“Establishing clear ethical guidelines and governance structures for the deployment of AI around the world is the first step to promoting trust and confidence, mitigating its risks, and ensuring that its benefits are fairly distributed,” says social scientist and co-author James William Santos of the Pontifical Catholic University of Rio Grande do Sul.

“Previous work predominantly centered around North American and European documents, which prompted us to actively seek and include perspectives from regions such as Asia, Latin America, Africa, and beyond,” says lead author Nicholas Kluge Corrêa of the Pontifical Catholic University of Rio Grande do Sul and the University of Bonn.

To determine whether a global consensus exists regarding the ethical development and use of AI, and to help guide such a consensus, the researchers conducted a systematic review of policy and ethical guidelines published between 2014 and 2022. From this, they identified 200 documents related to AI ethics and governance from 37 countries and six continents and written or translated into five different languages (English, Portuguese, French, German, and Spanish). These documents included recommendations, practical guides, policy frameworks, legal landmarks, and codes of conduct.

Then, the team conducted a meta-analysis of these documents to identify the most common ethical principles, examine their global distribution, and assess biases in terms of the type of organizations or people producing these documents.

The researchers found that the most common principles were transparency, security, justice, privacy, and accountability, which appeared in 82.5%, 78%, 75.5%, 68.5%, and 67% of the documents, respectively. The least common principles were labor rights, truthfulness, intellectual property, and children/adolescent rights, which appeared in 19.5%, 8.5%, 7%, and 6% of the documents, and the authors emphasize that these principles deserve more attention. For example, truthfulness — the idea that AI should provide truthful information — is becoming increasingly relevant with the release of generative AI technologies like ChatGPT. And since AI has the potential to displace workers and change the way we work, practical measures are to avoid mass unemployment or monopolies.

Most (96%) of the guidelines were “normative” — describing ethical values that should be considered during AI development and use — while only 2% recommended practical methods of implementing AI ethics, and only 4.5% proposed legally binding forms of AI regulation.

“It’s mostly voluntary commitments that say, ‘these are some principles that we hold important,’ but they lack practical implementations and legal requirements,” says Santos. “If you’re trying to build AI systems or if you’re using AI systems in your enterprise, you have to respect things like privacy and user rights, but how you do that is the gray area that does not appear in these guidelines.”

The researchers also identified several biases in terms of where these guidelines were produced and who produced them. The researchers noted a gender disparity in terms of authorship. Though 66% of samples had no authorship information, the authors of the remaining documents more often had male names (549 = 66% male, 281 = 34% female).

Geographically, most of the guidelines came from countries in Western Europe (31.5%), North America (34.5%), and Asia (11.5%), while less than 4.5% of the documents originated in South America, Africa, and Oceania combined. Some of these imbalances in distribution may be due to language and public access limitations, but the team says that these results suggest that many parts of the Global South are underrepresented in the global discourse on AI ethics. In some cases, this includes countries that are heavily involved in AI research and development, such as China, whose output of AI-related research increased by over 120% between 2016 and 2019.

“Our research demonstrates and reinforces our call for the Global South to wake up and a plea for the Global North to be ready to listen and welcome us,” says co-author Camila Galvão of the Pontifical Catholic University of Rio Grande do Sul. “We must not forget that we live in a plural, unequal, and diverse world. We must remember the voices that, until now, haven’t had the opportunity to claim their preferences, explain their contexts, and perhaps tell us something that we still don’t know.”

As well as incorporating more voices, the researchers say that future efforts should focus on how to practically implement principles of AI ethics. “The next step is to build a bridge between abstract principles of ethics and the practical development of AI systems and applications,” says Santos.

Researchers use the mouse as a model organism to study the structure and function of skeletal muscle, neuromuscular diseases and aging processes in muscle. The scientists are aware of their responsibility in the use of animals and have committed themselves at the University of Basel to rigorously implement the so-called 3R principles — Replacement, Reduction, Refinement — in animal-assisted research and animal husbandry.

The new method developed by Professor Markus Rüegg’s research group at the Biozentrum, University of Basel, is a further step towards reducing the number of laboratory animals. This method also opens new ways to investigate several genes simultaneously or even entire signaling pathways in muscle fibers quickly, cost-effectively and efficiently. The results of the study have now been published in Nature Communications.

