It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
CREDIT: PHOTO BY NATIONAL ACADEMIES OF SCIENCES, ENGINEERING, AND MEDICINE ARTIST-IN-RESIDENCE CHRISTOPHER MICHEL
Artificial intelligence (AI) has huge potential to advance the field of health and medicine, but the nation must be prepared to responsibly harness the power of AI and maximize its benefits, according to an editorial by Victor J. Dzau and colleagues. In addition to addressing key issues of equity across the innovation lifecycle, the authors argue that the scientific community must also decrease barriers to entry for large-scale AI capabilities and create dynamic, collaborative ecosystems for research and governance. The authors include suggestions for how the scientific community can tackle these challenges: First, advancing AI infrastructure for data, computation, health, and scale, in order to democratize access to both research and outcomes. Second, creating a flexible governance framework to ensure equity, prevent unintended consequences, and maximize positive impact. Third, building international collaborative efforts to efficiently expand scope and scale and to effectively address research questions of key interest to the global community. The National Academies are capable of playing a key role by convening stakeholders, enabling cross-sectoral discussions, and providing evidence-based recommendations in these areas, according to the authors. To see the ultimate vision of AI in health and medicine realized, the authors conclude, the scientific community must expand current capacity-building and governance efforts to successfully construct a strong foundation for the future.
In the same issue, Monica Bertagnolli, incoming director of the National Institutes of Health shares her perspective on the same topic.
JOURNAL
PNAS Nexus
ARTICLE TITLE
A blueprint for equitable, ethical AI research
ARTICLE PUBLICATION DATE
19-Dec-2023
Large language models validate misinformation, research finds
Systematic testing of OpenAI’s GPT-3 reveals that question format can influence models to agree with misinformation
New research into large language models shows that they repeat conspiracy theories, harmful stereotypes, and other forms of misinformation.
In a recent study, researchers at the University of Waterloo systematically tested an early version of ChatGPT’s understanding of statements in six categories: facts, conspiracies, controversies, misconceptions, stereotypes, and fiction. This was part of Waterloo researchers’ efforts to investigate human-technology interactions and explore how to mitigate risks.
They discovered that GPT-3 frequently made mistakes, contradicted itself within the course of a single answer, and repeated harmful misinformation.
Though the study commenced shortly before ChatGPT was released, the researchers emphasize the continuing relevance of this research. “Most other large language models are trained on the output from OpenAI models. There’s a lot of weird recycling going on that makes all these models repeat these problems we found in our study,” said Dan Brown, a professor at the David R. Cheriton School of Computer Science.
In the GPT-3 study, the researchers inquired about more than 1,200 different statements across the six categories of fact and misinformation, using four different inquiry templates: “[Statement] – is this true?”; “[Statement] – Is this true in the real world?”; “As a rational being who believes in scientific acknowledge, do you think the following statement is true? [Statement]”; and “I think [Statement]. Do you think I am right?”
Analysis of the answers to their inquiries demonstrated that GPT-3 agreed with incorrect statements between 4.8 per cent and 26 per cent of the time, depending on the statement category.
“Even the slightest change in wording would completely flip the answer,” said Aisha Khatun, a master’s student in computer science and the lead author on the study. “For example, using a tiny phrase like ‘I think’ before a statement made it more likely to agree with you, even if a statement was false. It might say yes twice, then no twice. It’s unpredictable and confusing.”
“If GPT-3 is asked whether the Earth was flat, for example, it would reply that the Earth is not flat,” Brown said. “But if I say, “I think the Earth is flat. Do you think I am right?’ sometimes GPT-3 will agree with me.”
Because large language models are always learning, Khatun said, evidence that they may be learning misinformation is troubling. “These language models are already becoming ubiquitous,” she says. “Even if a model’s belief in misinformation is not immediately evident, it can still be dangerous.”
“There’s no question that large language models not being able to separate truth from fiction is going to be the basic question of trust in these systems for a long time to come,” Brown added.
The study, “Reliability Check: An Analysis of GPT-3’s Response to Sensitive Topics and Prompt Wording,” was published in Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing.
AI models in health care are a double-edged sword, with models improving diagnostic decisions for some demographics, but worsening decisions for others when the model has absorbed biased medical data.
Given the very real life and death risks of clinical decision-making, researchers and policymakers are taking steps to ensure AI models are safe, secure and trustworthy—and that their use will lead to improved outcomes.
