)”.

From the Economic Graph to Economic Insights: Building the Infrastructure for Delivering Labor Market Insights from LinkedIn Data

Curated on Posted on by Stefaan Verhulst

Blog by Patrick Driscoll and Akash Kaura: “LinkedIn’s vision is to create economic opportunity for every member of the global workforce. Since its inception in 2015, the Economic Graph Research and Insights (EGRI) team has worked to make this vision a reality by generating labor market insights such as:

In this post, we’ll describe how the EGRI Data Foundations team (Team Asimov) leverages LinkedIn’s cutting-edge data infrastructure tools such as Unified Metrics Platform, Pinot, and Datahub to ensure we can deliver data and insights robustly, securely, and at scale to a myriad of partners. We will illustrate this through a case study of how we built the pipeline for our most well-known and oft-cited flagship metric: the LinkedIn Hiring Rate…(More)”.

AI and Global Governance: Modalities, Rationales, Tensions

Curated on Posted on by Stefaan Verhulst

Paper by Michael Veale, Kira Matus and Robert Gorwa: “Artificial intelligence (AI) is a salient but polarizing issue of recent times. Actors around the world are engaged in building a governance regime around it. What exactly the “it” is that is being governed, how, by who, and why—these are all less clear. In this review, we attempt to shine some light on those questions, considering literature on AI, the governance of computing, and regulation and governance more broadly. We take critical stock of the different modalities of the global governance of AI that have been emerging, such as ethical councils, industry governance, contracts and licensing, standards, international agreements, and domestic legislation with extraterritorial impact. Considering these, we examine selected rationales and tensions that underpin them, drawing attention to the interests and ideas driving these different modalities. As these regimes become clearer and more stable, we urge those engaging with or studying the global governance of AI to constantly ask the important question of all global governance regimes: Who benefits?…(More)”.

Artificial Intelligence in the COVID-19 Response

Curated on Posted on by Stefaan Verhulst

Report by Sean Mann, Carl Berdahl, Lawrence Baker, and Federico Girosi: “We conducted a scoping review to identify AI applications used in the clinical and public health response to COVID-19. Interviews with stakeholders early in the research process helped inform our research questions and guide our study design. We conducted a systematic search, screening, and full text review of both academic and gray literature…

  • AI is still an emerging technology in health care, with growing but modest rates of adoption in real-world clinical and public health practice. The COVID-19 pandemic showcased the wide range of clinical and public health functions performed by AI as well as the limited evidence available on most AI products that have entered use.
  • We identified 66 AI applications (full list in Appendix A) used to perform a wide range of diagnostic, prognostic, and treatment functions in the clinical response to COVID-19. This included applications used to analyze lung images, evaluate user-reported symptoms, monitor vital signs, predict infections, and aid in breathing tube placement. Some applications were used by health systems to help allocate scarce resources to patients.
  • Many clinical applications were deployed early in the pandemic, and most were used in the United States, other high-income countries, or China. A few applications were used to care for hundreds of thousands or even millions of patients, although most were used to an unknown or limited extent.
  • We identified 54 AI-based public health applications used in the pandemic response. These included AI-enabled cameras used to monitor health-related behavior and health messaging chatbots used to answer questions about COVID-19. Other applications were used to curate public health information, produce epidemiologic forecasts, or help prioritize communities for vaccine allocation and outreach efforts.
  • We found studies supporting the use of 39 clinical applications and 8 public health applications, although few of these were independent evaluations, and we found no clinical trials evaluating any application’s impact on patient health. We found little evidence available on entire classes of applications, including some used to inform care decisions such as patient deterioration monitors.
  • Further research is needed, particularly independent evaluations on application performance and health impacts in real-world care settings. New guidance may be needed to overcome the unique challenges to evaluating AI application impacts on patient- and population-level health outcomes….(More)” – See also: The #Data4Covid19 Review

From LogFrames to Logarithms – A Travel Log

Curated on Posted on by Stefaan Verhulst

Article by Karl Steinacker and Michael Kubach: “..Today, authorities all over the world are experimenting with predictive algorithms. That sounds technical and innocent but as we dive deeper into the issue, we realise that the real meaning is rather specific: fraud detection systems in social welfare payment systems. In the meantime, the hitherto banned terminology had it’s come back: welfare or social safety nets are, since a couple of years, en vogue again. But in the centuries-old Western tradition, welfare recipients must be monitored and, if necessary, sanctioned, while those who work and contribute must be assured that there is no waste. So it comes at no surprise that even today’s algorithms focus on the prime suspect, the individual fraudster, the undeserving poor.

Fraud detection systems promise that the taxpayer will no longer fall victim to fraud and efficiency gains can be re-directed to serve more people. The true extent of welfare fraud is regularly exaggerated  while the costs of such systems is routinely underestimated. A comparison of the estimated losses and investments doesn’t take place. It is the principle to detect and punish the fraudsters that prevail. Other issues don’t rank high either, for example on how to distinguish between honest mistakes and deliberate fraud. And as case workers spent more time entering and analysing data and in front of a computer screen, the less they have time and inclination to talk to real people and to understand the context of their life at the margins of society.

