Paper by Anthony Cintron Roman, Kevin Xu, Arfon Smith, Jehu Torres Vega, Caleb Robinson, Juan M Lavista Ferres: “GitHub is the world’s largest platform for collaborative software development, with over 100 million users. GitHub is also used extensively for open data collaboration, hosting more than 800 million open data files, totaling 142 terabytes of data. This study highlights the potential of open data on GitHub and demonstrates how it can accelerate AI research. We analyze the existing landscape of open data on GitHub and the patterns of how users share datasets. Our findings show that GitHub is one of the largest hosts of open data in the world and has experienced an accelerated growth of open data assets over the past four years. By examining the open data landscape on GitHub, we aim to empower users and organizations to leverage existing open datasets and improve their discoverability — ultimately contributing to the ongoing AI revolution to help address complex societal issues. We release the three datasets that we have collected to support this analysis as open datasets at this https URL…(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)”
Rewiring The Web: The future of personal data
Paper by Jon Nash and Charlie Smith: “In this paper, we argue that the widespread use of personal information online represents a fundamental flaw in our digital infrastructure that enables staggeringly high levels of fraud, undermines our right to privacy, and limits competition.
To realise a web fit for the twenty-first century, we need to fundamentally rethink the ways in which we interact with organisations online.
If we are to preserve the founding values of an open, interoperable web in the face of such profound change, we must update the institutions, regulatory regimes, and technologies that make up this network of networks.
Many of the problems we face stem from the vast amounts of personal information that currently flow through the internet—and fixing this fundamental flaw would have a profound effect on the quality of our lives and the workings of the web…(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)”.
Privacy-enhancing technologies (PETs)
Report by the Information Commissioner’s Office (UK): “This guidance discusses privacy-enhancing technologies (PETs) in detail. Read it if you have questions not answered in the Guide, or if you need a deeper understanding to help you apply PETs in practice.
The first part of the guidance is aimed at DPOs (data protection officers) and those with specific data protection responsibilities in larger organisations. It focuses on how PETs can help you achieve compliance with data protection law.
The second part is intended for a more technical audience, and for DPOs who want to understand more detail about the types of PETs that are currently available. It gives a brief introduction to eight types of PETs and explains their risks and benefits…(More)”.
How Does Data Access Shape Science?
Paper by Abhishek Nagaraj & Matteo Tranchero: “This study examines the impact of access to confidential administrative data on the rate, direction, and policy relevance of economics research. To study this question, we exploit the progressive geographic expansion of the U.S. Census Bureau’s Federal Statistical Research Data Centers (FSRDCs). FSRDCs boost data diffusion, help empirical researchers publish more articles in top outlets, and increase citation-weighted publications. Besides direct data usage, spillovers to non-adopters also drive this effect. Further, citations to exposed researchers in policy documents increase significantly. Our findings underscore the importance of data access for scientific progress and evidence-based policy formulation…(More)”.
Local Data Spaces: Leveraging trusted research environments for secure location-based policy research
Paper by Jacob L. Macdonald, Mark A. Green, Maurizio Gibin, Simon Leech, Alex Singleton and Paul Longely: “This work explores the use of Trusted Research Environments for the secure analysis of sensitive, record-level data on local coronavirus disease-2019 (COVID-19) inequalities and economic vulnerabilities. The Local Data Spaces (LDS) project was a targeted rapid response and cross-disciplinary collaborative initiative using the Office for National Statistics’ Secure Research Service for localized comparison and analysis of health and economic outcomes over the course of the COVID-19 pandemic. Embedded researchers worked on co-producing a range of locally focused insights and reports built on secure secondary data and made appropriately open and available to the public and all local stakeholders for wider use. With secure infrastructure and overall data governance practices in place, accredited researchers were able to access a wealth of detailed data and resources to facilitate more targeted local policy analysis. Working with data within such infrastructure as part of a larger research project involved advanced planning and coordination to be efficient. As new and novel granular data resources become securely available (e.g., record-level administrative digital health records or consumer data), a range of local policy insights can be gained across issues of public health or local economic vitality. Many of these new forms of data however often come with a large degree of sensitivity around issues of personal identifiability and how the data is used for public-facing research and require secure and responsible use. Learning to work appropriately with secure data and research environments can open up many avenues for collaboration and analysis…(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)”.
