Lethal AI weapons are here: how can we control them?

Curated on Posted on by Stefaan Verhulst

Article by David Adam: “The development of lethal autonomous weapons (LAWs), including AI-equipped drones, is on the rise. The US Department of Defense, for example, has earmarked US$1 billion so far for its Replicator programme, which aims to build a fleet of small, weaponized autonomous vehicles. Experimental submarines, tanks and ships have been made that use AI to pilot themselves and shoot. Commercially available drones can use AI image recognition to zero in on targets and blow them up. LAWs do not need AI to operate, but the technology adds speed, specificity and the ability to evade defences. Some observers fear a future in which swarms of cheap AI drones could be dispatched by any faction to take out a specific person, using facial recognition.

Warfare is a relatively simple application for AI. “The technical capability for a system to find a human being and kill them is much easier than to develop a self-driving car. It’s a graduate-student project,” says Stuart Russell, a computer scientist at the University of California, Berkeley, and a prominent campaigner against AI weapons. He helped to produce a viral 2017 video called Slaughterbots that highlighted the possible risks.

The emergence of AI on the battlefield has spurred debate among researchers, legal experts and ethicists. Some argue that AI-assisted weapons could be more accurate than human-guided ones, potentially reducing both collateral damage — such as civilian casualties and damage to residential areas — and the numbers of soldiers killed and maimed, while helping vulnerable nations and groups to defend themselves. Others emphasize that autonomous weapons could make catastrophic mistakes. And many observers have overarching ethical concerns about passing targeting decisions to an algorithm…(More)”

The Future Data Economy

Curated on Posted on by Stefaan Verhulst

Report by the IE University’s Center for the Governance of Change: “…summarizes the ideas and recommendations of a year of research into the possibilities of creating a data economy that is fair, competitive and secure, carried out together with experts in the field such as Andrea Renda and Stefaan Verhulst.

According to the report, the data economy represents “a fundamental reconfiguration of how value is generated, exchanged, and understood in our world today” but it remains deeply misunderstood:

  • The authors argue that data’s particular characteristics make it different from other commodities and therefore more difficult to regulate.
  • Optimizing data flows defies the sort of one-size-fits-all solutions that policymakers tend to search for in other domains, requiring instead a more nuanced, case-by-case approach. 
  • Policymakers need to strike a delicate balance between making data sufficiently accessible to foster innovation, competition, and economic growth, while regulating its access and use to protect privacy, security, and consumer rights.

The report identifies additional overarching principles that lay the groundwork for a more coherent regulatory framework and a more robust social contract in the future data economy:

  • A paradigm shift towards greater collaboration on all fronts to address the challenges and harness the opportunities of the data economy.
  • Greater data literacy at all levels of society to make better decisions, manage risks more effectively, and harness the potential of data responsibly.
  • Regaining social trust, not only a moral imperative but also a prerequisite for the long-term sustainability and viability of data governance models.

To realize this vision, the report advances 15 specific recommendations for policymakers, including:

  • Enshrining people’s digital rights through robust regulatory measures that empower them with genuine control over their digital experiences.
  • Investing in data stewards to increase companies’ ability to recognize opportunities for collaboration and respond to external data requests. 
  • Designing liability frameworks to properly identify responsibility in cases of data misuse…(More)”

The Open Data Maturity Ranking is shoddy – it badly needs to be re-thought

Curated on Posted on by Stefaan Verhulst

Article by Olesya Grabova: “Digitalising government is essential for Europe’s future innovation and economic growth and one of the keys to achieving this is open data – information that public entities gather, create, or fund, and it’s accessible to all to freely use.

This includes everything from public budget details to transport schedules. Open data’s benefits are vast — it fuels research, boosts innovation, and can even save lives in wartime through the creation of chatbots with information about bomb shelter locations. It’s estimated that its economic value will reach a total of EUR 194 billion for EU countries and the UK by 2030.

This is why correctly measuring European countries’ progress in open data is so important. And that’s why the European Commission developed the Open Data Maturity (ODM) ranking, which annually measures open data quality, policies, online portals, and impact across 35 European countries.

Alas, however, it doesn’t work as well as it should and this needs to be addressed.

A closer look at the report’s overall approach reveals the ranking hardly reflects countries’ real progress when it comes to open data. This flawed system, rather than guiding countries towards genuine improvement, risks misrepresenting their actual progress and misleads citizens about their country’s advancements, which further stalls opportunities for innovation.