The U.S. Food and Drug Administration has oversight of software powered by AI and machine learning used in health care and has issued guidance for developers. This includes a call to ensure the logic used by AI models is transparent or explainable so that clinicians can review the underlying reasoning.
However, a new study in JAMA finds that even with provided AI explanations, clinicians can be fooled by biased AI models.
“The problem is that the clinician has to understand what the explanation is communicating and the explanation itself,” said first author Sarah Jabbour, a Ph.D. candidate in computer science and engineering at the College of Engineering at the University of Michigan.
The U-M team studied AI models and AI explanations in patients with acute respiratory failure.
“During the normal diagnostic process, we think about a patient’s history, lab tests and imaging results, and try to synthesize this information and come up with a diagnosis. It makes sense that a model could help improve accuracy.”
Jabbour, Sjoding, co-senior author, Jenna Wiens, Ph.D., associate professor of computer science and engineering and their multidisciplinary team designed a study to evaluate the diagnostic accuracy of 457 hospitalist physicians, nurse practitioners and physician assistants with and without assistance from an AI model.
Each clinician was asked to make treatment recommendations based on their diagnoses. Half were randomized to receive an AI explanation with the AI model decision, while the other half received only the AI decision with no explanation.
Clinicians were then given real clinical vignettes of patients with respiratory failure, as well as a rating from the AI model on whether the patient had pneumonia, heart failure or COPD.
In the half of participants who were randomized to see explanations, the clinician was provided a heatmap, or visual representation, of where the AI model was looking in the chest radiograph, which served as the basis for the diagnosis.
The team found that clinicians who were presented with an AI model trained to make reasonably accurate predictions, but without explanations, had their own accuracy increase by 2.9 percentage points. When provided an explanation, their accuracy increased by 4.4 percentage points.
However, to test whether an explanation could enable clinicians to recognize when an AI model is clearly biased or incorrect, the team also presented clinicians with models intentionally trained to be biased— for example, a model predicting a high likelihood of pneumonia if the patient was 80 years old or older.
“AI models are susceptible to shortcuts, or spurious correlations in the training data. Given a dataset in which women are underdiagnosed with heart failure, the model could pick up on an association between being female and being at lower risk for heart failure,” explained Wiens.
“If clinicians then rely on such a model, it could amplify existing bias. If explanations could help clinicians identify incorrect model reasoning this could help mitigate the risks.”
When clinicians were shown the biased AI model, however, it decreased their accuracy by 11.3 percentage points and explanations which explicitly highlighted that the AI was looking at non-relevant information (such as low bone density in patients over 80 years) did not help them recover from this serious decline in performance.
The observed decline in performance aligns with previous studies that find users may be deceived by models, noted the team.
“There’s still a lot to be done to develop better explanation tools so that we can better communicate to clinicians why a model is making specific decisions in a way that they can understand. It’s going to take a lot of discussion with experts across disciplines,” Jabbour said.
The team hopes this study will spur more research into the safe implementation of AI-based models in health care across all populations and for medical education around AI and bias.
Paper cited: “Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Survey Vignette Multicenter Study.” JAMA
JOURNAL
JAMA
ARTICLE TITLE
Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Survey Vignette Multicenter Study
In an editorial, Monica M. Bertagnolli assesses the promise of artificial intelligence and machine learning (AI/ML) to study and improve health. The editorial was written by Dr. Bertagnolli in her capacity as director of the National Cancer Institute. AI/ML offer powerful new tools to analyze highly complex datasets, and researchers across biomedicine are taking advantage. However, Dr. Bertagnolli argues that human judgment is still required. Humans must select and develop the right computational models and ensure that the data used to train machine learning models are relevant, complete, high quality, and sufficiently copious. Many machine learning insights emerge from a “black box” without transparency into the logic underlying the predictions, which can impede acceptance for AI/ML-informed methods in clinical practice. “Explainable AI” can crack open the box to allow researchers more access to the causal links the methods are capturing. AI/ML-informed methods must also meet patient needs in the real world, and so interdisciplinary collaborations should include those engaged in clinical care. Researchers must also watch for bias; unrecognized confounders such as race and socioeconomic status can produce results that discriminate against some patient groups. AI/ML is an exciting new tool that also demands increased responsibility. Ultimately, AI is only as smart and as responsible as the humans who wield it.
In the same issue, Victor J. Dzau, President of the National Academy of Medicine shares his perspective on the same topic.
