Paper by Ayan Mukhopadhyay: “Emergency response management (ERM) is a challenge faced by communities across the globe. First responders must respond to various incidents, such as fires, traffic accidents, and medical emergencies. They must respond quickly to incidents to minimize the risk to human life. Consequently, considerable attention has been devoted to studying emergency incidents and response in the last several decades. In particular, data-driven models help reduce human and financial loss and improve design codes, traffic regulations, and safety measures. This tutorial paper explores four sub-problems within emergency response: incident prediction, incident detection, resource allocation, and resource dispatch. We aim to present mathematical formulations for these problems and broad frameworks for each problem. We also share open-source (synthetic) data from a large metropolitan area in the USA for future work on data-driven emergency response…(More)”.
Ethical Considerations Towards Protestware
Paper by Marc Cheong, Raula Gaikovina Kula, and Christoph Treude: “A key drawback to using a Open Source third-party library is the risk of introducing malicious attacks. In recently times, these threats have taken a new form, when maintainers turn their Open Source libraries into protestware. This is defined as software containing political messages delivered through these libraries, which can either be malicious or benign. Since developers are willing to freely open-up their software to these libraries, much trust and responsibility are placed on the maintainers to ensure that the library does what it promises to do. This paper takes a look into the possible scenarios where developers might consider turning their Open Source Software into protestware, using an ethico-philosophical lens. Using different frameworks commonly used in AI ethics, we explore the different dilemmas that may result in protestware. Additionally, we illustrate how an open-source maintainer’s decision to protest is influenced by different stakeholders (viz., their membership in the OSS community, their personal views, financial motivations, social status, and moral viewpoints), making protestware a multifaceted and intricate matter…(More)”
A Snapshot of Artificial Intelligence Procurement Challenges
Press Release: “The GovLab has released a new report offering recommendations for government in procuring artificial intelligence (AI) tools. As the largest purchaser of technology, it is critical for the federal government to adapt its procurement practices to ensure that beneficial AI tools can be responsibly and rapidly acquired and that safeguards are in place to ensure that technology improves people’s lives while minimizing risks.
Based on conversations with over 35 leaders in government technology, the report identifies key challenges impeding successful procurement of AI, and offers five urgent recommendations to ensure that government is leveraging the benefits of AI to serve residents:
- Training: Invest in training public sector professionals to understand and differentiate between high- and low-risk AI opportunities. This includes teaching individuals and government entities to define problems accurately and assess algorithm outcomes. Frequent training updates are necessary to adapt to the evolving AI landscape.
- Tools: Develop decision frameworks, contract templates, auditing tools, and pricing models that empower procurement officers to confidently acquire AI. Open data and simulated datasets can aid in testing algorithms and identifying discriminatory effects.
- Regulation and Guidance: Recognize the varying complexity of AI use cases and develop a system that guides acquisition professionals to allocate time appropriately. This approach ensures more problematic cases receive thorough consideration.
- Organizational Change: Foster collaboration, knowledge sharing, and coordination among procurement officials and policymakers. Including mechanisms for public input allows for a multidisciplinary approach to address AI challenges.
- Narrow the Expertise Gap: Integrate individuals with expertise in new technologies into various government departments, including procurement, legal, and policy teams. Strengthen connections with academia and expand fellowship programs to facilitate the acquisition of relevant talent capable of auditing AI outcomes. Implement these programs at federal, state, and local government levels…(More)”
Adopting AI Responsibly: Guidelines for Procurement of AI Solutions by the Private Sector
WEF Report: “In today’s rapidly evolving technological landscape, responsible and ethical adoption of artificial intelligence (AI) is paramount for commercial enterprises. The exponential growth of the global AI market highlights the need for establishing standards and frameworks to ensure responsible AI practices and procurement. To address this crucial gap, the World Economic Forum, in collaboration with GEP, presents a comprehensive guide for commercial organizations…(More)”.
How existential risk became the biggest meme in AI
Article by Will Douglas Heaven: “Who’s afraid of the big bad bots? A lot of people, it seems. The number of high-profile names that have now made public pronouncements or signed open letters warning of the catastrophic dangers of artificial intelligence is striking.