The difficulty of studying genes in muscle fibers

Studying gene function in muscle is challenging. On the one hand, muscle fibers are very large and very fragile when isolated. On the other hand, in humans, they are up to half a meter long and contain thousands of nuclei. In order to change and study gene function in muscle fibers, all of the muscle fiber nuclei must be changed, which is difficult to achieve.

For some years now, scientists have been using the CRISPR/Cas9 method to study gene function. This method uses a virus to introduce the so-called Cas9 protein and a specifically designed guide RNA into the organism and thus into the nuclei. The Cas9 protein cuts the genomic DNA at the site recognized by the guide RNA. This combination of Cas9 protein and guide RNA allows altering gene function in the cell.

The CRISPR-Cas9 method can be split up

However, to ensure that the virus only alters the gene expression of muscle fibers and not those of other organs at the same time, the research team combined the CRISPR/Cas9 method with another method: First, the researchers succeeded in breeding mice with the Cas9 protein already present in their muscle fibers — but only there. They then introduced the desired guide RNA into the organism with a so-called adeno-associated virus, which infects muscle.

This combination causes the guide RNA in the muscle fibers to encounter the Cas9 protein, changing the genetic material as desired. “The method enables us to ensure that only the muscle fibers actually change their genetic material,” explains first author Marco Thürkauf.

Fewer laboratory animals and more efficient results

Since the adeno-associated virus can also transport several guide RNAs simultaneously, the team can now use the method to investigate several genes simultaneously or even entire signaling pathways. Furthermore, the method significantly reduces the number of experimental animals required.

“All animals used are suitable for studying genes and do not have to be bred over years. This makes it possible to study muscle fibers as well as neuromuscular diseases without using a large number of mice,” says Marco Thürkauf.

Other research groups have also already signaled their interest. “We already have several interested groups in our research community that would like to use our method,” says Markus Rüegg. “This is a great gain for both muscle research per se as well as for our goal of reducing animal experiments.”

Now, a new paper in Nature illustrates a Harvard quantum computing platform’s potential to solve the longstanding problem known as quantum error correction.

Leading the Harvard team is quantum optics expert Mikhail Lukin, the Joshua and Beth Friedman University Professor in physics and co-director of the Harvard Quantum Initiative. The work reported in Nature was a collaboration among Harvard, MIT, and Boston-based QuEra Computing. Also involved was the group of Markus Greiner, the George Vasmer Leverett Professor of Physics.

An effort spanning the last several years, the Harvard platform is built on an array of very cold, laser-trapped rubidium atoms. Each atom acts as a bit — or a “qubit” as it’s called in the quantum world — which can perform extremely fast calculations.

The team’s chief innovation is configuring their “neutral atom array” to be able to dynamically change its layout by moving and connecting atoms — this is called “entangling” in physics parlance — mid-computation. Operations that entangle pairs of atoms, called two-qubit logic gates, are units of computing power.

Running a complicated algorithm on a quantum computer requires many gates. However, these gate operations are notoriously error-prone, and a buildup of errors renders the algorithm useless.

In the new paper, the team reports near-flawless performance of its two-qubit entangling gates with extremely low error rates. For the first time, they demonstrated the ability to entangle atoms with error rates below 0.5 percent. In terms of operation quality, this puts their technology’s performance on par with other leading types of quantum computing platforms, like superconducting qubits and trapped-ion qubits.

However, Harvard’s approach has major advantages over these competitors due to its large system sizes, efficient qubit control, and ability to dynamically reconfigure the layout of atoms.

“We’ve established that this platform has low enough physical errors that you can actually envision large-scale, error-corrected devices based on neutral atoms,” said first author Simon Evered, a Harvard Griffin Graduate School of Arts and Sciences student in Lukin’s group. “Our error rates are low enough now that if we were to group atoms together into logical qubits — where information is stored non-locally among the constituent atoms — these quantum error-corrected logical qubits could have even lower errors than the individual atoms.”

The Harvard team’s advances are reported in the same issue of Nature as other innovations led by former Harvard graduate student Jeff Thompson, now at Princeton University, and former Harvard postdoctoral fellow Manuel Endres, now at California Institute of Technology. Taken together, these advances lay the groundwork for quantum error-corrected algorithms and large-scale quantum computing. All of this means quantum computing on neutral atom arrays is showing the full breadth of its promise.

“These contributions open the door for very special opportunities in scalable quantum computing and a truly exciting time for this entire field ahead,” Lukin said.