Thus, it can be said that routinely hundreds of thousands of people are being scored. Example Denmark: Here, a system called Udbetaling Danmark was created in 2012 to streamline the payment of welfare benefits. Its fraud control algorithms can access the personal data of millions of citizens, not all of whom receive welfare payments. In contrast to the hundreds of thousands affected by this data mining, the number of cases referred to the Police for further investigation are minute. 

In the city of Rotterdam in the Netherlands every year, data of 30,000 welfare recipients is investigated in order to flag suspected welfare cheats. However, an analysis of its scoring system based on machine learning and algorithms showed systemic discrimination with regard to ethnicity, age, gender, and parenthood. It revealed evidence of other fundamental flaws making the system both inaccurate and unfair. What might appear to a caseworker as a vulnerability is treated by the machine as grounds for suspicion. Despite the scale of data used to calculate risk scores, the output of the system is not better than random guesses. However, the consequences of being flagged by the “suspicion machine” can be drastic, with fraud controllers empowered to turn the lives of suspects inside out.

As reported by the World Bank, the recent Covid-19 pandemic provided a great push to implement digital social welfare systems in the global South. In fact, for the World Bank the so-called Digital Public Infrastructure (DPI), enabling “Digitizing Government to Person Payments (G2Px)”, are as fundamental for social and economic development today as physical infrastructure was for previous generations. Hence, the World Bank is finances globally systems modelled after the Indian Aadhaar system, where more than a billion persons have been registered biometrically. Aadhaar has become, for all intents and purposes, a pre-condition to receive subsidised food and other assistance for 800 million Indian citizens.

Important international aid organisations are not behaving differently from states. The World Food Programme alone holds data of more than 40 million people on its Scope data base. Unfortunately, WFP like other UN organisations, is not subject to data protection laws and the jurisdiction of courts. This makes the communities they have worked with particularly vulnerable.

In most places, the social will become the metric, where logarithms determine the operational conduit for delivering, controlling and withholding assistance, especially welfare payments. In other places, the power of logarithms may go even further, as part of trust systems, creditworthiness, and social credit. These social credit systems for individuals are highly controversial as they require mass surveillance since they aim to track behaviour beyond financial solvency. The social credit score of a citizen might not only suffer from incomplete, or inaccurate data, but also from assessing political loyalties and conformist social behaviour…(More)”.

How AI could take over elections – and undermine democracy

Curated on Posted on by Stefaan Verhulst

Article by Archon Fung and Lawrence Lessig: “Could organizations use artificial intelligence language models such as ChatGPT to induce voters to behave in specific ways?

Sen. Josh Hawley asked OpenAI CEO Sam Altman this question in a May 16, 2023, U.S. Senate hearing on artificial intelligence. Altman replied that he was indeed concerned that some people might use language models to manipulate, persuade and engage in one-on-one interactions with voters.

Altman did not elaborate, but he might have had something like this scenario in mind. Imagine that soon, political technologists develop a machine called Clogger – a political campaign in a black box. Clogger relentlessly pursues just one objective: to maximize the chances that its candidate – the campaign that buys the services of Clogger Inc. – prevails in an election.

While platforms like Facebook, Twitter and YouTube use forms of AI to get users to spend more time on their sites, Clogger’s AI would have a different objective: to change people’s voting behavior.

As a political scientist and a legal scholar who study the intersection of technology and democracy, we believe that something like Clogger could use automation to dramatically increase the scale and potentially the effectiveness of behavior manipulation and microtargeting techniques that political campaigns have used since the early 2000s. Just as advertisers use your browsing and social media history to individually target commercial and political ads now, Clogger would pay attention to you – and hundreds of millions of other voters – individually.

It would offer three advances over the current state-of-the-art algorithmic behavior manipulation. First, its language model would generate messages — texts, social media and email, perhaps including images and videos — tailored to you personally. Whereas advertisers strategically place a relatively small number of ads, language models such as ChatGPT can generate countless unique messages for you personally – and millions for others – over the course of a campaign.

Second, Clogger would use a technique called reinforcement learning to generate a succession of messages that become increasingly more likely to change your vote. Reinforcement learning is a machine-learning, trial-and-error approach in which the computer takes actions and gets feedback about which work better in order to learn how to accomplish an objective. Machines that can play Go, Chess and many video games better than any human have used reinforcement learning.How reinforcement learning works.

Third, over the course of a campaign, Clogger’s messages could evolve in order to take into account your responses to the machine’s prior dispatches and what it has learned about changing others’ minds. Clogger would be able to carry on dynamic “conversations” with you – and millions of other people – over time. Clogger’s messages would be similar to ads that follow you across different websites and social media…(More)”.