Take Slovakia. It’s apparently the biggest climber,  leaping from 29th to 10th place in just over a year. One would expect that the country has made significant progress in making public sector information available and stimulating its reuse – one of the OMD assessment’s key elements.

A deeper examination reveals that this isn’t the case. Looking at the ODM’s methodology highlights where it falls short… and how it can be fixed…(More)”.

AI-Powered World Health Chatbot Is Flubbing Some Answers

Curated on Posted on by Stefaan Verhulst

Article by Jessica Nix: “The World Health Organization is wading into the world of AI to provide basic health information through a human-like avatar. But while the bot responds sympathetically to users’ facial expressions, it doesn’t always know what it’s talking about.

SARAH, short for Smart AI Resource Assistant for Health, is a virtual health worker that’s available to talk 24/7 in eight different languages to explain topics like mental health, tobacco use and healthy eating. It’s part of the WHO’s campaign to find technology that can both educate people and fill staffing gaps with the world facing a health-care worker shortage.

WHO warns on its website that this early prototype, introduced on April 2, provides responses that “may not always be accurate.” Some of SARAH’s AI training is years behind the latest data. And the bot occasionally provides bizarre answers, known as hallucinations in AI models, that can spread misinformation about public health.The WHO’s artificial intelligence tool provides public health information via a lifelike avatar.Source: Bloomberg

SARAH doesn’t have a diagnostic feature like WebMD or Google. In fact, the bot is programmed to not talk about anything outside of the WHO’s purview, including questions on specific drugs. So SARAH often sends people to a WHO website or says that users should “consult with your health-care provider.”

“It lacks depth,” Ramin Javan, a radiologist and researcher at George Washington University, said. “But I think it’s because they just don’t want to overstep their boundaries and this is just the first step.”..(More)”

Using Artificial Intelligence to Map the Earth’s Forests

Curated on Posted on by Stefaan Verhulst

Article from Meta and World Resources Institute: “Forests harbor most of Earth’s terrestrial biodiversity and play a critical role in the uptake of carbon dioxide from the atmosphere. Ecosystem services provided by forests underpin an essential defense against the climate and biodiversity crises. However, critical gaps remain in the scientific understanding of the structure and extent of global forests. Because the vast majority of existing data on global forests is derived from low to medium resolution satellite imagery (10 or 30 meters), there is a gap in the scientific understanding of dynamic and more dispersed forest systems such as agroforestry, drylands forests, and alpine forests, which together constitute more than a third of the world’s forests. 

Today, Meta and World Resources Institute are launching a global map of tree canopy height at a 1-meter resolution, allowing the detection of single trees at a global scale. In an effort to advance open source forest monitoring, all canopy height data and artificial intelligence models are free and publicly available…(More)”.

The economic research policymakers actually need

Curated on Posted on by Stefaan Verhulst

Blog by Jed Kolko: “…The structure of academia just isn’t set up to produce the kind of research many policymakers need. Instead, top academic journal editors and tenure committees reward research that pushes the boundaries of the discipline and makes new theoretical or empirical contributions. And most academic papers presume familiarity with the relevant academic literature, making it difficult for anyone outside of academia to make the best possible use of them.

The most useful research often came instead from regional Federal Reserve banks, non-partisan think-tanks, the corporate sector, and from academics who had the support, freedom, or job security to prioritize policy relevance. It generally fell into three categories:

  1. New measures of the economy
  2. Broad literature reviews
  3. Analyses that directly quantify or simulate policy decisions.

If you’re an economic researcher and you want to do work that is actually helpful for policymakers — and increases economists’ influence in government — aim for one of those three buckets.

The pandemic and its aftermath brought an urgent need for data at higher frequency, with greater geographic and sectoral detail, and about ways the economy suddenly changed. Some of the most useful research contributions during that period were new data and measures of the economy: they were valuable as ingredients rather than as recipes or finished meals. Here are some examples:

A Brief History of Automations That Were Actually People

Curated on Posted on by Stefaan Verhulst

Article by Brian Contreras: “If you’ve ever asked a chatbot a question and received nonsensical gibberish in reply, you already know that “artificial intelligence” isn’t always very intelligent.

And sometimes it isn’t all that artificial either. That’s one of the lessons from Amazon’s recent decision to dial back its much-ballyhooed “Just Walk Out” shopping technology, a seemingly science-fiction-esque software that actually functioned, in no small part, thanks to behind-the-scenes human labor.

This phenomenon is nicknamed “fauxtomation” because it “hides the human work and also falsely inflates the value of the ‘automated’ solution,” says Irina Raicu, director of the Internet Ethics program at Santa Clara University’s Markkula Center for Applied Ethics.