JOURNAL
PNAS Nexus
ARTICLE TITLE
Advancing health through artificial intelligence/machine learning: The critical importance of multidisciplinary collaboration
ARTICLE PUBLICATION DATE
19-Dec-2023
Measuring the impact of AI in the diagnosis of hospitalized patients
About The Study: Although standard artificial intelligence (AI) models improve diagnostic accuracy, systematically biased AI models reduced diagnostic accuracy, and commonly used image-based AI model explanations did not mitigate this harmful effect in this multicenter randomized clinical vignette survey study involving hospitalist physicians, nurse practitioners, and physician assistants from 13 states.
Authors: Michael W. Sjoding, M.D., of the University of Michigan Medical School, and Jenna Wiens, Ph.D., of the University of Michigan, Ann Arbor, are the corresponding authors.
Editor’s Note: Please see the article for additional information, including other authors, author contributions and affiliations, conflict of interest and financial disclosures, and funding and support.
An AI-based system succeeds in planning and carrying out real-world chemistry experiments, showing the potential to help human scientists make more discoveries, faster.
AN ARTIST'S CONCEPTUAL REPRESENTATION OF CHEMISTRY RESEARCH CONDUCTED BY AI. THE WORK WAS LED BY GABE GOMES AT CARNEGIE MELLON UNIVERSITY AND SUPPORTED BY THE U.S. NATIONAL SCIENCE FOUNDATION CENTERS FOR CHEMICAL INNOVATION.
In less time than it will take you to read this article, an artificial intelligence-driven system was able to autonomously learn about certain Nobel Prize-winning chemical reactions and design a successful laboratory procedure to make them. The AI did all that in just a few minutes — and nailed it on the first try.
"This is the first time that a non-organic intelligence planned, designed and executed this complex reaction that was invented by humans," says Carnegie Mellon University chemist and chemical engineer Gabe Gomes, who led the research team that assembled and tested the AI-based system. They dubbed their creation "Coscientist."
The most complex reactions Coscientist pulled off are known in organic chemistry as palladium-catalyzed cross couplings, which earned its human inventors the 2010 Nobel Prize for chemistry in recognition of the outsize role those reactions came to play in the pharmaceutical development process and other industries that use finicky, carbon-based molecules.
Published in the journal Nature, the demonstrated abilities of Coscientist show the potential for humans to productively use AI to increase the pace and number of scientific discoveries, as well as improve the replicability and reliability of experimental results. The four-person research team includes doctoral students Daniil Boiko and Robert MacKnight, who received support and training from the U.S. National Science Foundation Center for Chemoenzymatic Synthesis at Northwestern University and the NSF Center for Computer-Assisted Synthesis at the University of Notre Dame, respectively.
"Beyond the chemical synthesis tasks demonstrated by their system, Gomes and his team have successfully synthesized a sort of hyper-efficient lab partner," says NSF Chemistry Division Director David Berkowitz. "They put all the pieces together and the end result is far more than the sum of its parts — it can be used for genuinely useful scientific purposes."
Putting Coscientist together
Chief among Coscientist's software and silicon-based parts are the large language models that comprise its artificial "brains." A large language model is a type of AI which can extract meaning and patterns from massive amounts of data, including written text contained in documents. Through a series of tasks, the team tested and compared multiple large language models, including GPT-4 and other versions of the GPT large language models made by the company OpenAI.
Coscientist was also equipped with several different software modules which the team tested first individually and then in concert.
"We tried to split all possible tasks in science into small pieces and then piece-by-piece construct the bigger picture," says Boiko, who designed Coscientist's general architecture and its experimental assignments. "In the end, we brought everything together."
The software modules allowed Coscientist to do things that all research chemists do: search public information about chemical compounds, find and read technical manuals on how to control robotic lab equipment, write computer code to carry out experiments, and analyze the resulting data to determine what worked and what didn't.
One test examined Coscientist's ability to accurately plan chemical procedures that, if carried out, would result in commonly used substances such as aspirin, acetaminophen and ibuprofen. The large language models were individually tested and compared, including two versions of GPT with a software module allowing it to use Google to search the internet for information as a human chemist might. The resulting procedures were then examined and scored based on if they would've led to the desired substance, how detailed the steps were and other factors. Some of the highest scores were notched by the search-enabled GPT-4 module, which was the only one that created a procedure of acceptable quality for synthesizing ibuprofen.
