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Dr. Jeff Gill is a distinguished professor in the Department of Government at American University’s School of Public Affairs. He is also a Center for Neuroscience and Behavior member, the inaugural director at the School of Public Affairs’ Center for Data Science, and the editor-in-chief of the renowned journal Political Analysis.
Dr. Gill coordinates and supports empirical research across the campus by developing links with federal agencies, providing research support to faculty and graduate students, and building infrastructure to handle large and complex datasets. His research applies Bayesian modeling and data analysis (decision theory, testing, model selection, elicited priors) to questions in general social science quantitative methodology, political behavior and institutions, medical/health data analysis (especially physiology, circulation/blood, pediatric traumatic brain injury, and epidemiological measurement/data issues), using computationally intensive tools.
He holds a bachelor’s degree in math from the University of California, Los Angeles, a master’s of business administration from Georgetown University, and a PhD from American University.
He spoke to OnlineEngineeringPrograms.com in 2023 and 2026.
Dr. Gill notes that one of the most significant changes since 2023 is the extent to which AI has become embedded in everyday life. “There are two things that have changed notably over the last two years,” he explains. “One is the ubiquity of AI. Most people who are online and reasonably technically acclimated use AI all the time now, such as ChatGPT, Gemini, LLaMA. Grandmothers are now using AI for queries and questions. That wasn’t true when we last talked. It was more of a boutique thing.”
He adds that this shift was somewhat predictable, particularly as major search engines began integrating AI directly into search results, enabling users to obtain definitive answers rather than sifting through dozens of links.
While this widespread adoption has made AI a common consumer tool, Dr. Gill also highlights a troubling trend that has accelerated alongside its growth. “The second change is not so good,” he says. “There’s been a dramatic increase in nefarious actors using AI technology for phishing, scamming, cheating, and stealing.”
According to Dr. Gill, AI systems can now generate highly convincing phishing emails and distribute them at a massive scale, making fraud both faster and more efficient than ever before. “That’s become a real trend in the last year,” he notes, adding that the challenge of stopping it is significant as the technology continues to advance.
Dr. Gill emphasizes that these threats are difficult, if not impossible, to fully eliminate. “It cannot be stopped,” he says. “And so it falls on your shoulders and my shoulders to identify it.” Compounding the issue, he points to shifts at the federal level that have weakened enforcement capabilities. “The other trend is that the FBI has been eviscerated in terms of their ability to investigate and deal with cyber crimes,” he explains, underscoring how administrative changes have made it even harder to prosecute AI-driven fraud and malicious activity.
When Dr. Gill first spoke with us in 2023, the rapid pace of artificial intelligence development was already a central theme, but he says it has only intensified since then. “The other trend that’s continued unabated is the pace of AI and machine learning research,” he explains. “By academic standards, it exceeds any other field by a factor of 100 or more.” According to Dr. Gill, the sheer volume and speed of advancements have pushed the research frontier forward at a scale rarely seen in modern science.
He notes that this acceleration is being driven by both academic productivity and the growing number of researchers focused on AI-related breakthroughs. “The pace of research and the research frontier is dramatically different,” he says, describing progress as unfolding at an “unbelievable scale, unseen in human history.” As a result, innovations that once took decades to emerge are now developing in a matter of years, or even months, reshaping industries and redefining what’s possible across healthcare, technology, and beyond.
Dr. Gill explains that one of the most significant developments in recent years is the emergence of artificial intelligence as a disruptor of entry-level data science roles. “The major shift is that AI is essentially threatening entry-level data science in the job market,” he says. He points out that many foundational tasks traditionally handled by junior data scientists, such as cleaning messy data, building research histories, and conducting literature reviews, can now be performed efficiently by AI systems. As a result, the early-career landscape is becoming increasingly uncertain.
This disruption is happening at a unique moment in the evolution of data science as an academic discipline. “Data science as a degree is very new, and there isn’t a history of how it works out in the economic marketplace,” Dr. Gill notes. “In the middle of data science growing up as an academic degree, AI hits. Something like this has never happened before, so it’s unclear what the ramifications are.”
Despite this uncertainty, he believes the skills that matter most are shifting. While technical skills such as programming in Python, R, and SQL remain important, AI now handles many of those tasks with ease. “The soft skills are more important than the hard skills,” he explains, highlighting problem-solving, teamwork, communication, and the ability to conceptualize complex issues as increasingly critical.
Dr. Gill continues to believe that artificial intelligence should be regulated, pointing out that every major technological advancement in modern history has eventually required oversight.“When railroads became important, we had an agency to regulate the railroads. When airplanes became important, we had an agency to regulate air travel. When nuclear power became important, we had an agency to regulate nuclear power, and the list goes on,” he says.
Despite AI’s growing influence, however, he sees no real movement toward similar safeguards in the United States. “There is no momentum whatsoever to regulate AI in any form, and this administration will never do it,” he adds.
According to Dr. Gill, this lack of regulation is creating an environment where powerful private actors are free to push forward without constraints. “People like Elon Musk are in this domain because they know there’s going to be no regulation, and they can do what they want,” he explains.
Despite the challenges surrounding AI’s rapid growth and misuse, Dr. Gill identifies an opportunity that could redefine the future of data science. He explains that the vast majority of digitized human knowledge has never been fully analyzed. “The amount of digitized human knowledge sitting on hard drives is enormous, and less than one percent of it has ever been analyzed and modeled,” he says.
