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Tokyo’s Silent ‘Theft’ Event Returns: Take Home Items Without Making a Sound

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Have you ever wanted to experience the thrill of shoplifting but hate the mundane side effects of being arrested and going to jail? If so, then “盗-TOH-” (meaning “steal” in Japanese), is the perfect event for you. The unique experiential event where silence is literally golden is back for a second round. 盗-TOH- returns to […]

The post Tokyo’s Silent ‘Theft’ Event Returns: Take Home Items Without Making a Sound appeared first on Tokyo Weekender.

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Boolean Clashes: Discretionary Decision Making in AI-Driven Recruiting

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As artificial intelligence (AI) systems increasingly mediate our social world, regulators rush to protect citizens from potential AI harms. Many AI regulations focus on assessing potentially biased outcomes of AI. But AI systems are always embedded into social contexts and decision-making processes that are typically distributed across a range of human and machine agents. Bias and discrimination can occur anywhere in this human-machine network. Only focusing on potentially biased outcomes of an AI system will not fix the bias and discrimination problems that are integral to the whole human-machine network. Addressing this issue means focusing AI accountability approaches on practices and processes, rather than just machines or just humans.

Let’s take the world of recruiting as a case study. Recruiting has become a frontier of AI-driven automation. AI recruiting tools support search for candidates on job platforms, candidate screening (such as video interviewing or technical interviews to test coding skills), crafting job descriptions, and integrating AI (for example, chatbots) into applicant tracking systems. Using these tools can also produce instances of discrimination. Infamous examples include Amazon’s sexist hiring AI1 and Facebook’s ageist and gendered job ads.2 Fueled by the COVID-19 pandemic and demand for remote recruiter-candidate interaction, the human resources (HR) tech market is large, and it continues to grow (projected to $39.90 billion by 2029).4

Even as particularly problematic tools are retired, issues of technology-mediated and AI-accelerated bias and discrimination persist. AI tools used in candidate assessments (such as interviews or tests) are prone to error, often disadvantage certain populations6 or are based on pseudo-scientific constructs.7 Regulators are paying heightened attention to the use of AI in recruiting and employment, with influential regulation focusing explicitly on AI in HR as a “high risk” area of AI deployment.3

But discrimination that persists in HR cannot be attributed solely to the AI. It is the result of a complex sociotechnical system that includes both AI and the many people engaged in HR processes and practices. In recruiting alone, this includes sourcing specialists, talent acquisition managers, recruiters, hiring managers, HR administrators, and others, who interact with and potentially make decisions about candidates. Various AI systems and other technologies are spread across that network of actors. What is needed to mitigate potential discrimination and harm is a closer look at the professional practice of recruiting, how recruiting professionals use and make sense of AI systems, and how this affects their discretionary decision making.

Keeping It Old School: The Persistence of Boolean Search

In low-volume recruiting (that is, recruiting from a scarce talent pool so finding candidates is hard), recruiters’ traditional professional practice revolves around Boolean search. When searching for talent in databases, they assemble the specifications of the job into what they anticipate will be a powerful Boolean string. A professionally crafted Boolean search string designed to locate a computer programmer who has experience with a particular group of programs and possesses leadership skills might appear as shown in Figure 1.

Figure 1.  Real-world Boolean string for sourcing shared by research participant.

The Boolean search method is grounded in binary logic with a simple premise that statements can only be true or false. It has transcended its mathematical origins to become a cornerstone of information retrieval writ large (as anyone who has been taught to use a library catalog knows). Boolean search allows users to express the relationship between keywords in a search, rather than just the presence of the keywords (see Figure 2). Key for this are the three operators AND, OR, and NOT. Using the AND operator narrows the search by including only the results that contain all the specified keywords. The OR operator broadens the search to include results that contain either of the chosen keywords. The NOT operator excludes results that contain the keyword following it.

Figure 2.  Basic Boolean logic (source: Jakub T. Jankiewicz, Wikimedia Commons).

Boolean logic operates within recruiters’ minds as they carefully select the most fitting keywords for the role they are trying to fill. Working to match job specifications with ideal candidates, they turn to Boolean search across vast candidate databases (such as LinkedIn) into an epistemology—a way of knowing and understanding the world of potential hires.8

Constructing a Boolean search string for finding fitting job candidates is not merely a technical exercise. It is a labor-intensive and iterative process that demands creativity, analytical rigor, and often years of experience. Typically, recruiters invested considerable time and effort in iteratively refining their Boolean search strings. Each keyword selection, operator placement, and logical structure acts as a deliberate choice, designed to surface the “right” candidate profiles. This process transcends mere keyword lists; it demands an iterative dance between logic and intuition, honed through experience and a deep knowledge of the target talent pool. For a recruiter, finding the “perfect” Boolean string is like finding a vein of gold. It can vastly improve efficiency and efficacy of candidate search for a specific role, or type of role. Boolean search allows recruiters to iteratively adapt their queries in real-time based on the feedback provided through the search engine results. This is where recruiters can exercise the discretionary decision-making power that is the essence for their own job: making the decision on who is the “right” candidate.