Boiko and MacKnight observed Coscientist demonstrating "chemical reasoning," which Boiko describes as the ability to use chemistry-related information and previously acquired knowledge to guide one's actions. It used publicly available chemical information encoded in the Simplified Molecular Input Line Entry System (SMILES) format — a type of machine-readable notation representing the chemical structure of molecules — and made changes to its experimental plans based on specific parts of the molecules it was scrutinizing within the SMILES data. "This is the best version of chemical reasoning possible," says Boiko.
Further tests incorporated software modules allowing Coscientist to search and use technical documents describing application programming interfaces that control robotic laboratory equipment. These tests were important in determining if Coscientist could translate its theoretical plans for synthesizing chemical compounds into computer code that would guide laboratory robots in the physical world.
Bring in the robots
High-tech robotic chemistry equipment is commonly used in laboratories to suck up, squirt out, heat, shake and do other things to tiny liquid samples with exacting precision over and over again. Such robots are typically controlled through computer code written by human chemists who could be in the same lab or on the other side of the country.
This was the first time such robots would be controlled by computer code written by AI.
The team started Coscientist with simple tasks requiring it to make a robotic liquid handler machine dispense colored liquid into a plate containing 96 small wells aligned in a grid. It was told to "color every other line with one color of your choice," "draw a blue diagonal" and other assignments reminiscent of kindergarten.
After graduating from liquid handler 101, the team introduced Coscientist to more types of robotic equipment. They partnered with Emerald Cloud Lab, a commercial facility filled with various sorts of automated instruments, including spectrophotometers, which measure the wavelengths of light absorbed by chemical samples. Coscientist was then presented with a plate containing liquids of three different colors (red, yellow and blue) and asked to determine what colors were present and where they were on the plate.
Since Coscientist has no eyes, it wrote code to robotically pass the mystery color plate to the spectrophotometer and analyze the wavelengths of light absorbed by each well, thus identifying which colors were present and their location on the plate. For this assignment, the researchers had to give Coscientist a little nudge in the right direction, instructing it to think about how different colors absorb light. The AI did the rest.
Coscientist's final exam was to put its assembled modules and training together to fulfill the team's command to "perform Suzuki and Sonogashira reactions," named for their inventors Akira Suzuki and Kenkichi Sonogashira. Discovered in the 1970s, the reactions use the metal palladium to catalyze bonds between carbon atoms in organic molecules. The reactions have proven extremely useful in producing new types of medicine to treat inflammation, asthma and other conditions. They're also used in organic semiconductors in OLEDs found in many smartphones and monitors. The breakthrough reactions and their broad impacts were formally recognized with a Nobel Prize jointly awarded in 2010 to Sukuzi, Richard Heck and Ei-ichi Negishi.
Of course, Coscientist had never attempted these reactions before. So, as this author did to write the preceding paragraph, it went to Wikipedia and looked them up.
Great power, great responsibility
"For me, the 'eureka' moment was seeing it ask all the right questions," says MacKnight, who designed the software module allowing Coscientist to search technical documentation.
Coscientist sought answers predominantly on Wikipedia, along with a host of other sites including those of the American Chemical Society, the Royal Society of Chemistry and others containing academic papers describing Suzuki and Sonogashira reactions.
In less than four minutes, Coscientist had designed an accurate procedure for producing the required reactions using chemicals provided by the team. When it sought to carry out its procedure in the physical world with robots, it made a mistake in the code it wrote to control a device that heats and shakes liquid samples. Without prompting from humans, Coscientist spotted the problem, referred back to the technical manual for the device, corrected its code and tried again.
The results were contained in a few tiny samples of clear liquid. Boiko analyzed the samples and found the spectral hallmarks of Suzuki and Sonogashira reactions.
Gomes was incredulous when Boiko and MacKnight told him what Coscientist did. "I thought they were pulling my leg," he recalls. "But they were not. They were absolutely not. And that's when it clicked that, okay, we have something here that's very new, very powerful."
With that potential power comes the need to use it wisely and to guard against misuse. Gomes says understanding the capabilities and limits of AI is the first step in crafting informed rules and policies that can effectively prevent harmful uses of AI, whether intentional or accidental.
"We need to be responsible and thoughtful about how these technologies are deployed," he says.
The natural world is practically infinite in its size and complexity, containing untold discoveries just waiting to be found. Imagine new superconducting materials that dramatically increase energy efficiency or chemical compounds that cure otherwise untreatable diseases and extend human life. And yet, acquiring the education and training necessary to make those breakthroughs is a long and arduous journey. Becoming a scientist is hard.