Hundreds of scientists, business leaders, and policymakers have spoken up, from deep learning pioneers Geoffrey Hinton and Yoshua Bengio to the CEOs of top AI firms, such as Sam Altman and Demis Hassabis, to the California congressman Ted Lieu and the former president of Estonia Kersti Kaljulaid.
The starkest assertion, signed by all those figures and many more, is a 22-word statement put out two weeks ago by the Center for AI Safety (CAIS), an agenda-pushing research organization based in San Francisco. It proclaims: “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.”
The wording is deliberate. “If we were going for a Rorschach-test type of statement, we would have said ‘existential risk’ because that can mean a lot of things to a lot of different people,” says CAIS director Dan Hendrycks. But they wanted to be clear: this was not about tanking the economy. “That’s why we went with ‘risk of extinction’ even though a lot of us are concerned with various other risks as well,” says Hendrycks.
We’ve been here before: AI doom follows AI hype. But this time feels different. The Overton window has shifted. What were once extreme views are now mainstream talking points, grabbing not only headlines but the attention of world leaders. “The chorus of voices raising concerns about AI has simply gotten too loud to be ignored,” says Jenna Burrell, director of research at Data and Society, an organization that studies the social implications of technology.
What’s going on? Has AI really become (more) dangerous? And why are the people who ushered in this tech now the ones raising the alarm?
It’s true that these views split the field. Last week, Yann LeCun, chief scientist at Meta and joint recipient with Hinton and Bengio of the 2018 Turing Award, called the doomerism “preposterously ridiculous.” Aidan Gomez, CEO of the AI firm Cohere, said it was “an absurd use of our time.”
Others scoff too. “There’s no more evidence now than there was in 1950 that AI is going to pose these existential risks,” says Signal president Meredith Whittaker, who is cofounder and former director of the AI Now Institute, a research lab that studies the social and policy implications of artificial intelligence. “Ghost stories are contagious—it’s really exciting and stimulating to be afraid.”
“It is also a way to skim over everything that’s happening in the present day,” says Burrell. “It suggests that we haven’t seen real or serious harm yet.”…(More)”.
TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI
Paper by Andrew Critch and Stuart Russell: “While several recent works have identified societal-scale and extinction-level risks to humanity arising from artificial intelligence, few have attempted an {\em exhaustive taxonomy} of such risks. Many exhaustive taxonomies are possible, and some are useful — particularly if they reveal new risks or practical approaches to safety. This paper explores a taxonomy based on accountability: whose actions lead to the risk, are the actors unified, and are they deliberate? We also provide stories to illustrate how the various risk types could each play out, including risks arising from unanticipated interactions of many AI systems, as well as risks from deliberate misuse, for which combined technical and policy solutions are indicated…(More)”.
An algorithm intended to reduce poverty in Jordan disqualifies people in need
Article by Tate Ryan-Mosley: “An algorithm funded by the World Bank to determine which families should get financial assistance in Jordan likely excludes people who should qualify, according to an investigation published this morning by Human Rights Watch.
The algorithmic system, called Takaful, ranks families applying for aid from least poor to poorest using a secret calculus that assigns weights to 57 socioeconomic indicators. Applicants say that the calculus is not reflective of reality, however, and oversimplifies people’s economic situation, sometimes inaccurately or unfairly. Takaful has cost over $1 billion, and the World Bank is funding similar projects in eight other countries in the Middle East and Africa.
Human Rights Watch identified several fundamental problems with the algorithmic system that resulted in bias and inaccuracies. Applicants are asked how much water and electricity they consume, for example, as two of the indicators that feed into the ranking system. The report’s authors conclude that these are not necessarily reliable indicators of poverty. Some families interviewed believed the fact that they owned a car affected their ranking, even if the car was old and necessary for transportation to work.
The report reads, “This veneer of statistical objectivity masks a more complicated reality: the economic pressures that people endure and the ways they struggle to get by are frequently invisible to the algorithm.”..(More)”.
The A.I. Revolution Will Change Work. Nobody Agrees How.
Sarah Kessler in The New York Times: “In 2013, researchers at Oxford University published a startling number about the future of work: 47 percent of all United States jobs, they estimated, were “at risk” of automation “over some unspecified number of years, perhaps a decade or two.”