In traditional, non-AI-driven information retrieval by way of Boolean search, recruiters can easily discern the relationship between the keywords expressed in the Boolean string and the search results. This gives them discretionary room to maneuver. They can rely on the search engine faithfully delivering to their query, and they can predict the effects of tweaks in their Boolean expression. In other words, traditional Boolean search provides the kind of transparency recruiters require for the discretionary decisions that are specific to their profession.

AI-Driven Search and Boolean Epistemology

In AI-driven candidate search, however, the system is not faithfully delivering on the keyword relationship expressed in the Boolean string. AI-systems, including generative AI systems that respond to prompts, are calibrated to produce statically probable outputs based on a search or prompt (as well as various unknown factors, such as previous search behavior), rather than the precise keyword relationship. Here, the system interprets the keywords in ways that are not discernable (and therefore actionable) by recruiters. For example, a recruiter may include the term “New York City” in the string because they need a candidate who is based in New York City for tax reasons. The AI may interpret this in undesirable ways and, for example, suggest candidates as “most relevant” (and ranked at the top of the search results) who are based in Hoboken, NJ, USA. A new search with the exact same Boolean expression run a few hours later may show candidates based in the Hudson Valley, NY, USA.

It remains unclear to the recruiter, how and why the AI system powering the search made this leap. The Boolean epistemology that recruiters traditionally deploy affects how they make sense of AI and influences if and where mistrust and potential bias manifest in HR. The “interpretive lift” undertaken by the AI system is palpable but never consistent or squarable with the professional epistemology recruiters use, and it curbs the discretionary decision space available to them. They cannot tweak the keywords to better understand the effects of each one on the search result. In AI-driven search, these causal effects cannot be known by recruiters. In other words: Boolean epistemology and AI epistemology clash.

Navigating the Chasm

The clash between these two epistemologies leads recruiters to mistrust the AI systems that their own employers often require them to use. Recruiters are acutely aware of the “epistemological clash.” They know that, through the machine learning feedback loop (in which data generated through the interaction with an AI system re-enters the system and affects its predictions), their interactions with AI-driven search engines are recorded and affect subsequent search results. To preserve their discretionary decision-space (which is central to their professional identity), recruiters sometimes try to “neutralize” the AI-driven search system or “confuse” it. They may input a vast range of different Boolean search strings, save all results in a separate spreadsheet, and manually comb through them. They may also manually infer features they deem important rather than relying on the machine to do it. For example, they may infer gender or racial identity from location or educational background to try to ensure a diverse candidate pool and avoid bias and discrimination.

Viewed as a larger socio-technical work system, recruiters’ interactions with AI-driven search tools reclaim discretionary capacity and allow them, not machines, to make decisions about candidates. This involves substantial work as Boolean searches must be meticulously composed and continuously tweaked, which reduces the alleged time-saving value of AI systems. It also demonstrates how AI-driven recruiting systems may be used in ways that sustain, rather than curb, issues of (human) bias and discrimination.

Thus, it is insufficient to address AI discrimination by looking at the potentially biased outcomes of an AI system. A more nuanced approach is needed as the field of AI ethics and accountability and transparency progresses, and as AI regulation becomes more common. This becomes particularly important as generative AI systems enter the HR space making the AI’s interpretation of search commands or prompts even less transparent and adding the risk of “hallucinations.”

Understanding how professional discretion is affected by new forms of AI-driven automation, within and beyond HR, is extremely important. We must treat the black box of AI as a socio-technical phenomenon in which professional epistemologies and practices clash with hidden AI functionalities. Concretely, this means integrating work practices and decision-making processes into AI accountability efforts. Only by taking this larger systems view can we avoid the “many hands” problem that makes it so hard to identify who is responsible for the harms that computer systems can cause.5 Centering what people are doing and how—including with machines—rather than treating machines as the sole focus of regulatory attention, can help address the continuation of human-machine bias.