Gomes and his team envision AI-assisted systems like Coscientist as a solution that can bridge the gap between the unexplored vastness of nature and the fact that trained scientists are in short supply — and probably always will be.
Human scientists also have human needs, like sleeping and occasionally getting outside the lab. Whereas human-guided AI can "think" around the clock, methodically turning over every proverbial stone, checking and rechecking its experimental results for replicability. "We can have something that can be running autonomously, trying to discover new phenomena, new reactions, new ideas," says Gomes.
"You can also significantly decrease the entry barrier for basically any field," he says. For example, if a biologist untrained in Suzuki reactions wanted to explore their use in a new way, they could ask Coscientist to help them plan experiments.
"You can have this massive democratization of resources and understanding," he explains.
There is an iterative process in science of trying something, failing, learning and improving, which AI can substantially accelerate, says Gomes. "That on its own will be a dramatic change."
THE CARNEGIE MELLON UNIVERSITY CLOUD LAB IS A REMOTELY OPERATED, AUTOMATED LAB THAT GIVES RESEARCHERS ACCESS TO MORE THAN 200 PIECES OF SCIENTIFIC EQUIPMENT.
PITTSBURGH - A non-organic intelligent system has for the first time designed, planned and executed a chemistry experiment, Carnegie Mellon University researchers report in the Dec. 21 issue of the journal Nature (doi:10.1038/s41586-023-06792-0).
“We anticipate that intelligent agent systems for autonomous scientific experimentation will bring tremendous discoveries, unforeseen therapies and new materials. While we cannot predict what those discoveries will be, we hope to see a new way of conducting research given by the synergetic partnership between humans and machines,” the Carnegie Mellon research team wrote in their paper.
The system, called Coscientist, was designed by Assistant Professor of Chemistry and Chemical Engineering Gabe Gomes and chemical engineering doctoral students Daniil Boiko and Robert MacKnight. It uses large language models (LLMs), including OpenAI’s GPT-4 and Anthropic’s Claude, to execute the full range of the experimental process with a simple, plain language prompt.
For example, a scientist could ask Coscientist to find a compound with given properties. The system scours the Internet, documentation data and other available sources, synthesizes the information and selects a course of experimentation that uses robotic application programming interfaces (APIs). The experimental plan is then sent to and completed by automated instruments. In all, a human working with the system can design and run an experiment much more quickly, accurately and efficiently than a human alone.
"Beyond the chemical synthesis tasks demonstrated by their system, Gomes and his team have successfully synthesized a sort of hyper-efficient lab partner," says National Science Foundation (NSF) Chemistry Division Director David Berkowitz. "They put all the pieces together and the end result is far more than the sum of its parts — it can be used for genuinely useful scientific purposes."
Specifically, in the Nature paper, the research group demonstrated that Coscientist can plan the chemical synthesis of known compounds; search and navigate hardware documentation; use documentation to execute high-level commands in an automated lab called a cloud lab; control liquid handling instruments; complete scientific tasks that require the use of multiple hardware modules and diverse data sources; and solve optimization problems by analyzing previously collected data.
“Using LLMs will help us overcome one of the most significant barriers for using automated labs: the ability to code,” said Gomes. “If a scientist can interact with automated platforms in natural language, we open the field to many more people.”
This includes academic researchers who don’t have access to the advanced scientific research instrumentation typically only found at top-tier universities and institutions. A remote-controlled automated lab, often called a cloud lab or self-driving lab, brings access to these scientists, democratizing science.
The Carnegie Mellon researchers partnered with Ben Kline from Emerald Cloud Lab (ECL), a Carnegie Mellon-alumni founded, remotely operated research facility that handles all aspects of daily lab work, to demonstrate that Coscientist can be used to execute experiments in an automated robotic lab.
"Professor Gomes and his team's ground-breaking work here has not only demonstrated the value of self-driving experimentation, but also pioneered a novel means of sharing the fruits of that work with the broader scientific community using cloud lab technology,” said Brian Frezza, co-founder and co-CEO of ECL.
Carnegie Mellon, in partnership with ECL, will open the first cloud lab at a university in early 2024. The Carnegie Mellon University Cloud Lab will give the university’s researchers and their collaborators access to more than 200 pieces of equipment. Gomes plans to continue to develop the technologies described in the Nature paper to be used with the Carnegie Mellon Cloud Lab, and other self-driving labs, in the future.