Take Just Walk Out: It promises a seamless retail experience in which customers at Amazon Fresh groceries or third-party stores can grab items from the shelf, get billed automatically and leave without ever needing to check out. But Amazon at one point had more than 1,000 workers in India who trained the Just Walk Out AI model—and manually reviewed some of its sales—according to an article published last year on the Information, a technology business website.

An anonymous source who’d worked on the Just Walk Out technology told the outlet that as many as 700 human reviews were needed for every 1,000 customer transactions. Amazon has disputed the Information’s characterization of its process. A company representative told Scientific American that while Amazon “can’t disclose numbers,” Just Walk Out has “far fewer” workers annotating shopping data than has been reported. In an April 17 blog post, Dilip Kumar, vice president of Amazon Web Services applications, wrote that “this is no different than any other AI system that places a high value on accuracy, where human reviewers are common.”…(More)”

Global Contract-level Public Procurement Dataset

Curated on Posted on by Stefaan Verhulst

Paper by Mihály Fazekas et al: “One-third of total government spending across the globe goes to public procurement, amounting to about 10 trillion dollars a year. Despite its vast size and crucial importance for economic and political developments, there is a lack of globally comparable data on contract awards and tenders run. To fill this gap, this article introduces the Global Public Procurement Dataset (GPPD). Using web scraping methods, we collected official public procurement data on over 72 million contracts from 42 countries between 2006 and 2021 (time period covered varies by country due to data availability constraints). To overcome the inconsistency of data publishing formats in each country, we standardized the published information to fit a common data standard. For each country, key information is collected on the buyer(s) and supplier(s), geolocation information, product classification, price information, and details of the contracting process such as contract award date or the procedure type followed. GPPD is a contract-level dataset where specific filters are calculated allowing to reduce the dataset to the successfully awarded contracts if needed. We also add several corruption risk indicators and a composite corruption risk index for each contract which allows for an objective assessment of risks and comparison across time, organizations, or countries. The data can be reused to answer research questions dealing with public procurement spending efficiency among others. Using unique organizational identification numbers or organization names allows connecting the data to company registries to study broader topics such as ownership networks…(More)”.

The Ethics of Advanced AI Assistants

Curated on Posted on by Stefaan Verhulst

Paper by Iason Gabriel et al: “This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user – across one or more domains – in line with the user’s expectations. The paper starts by considering the technology itself, providing an overview of AI assistants, their technical foundations and potential range of applications. It then explores questions around AI value alignment, well-being, safety and malicious uses. Extending the circle of inquiry further, we next consider the relationship between advanced AI assistants and individual users in more detail, exploring topics such as manipulation and persuasion, anthropomorphism, appropriate relationships, trust and privacy. With this analysis in place, we consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants. Finally, we conclude by providing a range of recommendations for researchers, developers, policymakers and public stakeholders…(More)”.

The End of the Policy Analyst? Testing the Capability of Artificial Intelligence to Generate Plausible, Persuasive, and Useful Policy Analysis

Curated on Posted on by Stefaan Verhulst

Article by Mehrdad Safaei and Justin Longo: “Policy advising in government centers on the analysis of public problems and the developing of recommendations for dealing with them. In carrying out this work, policy analysts consult a variety of sources and work to synthesize that body of evidence into useful decision support documents commonly called briefing notes. Advances in natural language processing (NLP) have led to the continuing development of tools that can undertake a similar task. Given a brief prompt, a large language model (LLM) can synthesize information in content databases. This article documents the findings from an experiment that tested whether contemporary NLP technology is capable of producing public policy relevant briefing notes that expert evaluators judge to be useful. The research involved two stages. First, briefing notes were created using three models: NLP generated; human generated; and NLP generated/human edited. Next, two panels of retired senior public servants (with only one panel informed of the use of NLP in the experiment) were asked to judge the briefing notes using a heuristic evaluation rubric. The findings indicate that contemporary NLP tools were not able to, on their own, generate useful policy briefings. However, the feedback from the expert evaluators indicates that automatically generated briefing notes might serve as a useful supplement to the work of human policy analysts. And the speed with which the capabilities of NLP tools are developing, supplemented with access to a larger corpus of previously prepared policy briefings and other policy-relevant material, suggests that the quality of automatically generated briefings may improve significantly in the coming years. The article concludes with reflections on what such improvements might mean for the future practice of policy analysis…(More)”.