Conclusion

AI functionalities clash with the Boolean epistemology of candidate search in professional recruiting. This encourages human intervention and enables continued employment bias and discrimination. Employment fairness is of enormous ethical importance, but HR recruitment is just one of many areas of life where AI has been implicated in bias and discrimination. Focusing solely on AI opacity as the cause of bias and discrimination misses the fundamental socio-technical nature of the phenomenon and points to ineffectual solutions.

We are in urgent need of more empirically grounded research on how AI is actually used so that we understand and address where and how bias and discrimination can occur in the distributed human-machine decision-system networks that influence important life outcomes. This is increasingly urgent with the rise of generative AI technologies such as ChatGPT and their rapid adoption. Focusing accountability approaches on practices, processes, and technologies rather than just machines or just humans, is a crucial first step toward building a just society.

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mrmarchant
5 hours ago
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Unfortunately, This Is What the Soft Animal of Your Body Loves

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Unfortunately, These Are the Things the Soft Animal of Your Body Loves

Editor’s Note: This work is an irreverent riff on Mary Oliver’s iconic poem “Wild Geese.”

Putting bugles on the ends of your fingers to make cunning little claws because you do not have to be good.

Being crushed under eleven weighted blankets like a cozycore Giles Corey. Meanwhile the world goes on.

Eating a deconstructed PB&J by putting a family of things (a peanut, a grape, a crouton) into your mouth all at once.

Honking and strutting around like a regal, wild goose, over and over announcing your place, while you wait for your Dunkin’ Donuts order to be ready.

Offering yourself to the world’s imagination by growing out very long bangs down to your chin and yelling “LET THE SHOW BEGIN” whenever you part them to reveal your face.

Accidentally “composting” in the crisper drawer until you create a terrarium, a landscape of deep trees and sprouting radishes.

Collecting a nest of small trinkets and clear pebbles of the rain because you have been oh so very good.

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Walking on your knees, then crawling upon your belly and tonguing crumbs out of the carpet.

Curling up inside the harsh, exciting armpit of your mate and taking a small nap.

The post Unfortunately, This Is What the Soft Animal of Your Body Loves appeared first on Electric Literature.

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mrmarchant
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How Pebbles Form Planets

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The secret to the formation of planets may lie in ordinary static electricity—the same phenomenon that can make your hair stand on end or give you an electric shock after walking across a carpet.

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A new study, published in Nature Astronomy, suggests that static electricity allows tiny dust particles in protoplanetary disks—the rotating platters of gas and dust that form around young stars—to clump together into “pebbles” that are large enough to play a role in the formation of planets.

The image above shows basaltic beads, each measuring 0.55 millimeters, that were used in an experiment, which took place aboard a suborbital rocket.

The findings help resolve a mystery that has shrouded something called the bouncing barrier—the size threshold that particles must reach in order to rely on gravity to join with other particles—says lead author of the study Jens Teiser, an astrophysicist at the University of Duisburg-Essen in Germany.

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The dust particles need static electricity to make them “sticky” enough to cluster into pebbles that can form planets.

Only when particles grow larger than this threshold size—roughly a quarter of an inch, depending on conditions—can they eventually join to form rocky “planetesimals,” from about half a mile to 100 miles across, that scientists think then collide within protoplanetary disks to create planets like Earth.

Smaller “dust particles don’t stick together,” Teiser says, unless they have an electrostatic charge.

Static electricity is produced when different objects with an imbalance of positive and negative charges make contact, which results in an electrostatic charge. In this case, the electrostatic charge is generated by collisions between tiny dust particles, which can cause them to either gain electrons or lose electrons, resulting in a negative or a positive charge, respectively. Oppositely charged particles will then attract each other—according to the law of electrostatics—and can clump together to create even larger charged particles, Teiser says.

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Teiser and his colleagues suspected this was the case after conducting “tower drop” experiments with tiny basalt particles, in which they observed the behavior of these particles during nine seconds of near-weightlessness. But that wasn’t enough time to reach a conclusion, so in 2022 the researchers performed an experiment onboard a suborbital rocket that launched from Kiruna in northern Sweden, to observe how the particles behaved during six minutes of weightlessness.

During the 2022 launch, described in the latest study, the rocket reached an altitude of about 160 miles, and weightlessness kicked in as the rocket’s payload fell back to Earth. At that point, a particle reservoir aboard the vessel opened, releasing the particles. In some cases, the reservoir was shaken to give the particles electrostatic charge, but in other cases, it was not. Only those particles that had been shaken began to assemble into an aggregate. The largest cluster, shown in the image, was a little more than an inch in length. Teiser says his team of researchers sent four versions of their experiment aloft in the rocket, each with different starting conditions.