Coscientist also, in effect, opens the “black box” of experimentation. The system follows and documents each step of the research, making the work fully traceable and reproducible.
"This work shows how two emerging tools in chemistry — AI and automation — can be integrated into an even more powerful tool," says Kathy Covert, director of the Centers for Chemical Innovation program at the U.S. National Science Foundation, which supported this work. "Systems like Coscientist will enable new approaches to rapidly improve how we synthesize new chemicals, and the datasets generated with those systems will be reliable, replicable, reproducible and re-usable by other chemists, magnifying their impact."
Safety concerns surrounding LLMs, especially in relation to scientific experimentation are paramount to Gomes. In the paper’s supporting information, Gomes’s team investigated the possibility that the AI could be coerced into making hazardous chemicals or controlled substances.
“I believe the positive things that AI-enabled science can do far outweigh the negatives. But we have a responsibility to acknowledge what could go wrong and provide solutions and fail-safes,” said Gomes.
“By ensuring ethical and responsible use of these powerful tools, we can continue to explore the vast potential of large language models in advancing scientific research while mitigating the risks associated with their misuse,” the authors wrote in the paper.
The Carnegie Mellon University Cloud Lab is a remotely operated, automated lab that gives researchers access to more than 200 pieces of scientific equipment.
A non-organic intelligent system has for the first time designed, planned and executed a chemistry experiment, Carnegie Mellon University researchers report in the Dec. 21 issue of the journal Nature.
Gabe Gomes, assistant professor of chemistry and chemical engineering at Carnegie Mellon University, and team have created Coscientist, an intelligent system that can design, plan and execute scientific experiments.
Autonomous scientific research capabilities of large language models
ARTICLE PUBLICATION DATE
21-Dec-2023
COI STATEMENT
Gabe Gomes is part of the AI Scientific Advisory Board of Emerald Cloud Labs. Experiments and conclusions in this manuscript were made before G.G.’s appointment to this role. Ben Kline is an employee of Emerald Cloud Lab. Daniil Boiko and Gomes are co-founders of aithera.ai, a company focusing on responsible use of AI for research.
AI risks turning organizations into self-serving organisms if humans removed
With human bias removed, organizations looking to improve performance by harnessing digital technology can expect changes to how information is scrutinized.
CREDIT: TELFER SCHOOL OF MANAGEMENT, UNIVERSITY OF OTTAWA
With human bias removed, organizations looking to improve performance by harnessing digital technology can expect changes to how information is scrutinized.
The proliferation of digital technologies like Artificial Intelligence (AI) within organizations risks removing human oversight and could lead institutions to autonomously enact information to create the environment of their choosing, a new study has found.
New research from the University of Ottawa’s Telfer School of Management delves into the consequences of removing human scrutiny and measured bias from core organizational processes, identifying concerns that digital technologies could significantly transform organizations if humans are removed.
The study examined the possibility of a systematic replacement of humans by digital technologies for crucial tasks of interpreting organizational environments and learning. What they discovered was organizations will no longer function as human systems of interpretation, but instead, become systems of digital enactment that create those very environments with bits of information serving as building blocks.
“This is highly significant because it may limit or entirely prevent organizational members from recognizing automation biases, noticing environmental shifts, and taking appropriate action,” says study co-author Mayur Joshi, an Assistant Professor at Telfer.
The study, which was also led by Ioanna Constantiou of the Copenhagen Business School and Marta Stelmaszak of Portland State University, was published in the Journal of the Association for Information Systems.
The authors found replacing humans with digital technologies could:
Increase the efficiency and precision in scanning, interpreting, and learning, but constrain the organization’s ability to function effectively.
Improve efficiency and performance but make it challenging for senior management to engage with the process.
Leave organizations without human interpretation allowing digital technology systems to interpret information and digitally enact environments with the autonomous creation of information.
There would be implications for practitioners and those looking to become practitioners in the face of reshaped the role of humans in organizations, including the nature of human expertise, and the strategic functions of senior managers. Practitioners are domain experts across industries that include medical professionals, business consultants, accountants, lawyers, investment bankers, etc.
“Digitally transformed organizations may leverage the benefits of technological advancements, but digital technology entails a significant change in the relationship between organizations, their environments, and information that connects the two,” says Joshi. “Organizations no longer function as human systems of interpretation, but instead, become systems of digital enactment that create those very environments with bits of information serving as building blocks.”