But a decade later, unemployment in the country is at record low levels. The tsunami of grim headlines back then — like “The Rich and Their Robots Are About to Make Half the World’s Jobs Disappear” — look wildly off the mark.
But the study’s authors say they didn’t actually mean to suggest doomsday was near. Instead, they were trying to describe what technology was capable of.
It was the first stab at what has become a long-running thought experiment, with think tanks, corporate research groups and economists publishing paper after paper to pinpoint how much work is “affected by” or “exposed to” technology.
In other words: If cost of the tools weren’t a factor, and the only goal was to automate as much human labor as possible, how much work could technology take over?
When the Oxford researchers, Carl Benedikt Frey and Michael A. Osborne, were conducting their study, IBM Watson, a question-answering system powered by artificial intelligence, had just shocked the world by winning “Jeopardy!” Test versions of autonomous vehicles were circling roads for the first time. Now, a new wave of studies follows the rise of tools that use generative A.I.
In March, Goldman Sachs estimated that the technology behind popular A.I. tools such as DALL-E and ChatGPT could automate the equivalent of 300 million full-time jobs. Researchers at Open AI, the maker of those tools, and the University of Pennsylvania found that 80 percent of the U.S. work force could see an effect on at least 10 percent of their tasks.
“There’s tremendous uncertainty,” said David Autor, a professor of economics at the Massachusetts Institute of Technology, who has been studying technological change and the labor market for more than 20 years. “And people want to provide those answers.”
But what exactly does it mean to say that, for instance, the equivalent of 300 million full-time jobs could be affected by A. I.?
It depends, Mr. Autor said. “Affected could mean made better, made worse, disappeared, doubled.”…(More)”.
From Ethics to Law: Why, When, and How to Regulate AI
Paper by Simon Chesterman: “The past decade has seen a proliferation of guides, frameworks, and principles put forward by states, industry, inter- and non-governmental organizations to address matters of AI ethics. These diverse efforts have led to a broad consensus on what norms might govern AI. Far less energy has gone into determining how these might be implemented — or if they are even necessary. This chapter focuses on the intersection of ethics and law, in particular discussing why regulation is necessary, when regulatory changes should be made, and how it might work in practice. Two specific areas for law reform address the weaponization and victimization of AI. Regulations aimed at general AI are particularly difficult in that they confront many ‘unknown unknowns’, but the threat of uncontrollable or uncontainable AI became more widely discussed with the spread of large language models such as ChatGPT in 2023. Additionally, however, there will be a need to prohibit some conduct in which increasingly lifelike machines are the victims — comparable, perhaps, to animal cruelty laws…(More)”
Detecting Human Rights Violations on Social Media during Russia-Ukraine War
Paper by Poli Nemkova, et al: “The present-day Russia-Ukraine military conflict has exposed the pivotal role of social media in enabling the transparent and unbridled sharing of information directly from the frontlines. In conflict zones where freedom of expression is constrained and information warfare is pervasive, social media has emerged as an indispensable lifeline. Anonymous social media platforms, as publicly available sources for disseminating war-related information, have the potential to serve as effective instruments for monitoring and documenting Human Rights Violations (HRV). Our research focuses on the analysis of data from Telegram, the leading social media platform for reading independent news in post-Soviet regions. We gathered a dataset of posts sampled from 95 public Telegram channels that cover politics and war news, which we have utilized to identify potential occurrences of HRV. Employing a mBERT-based text classifier, we have conducted an analysis to detect any mentions of HRV in the Telegram data. Our final approach yielded an F2 score of 0.71 for HRV detection, representing an improvement of 0.38 over the multilingual BERT base model. We release two datasets that contains Telegram posts: (1) large corpus with over 2.3 millions posts and (2) annotated at the sentence-level dataset to indicate HRVs. The Telegram posts are in the context of the Russia-Ukraine war. We posit that our findings hold significant implications for NGOs, governments, and researchers by providing a means to detect and document possible human rights violations…(More)” See also Data for Peace and Humanitarian Response? The Case of the Ukraine-Russia War