The researchers believe their findings suggest that the dust particles in protoplanetary disks need static electricity to make them “sticky” enough to cluster into pebbles that can form planets. They were also able to calculate the maximum average speeds the tiny particles can travel when they collide if they are to create clumps: about a foot and a half per second. Collisions at greater speeds tended to erode the surfaces of large clusters.

The results will be used in models that try to explain how massive planets like our own arise from mere dust.

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Lead image: University of Duisburg-Essen (UDE)

The post How Pebbles Form Planets appeared first on Nautilus.

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Science of the loud sneeze, illustrated

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Some people sneeze very loudly. For the Washington Post, Teddy Amenabar, Álvaro Valiño, and Artur Galocha used animated illustrations to show what brings that on, along with tips on how to sneeze quietly.

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The extraordinary reason why scientists are collecting sea turtle tears

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A green sea turtle. | Ron Masessa/Getty Images

Each year, in late spring and early summer, female sea turtles will crawl out of the ocean under moonlight to lay their eggs in the sand, often returning to the same beach on which they were born many years earlier.

Sometimes when the turtles emerge to nest, researchers like Julianna Martin are watching patiently from the shadows.

For her doctoral research, Martin, a PhD student at the University of Central Florida, has been analyzing sea turtle tears. Yes, the tears of sea turtles. So on several summer nights in 2023 and 2024, she’d stake out beaches and wait for the turtles to start laying eggs. At that point, the reptiles enter a sort of “trance,” she said, allowing scientists like her to collect samples, including tears. 

Martin told me she would army crawl up to the turtles on the sand and dab around their eyes with a foam swab, soaking up the goopy tears they exude. Sea turtles regularly shed tears as a way to expel excess salt from their bodies. (As far as we know, they are not sad.)

Martin would then take those tears back to her lab for analysis. 

This odd work serves a purpose. Martin is examining sea turtle tears to see if they contain a specific kind of bacteria. Such a discovery, she said, could help unlock one of biology’s biggest and most awe-inspiring mysteries: how animals navigate using Earth’s invisible magnetic field.

The “holy grail” of sensory biology

After baby turtles hatch, they dig their way out of the sand and crawl into the ocean, where they embark on an epic journey that can take them thousands of miles across the open sea. Loggerheads that hatch in Florida, for example, swim across the Atlantic and reach islands off the coast of Portugal, before eventually returning to Florida’s beaches as adults to nest. 

Remarkably, the turtles typically return to the same region of Florida or even to the same beach. 

“These young turtles can guide themselves along that 10,000-mile migratory path despite never having been in the ocean before and despite traveling on their own,” said Kenneth Lohmann, a biologist at University of North Carolina at Chapel Hill who studies sea turtle navigation.

Researchers like Lohmann have learned that sea turtles, like many other species, seem to navigate using Earth’s magnetic field. That’s the subtle magnetic force — generated by the planet’s molten metal core — that surrounds Earth, not unlike the force around a bar magnet. The intensity and direction of the field vary across Earth’s surface, making it useful for navigation. Plus, the magnetic field is present even when other spatial cues, like light, are not. 

What remains a mystery, however, is how animals sense these magnetic forces. Decades of research have failed to turn up a mechanism for so-called magnetoreception or any kind of specialized organ that can sense magnetic force. As Martin’s adviser Robert Fitak has written, it’s like knowing an animal can respond to something visual but not finding any eyes. 

“It’s the last sense we effectively know nothing about,” sensory biologist Eric Warrant has said about magnetoreception. “The solution of this problem I would say is the greatest holy grail in sensory biology.”

Scientists have proposed a number of theories for how this might work. And all of them are totally bonkers. 

The prevailing theory is rooted in quantum mechanics, and it is extremely complicated. The theory posits that when certain light-sensitive molecules known as cryptochromes absorb light, they produce something called radical pairs — two separate molecules each with one unpaired electron. Those two unpaired electrons are quantumly entangled, which essentially means that their spin states are interdependent: They either point in the same direction or opposite directions, and they ping-pong between the two. 

This theory suggests that Earth’s magnetic field influences the spin states of those radical pairs, and that, in turn, affects the outcome of chemical reactions in the body of animals. Those chemical reactions — which animals can theoretically interpret, as they might, for example, smells or visuals — encode information about Earth’s magnetic field. (If you want to dive deeper, I suggest watching this lecture or reading this paper.) 