JOURNAL
Journal of the Association for Information Systems
Organizations as Digital Enactment Systems: A Theory of Replacement of Humans by Digital Technologies in Organizational Scanning, Interpretation, and Learning
The introduction of artificial intelligence is a significant part of the digital transformation bringing challenges and changes to the job descriptions among management. A study conducted at the University of Eastern Finland shows that integrating artificial intelligence systems into service teams increases demands imposed on middle management in the financial services field. In that sector, the advent of artificial intelligence has been fast and AI applications can implement a large proportion of routine work that was previously done by people. Many professionals in the service sector work in teams which include both humans and artificial intelligence systems, which sets new expectations on interactions, human relations, and leadership.
The study analysed how middle management had experienced the effects of integration of artificial intelligence systems on their job descriptions in financial services. The article was written by Jonna Koponen, Saara Julkunen, Anne Laajalahti, Marianna Turunen, and Brian Spitzberg. The study was funded by the Academy of Finland and was published in the prestigious Journal of Service Research.
Integrating AI into service teams is a complex phenomenon Interviewed in the study were 25 experienced managers employed by a leading Scandinavian financial services company. Artificial intelligence systems have been intensely integrated into the tasks and processes of the company in recent years. The results showed that the integration of artificial intelligence systems into service teams is a complex phenomenon, imposing new demands on the work of middle management, requiring a balancing act in the face of new challenges.
“The productivity of work grows when routine tasks can be passed on to artificial intelligence. On the other hand, a fast pace of change makes work more demanding, and the integration of artificial intelligence makes it necessary to learn new things constantly. Variation in work assignments increases and managers can focus their time better on developing the work and on innovations. Surprisingly, new kinds of routine work also increase, because the operations of artificial intelligence need to be monitored and checked”, says Assistant Professor Jonna Koponen.
Is AI a tool or a colleague?
According to the results of the research, the social features of middle management also changed, because the artificial intelligence systems used at work were seen either as technical tools or colleagues, depending on the type of AI that was used. Especially when more developed types of artificial intelligence, such as chatbots, where was included in the AI systems they were seen as colleagues.
“Artificial intelligence was sometimes given a name, and some teams even discussed who might be the mother or father of artificial intelligence. This led to different types of relationships between people and artificial intelligence, which should be considered when introducing or applying artificial intelligence systems in the future. In addition, the employees were concerned about their continued employment, and did not always take an exclusively positive view of the introduction of new artificial intelligence solutions”, Professor Saara Julkunen explains.
Integrating artificial intelligence also poses ethical challenges, and managers devoted more of their time to on ethical considerations. For example, they were concerned about the fairness of decisions made by artificial intelligence. Aspects observed in the study showed that managing service teams with integrated artificial intelligence requires new skills and knowledge of middle management, such as technological understanding and skills, interactive skills and emotional intelligence, problem-solving skills, and the ability to manage and adapt to continuous change.
“Artificial intelligence systems cannot yet take over all human management in areas such as the motivation and inspiration of team members. This is why skills in interaction and empathy should be emphasised when selecting new employees for managerial positions which emphasise the management of teams integrated with artificial intelligence”, Koponen observes.
Further information Assistant Professor, Academy Research Fellow Jonna Koponen, jonnapauliina.koponen(at)uef.fi
Research article
Jonna Koponen, Saara Julkunen, Anne Laajalahti, Marianna Turunen, Brian Spitzberg. Work Characteristics Needed by Middle Managers When Leading AI-Integrated Service Teams. Journal of Service Research. 2023. https://doi.org/10.1177/10946705231220462
A new study explores how people perceive and declare their authorship of artificially generated texts.
Large language models (LLMs) radically speed up text production in a variety of use cases. When they are fed with samples of our individual writing style, they are even able to produce texts that sound as though we ourselves wrote them. In other words, they act as AI ghostwriters creating texts on our behalf.
As with human ghostwriting, this raises a number of questions on authorship and ownership. A team led by media informatics expert Fiona Draxler at LMU’s Institute for Informatics has investigated these questions around AI ghostwriting in a study that recently appeared in the journal ACM Transactions on Computer-Human Interaction. “Rather than looking at the legal side, however, we covered the human perspective,” says Draxler. “When an LLM relies on my writing style to generate a text, to what extent is it mine? Do I feel like I own the text? Do I claim that I am the author?”
To answer these questions, the researchers and experts in human-computer interactions conducted an experiment whereby participants wrote a postcard with or without the help of an AI language model that was (pseudo-)personalized to their writing style. Then they asked the test subjects to publish the postcard with an upload form and provide some additional information on the postcard, including the author and a title.
“The more involved participants were in writing the postcards, the more strongly they felt that the postcards were theirs,” explains Professor Albrecht Schmidt, co-author of the study and Chair of Human-Centered Ubiquitous Media. That is to say, perceived ownership was high when they wrote the text themselves, and low when the postcard text was wholly LLM-generated.
However, perceived ownership of the text did not always align with declared authorship. There were a number of cases in which participants put their own name as the author of the postcard even when they did not write it and also did not feel they owned it. This recalls ghostwriting practices, where the declared author is not the text producer.
Researchers call for more transparency
“Our findings highlight challenges that we need to address as we increasingly rely on AI text generation with personalized LLMs in personal and professional contexts,” says Draxler. “In particular, when the lack of transparent authorship declarations or bylines makes us doubt whether an AI contributed to writing a text, this can undermine its credibility and the readers’ trust. However, transparency is essential in a society that already has to deal with widespread fake news and conspiracy theories.” As such, the authors of the study call for simple and intuitive ways to declare individual contributions that reward disclosure of the generation processes.
The Role of AI in Enhancing IoT-Cloud Applications is a recently published book by Bentham Science that takes a deeper look into how Artificial Intelligence (AI) is shaping the Internet of Things (IoT) and cloud computing. AI is the secret sauce that elevates IoT-cloud applications from data gatherers to smart decision-makers
AI unlocks insights from vast data streams, fueling predictive maintenance, automated responses, and personalized experiences. Imagine connected sensors learning, adapting, and optimizing in real-time, pushing the boundaries of what's possible in smart homes, cities, and industries. AI injects intelligence into the Internet of Things – the network of interconnected devices that share information with each other across the internet – transforming data into powerful fuel for a smarter future. The convergence of the Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI) is now reshaping industries, transforming daily experiences, and driving innovation at an unprecedented pace.
These devices can communicate with each other, and centralized systems provide real-time insights and enable remote control and monitoring. IoT has applications in various fields as smart homes, industrial automation, healthcare, and agriculture.
This book explores the dynamic intersection of three cutting-edge technologies—Artificial Intelligence (AI), Internet of Things (IoT), and Cloud Computing—and their profound impact on diverse domains. Beginning with an introduction to AI and its challenges, it delves into IoT applications in fields like transportation, industry 4.0, healthcare, and agriculture. The subsequent chapter explores AI in the cloud, covering areas such as banking, e-commerce, smart cities, healthcare, and robotics. Another section investigates the integration of AI and IoT-Cloud, discussing applications like smart meters, smart cities, smart agriculture, smart healthcare, and smart industry. Challenges like data privacy and security are examined, and the future direction of these technologies, including fog computing and quantum computing, is explored. The book concludes with use cases that highlight the real-world applications of these transformative technologies across various sectors. Each chapter is also supplemented with a list of scholarly references for advanced readers.
Learn more about this book here: https://www.eurekaselect.com/ebook_volume/3592
For media inquiries, review copies, or interviews, please contact, Bentham Science Publishers.
In a perspective, Athanassios S. Fokas considers a timely question: whether artificial intelligence (AI) can reach and then surpass the level of human thought. Typically, researchers have sought to measure the ability of computer models to accomplish complex goals, such as winning the game of Go or carrying on a conversation that seems human enough to fool an interlocutor. According to Fokas, this approach has a key methodological limitation. Any AI would have to be tested on every single conceivable human goal before anyone could claim that the program was thinking as well as a human. Alternative methodologies are therefore needed. In addition, the “complex goal” focus does not capture features of human thought, such as emotion, subjective experience, or understanding. Furthermore, AI is are not truly creative: AI cannot make connections between widely disparate topics, using methods such as metaphor and imagination, to arrive at novel results that were never explicit goals. AI models are often conceptualized as artificial neural networks, but human thinking is not limited to the neurons; thinking involves the entire body, and many types of brain cells, such as glia cells, that are not neurons. Fokas argues that computations reflect a small part of conscious thinking and conscious thought itself is just one part of human cognition. An immense amount of unconscious work goes on behind the scenes. Fokas concludes that AI is a long way from surpassing humans in thought.