Another theory suggests that animals have bits of magnetic material in their bodies, such as the mineral magnetite. According to this theory, those magnetic bits are influenced by Earth’s magnetic field — just like a compass — and animals can sense those influences to figure out where they’re going. 

Martin and Fitak’s research is exploring this latter theory, but with an important twist. They suspect that sea turtles and other animals might rely on magnetite to sense Earth’s magnetic field but may not produce the magnetite themselves. Instead, they suggest, sea turtles may have a symbiotic relationship with magnetite-producing bacteria — literally living compasses — that sense the magnetic field and somehow communicate information back to the turtle.  

This isn’t an outrageous idea. Magnetic bacteria — more technically, magnetotactic bacteria — is real, and quite common in aquatic environments around the world. Plus, there’s evidence that magnetotactic bacteria help another microscopic organism, known as a protist, navigate. The question is, could they help turtles navigate, too?

Magnetic bacteria is a thing 

Magnetotactic bacteria are extremely cool. These microscopic organisms have what are essentially built-in compass needles, said Caroline Monteil, a microbial ecologist at the French research institute CEA. The needles comprise chains of magnetic particles produced by the microbes, which you can see under a microscope (shown in images below). Remarkably, those needles align the bacteria with Earth’s magnetic field lines, just like a real compass needle does. As the bacteria roam about, they move in line with the direction of the planet’s magnetic force. 

Magnetic sensing is useful for the bacteria, said Fitak, an assistant professor at UCF. Magnetotactic bacteria need specific levels of oxygen to survive, and those levels tend to vary with depth. Deeper levels of sediment in a stream, for example, might have less oxygen. In most of the world, the direction of the magnetic field is at least somewhat perpendicular to Earth’s surface — meaning, up and down — allowing the bacteria to move vertically through their environment to find the optimal habitat, as if they’re on a fixed track. 

In at least one case, magnetic bacteria team up with other organisms to help them find their way. A remarkable study published in 2019 found that microscopic organisms in the Mediterranean Sea called protists were able to sense magnetic forces because their bodies were covered in magnetic bacteria. When the authors put the north pole of a bar magnet next to a water droplet full of protists, they swam toward it. When they flipped the magnet, the protists swam away. (Different magnetic microbes are attracted to either north or south poles, often depending on where on Earth they live.)

You can actually see this in the video below. 

It’s not clear how the magnetic bacteria are actually guiding the protist, said Monteil, the study’s lead author. 

Now, returning to the turtles: The theory that Fitak and Martin are exploring is that sea turtles, like protists, might also have magnetotactic bacteria — those living compasses — in their bodies, and somehow be able to read them. Some microbes in the microbiome aid in digestion. Others provide directions. Maybe. 

One idea, Martin says, is that the bacteria could aggregate near nerves in the turtles that provide information about their position in space. Some of those nerves are near the tear ducts, she said — which is ultimately why she was army crawling on the beach to collect turtle tears. The goal, she said, is to figure out if those tears contain magnetotactic bacteria. That would be one indication that these animals might be using bacteria for navigation. 

“We’re not entirely sure how magnetotactic bacteria could be facilitating a magnetic sense, but that seemed like a good place to start,” Martin said. 

While her research is still underway, Martin has yet to find evidence of magnetotactic bacteria in the tears of the 30 or so turtles she’s analyzed so far. That’s disappointing, she said, but it doesn’t rule out the possibility that these bacteria exist somewhere in the body of a turtle and help them navigate. 

“There are so many other ideas about ways that magnetotactic bacteria could provide information to an organism about Earth’s magnetic field,” she said. “There’s a variety of other locations and other taxa that might be better for studying this theory.” 

Other scientists who study animal navigation are skeptical. 

It’s unlikely that symbiosis with magnetotactic bacteria is what enables sea turtle navigation, said Monteil. Part of the problem is that there’s no known mechanism through which the bacteria would communicate with the turtle. It’s also not clear what magnetotactic bacteria would get out of this relationship, if it is indeed symbiotic — could sea turtles provide the conditions bacteria need to survive? Maybe. Maybe not.

What’s more, Monteil said, is that magnetotactic bacteria are widespread in the environment, so even if Martin did find them in sea turtle tears, it would do little to prove the theory. Just because magnetic bacteria are present doesn’t mean they’re helping the animal navigate.

But then again, other theories are still entirely unproven, too — and some of them are a lot weirder.

“I don’t think it is impossible,” Monteil said of sea turtles and other organisms using magnetic bacteria to navigate. “Nothing is impossible. Life is amazing and has found ways to do things that we couldn’t imagine centuries before.”

“We don’t know until we know.”

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