That percentage continues to shrink as new data is generated faster than researchers can analyze it. Dr. Gill believes that AI and related technologies will increasingly be able to search this immense digital library, enabling discoveries that were previously impossible due to time and resource constraints.
For data scientists, this shift represents a powerful new frontier. Rather than focusing primarily on manual data cleaning and basic modeling tasks, many of which AI is beginning to automate, the field is moving toward higher-level analysis, interpretation, and discovery. Dr. Gill points to disciplines such as astronomy, paleontology, and anthropology, in which massive datasets remain largely unexploited. “There’s a gigantic amount of astronomical data that hasn’t been studied, planets we don’t know about, black holes we haven’t fully understood, and the list goes on,” he explains.
As AI enables deeper exploration of these data reserves, data scientists will play a critical role in guiding research questions, validating results, and translating insights into real-world understanding, positioning the profession at the center of future scientific breakthroughs.
The most significant change Dr. Gill has noticed in data science since OpenAI and other machine-learning enterprises have entered the game has been how quickly it moves: “The most important feature is not just the changes—it’s the pace of the changes. And that pace is incredible,” he says. “Papers written using tools from a year ago are already antiquated. That’s incredibly fast. The pace at which technical changes occur at all levels within machine learning and AI is wildly dramatic. And it’s going to continue to be so. ChatGPT was just the tip of the iceberg,” he says.
He continues, “It’s going to lead to a bifurcated world, both academically and non-academically, where a small fraction of people understand the technology and an overwhelming fraction are just subjected to it. There is a significant need for a more comprehensive understanding of these tools and their implications among data scientists and the general public. The key to navigating this new era of machine-learning tools lies in finding the balance between harnessing its transformative capabilities while mitigating potential threats.”
As with any new technology, regulation is needed to ensure responsible use of AI-driven data science. This includes addressing issues of bias and discrimination and protecting sensitive data and privacy. The potential consequences of unregulated AI in data science are significant, making it imperative for governments and organizations to establish ethical guidelines and regulations: “Governments have essentially lost control of this sphere. So if governments have lost control and corporations don’t have the incentive to regulate, the pace of abuses will also dramatically increase,” warns Dr. Gill.
The question then becomes who will regulate AI: “It probably won’t be foreign governments unless they want to use it for their own nefarious purposes. Specifically, I am thinking about Russia and China here. It won’t be corporations because the financial incentives are much bigger than their perceived ethical challenges. You cannot expect these companies to police themselves,” explains Dr. Gill.
Ultimately, Dr. Gill believes that the government will need to step in: “We need to have a regulatory agency dedicated to AI. I know that’s wildly unpopular with some political circles, both in and out of this country. But if you think about it, we didn’t have a railroad administration till we had railroads. We didn’t have OSHA until we had dangerous factories. As technical developments have come along, government agencies here and in other parts of the world have developed to protect citizens from it,” he says.
Despite the potential negative consequences, AI has numerous positive aspects in data science. AI can significantly enhance decision-making processes and increase efficiency and productivity in various industries. It also has the potential to uncover insights and patterns that may not have been discovered by humans alone: “The ability for deep learning, in particular, to incorporate vast amounts of data that statisticians simply cannot,” says Dr. Gill. “in the next few years, you will see gigantic leaps in some fields. Astronomy will be one in particular because the images we’re getting from the new telescopes are substantially more detailed. Biomedical science will make similar gigantic leaps.”
Another area that has benefited and will continue to benefit from AI is genetics. The theoretical models in genetics are much more sophisticated than they were even 10 or 15 years ago,” says Dr. Gill. “The primary reason is that we know exactly where the bottom of the rabbit hole is in genetics. It’s four molecules, and there will never be a layer beneath that. It’s an incredibly complicated world of four molecules, but it’s limited. In physics, they have no idea where the bottom of the rabbit hole is. Is it dark matter? Is it string theory? Parallel universes? They literally have no idea…Fields like genetics, where you have a definable intellectual space, will be where future AI will be incredibly powerful.”
While the potential for positive impact is immense, there are also negative consequences to consider regarding AI in data science: “A significant negative is the invasion of privacy. The biggest problem is that humans have long since decided to trade convenience for privacy,” says Dr. Gill. The vast amounts of data collected by AI tools can be used in ways that may not align with ethical or moral principles.
There is also a concern about the potential for bias and discrimination in AI-driven data science. “The evidence is strong that many AI algorithms have built-in inadvertent or purposeful prejudices. For example, facial recognition software has a harder time distinguishing African American faces,” explains Dr. Gill. The biases are pervasive.
AI has also been shown to be biased against women in hiring algorithms; algorithms used to guide healthcare decisions have been found to favor white patients over black patients; and predictive policing systems have disproportionately targeted neighborhoods with a higher population of ethnic minorities.
Most everything people do these days generates data that corporations store. “Data is being created even when you walk down the street through your phone or watch. If your car is less than ten years old, it sends the data back to the manufacturer. They haven’t quite figured out what to do with all that data. But they’re saving it. Hard disk space storage space for governments and major corporations is almost free these days. Fifteen years ago, corporations would throw the old data away because it was expensive and annoying to keep. As algorithms get more sophisticated, a shocking amount of data can be mined from the past,” warns Dr. Gill.
So, where does this leave data scientists? While the rise of AI may bring about concerns for job security, there are ways to future-proof your data science career in the age of AI. Adaptability and continuous learning are essential: “The basics are changing very rapidly, which means the edge of the envelope is changing rapidly as well. There’s infinite demand for data science degree holders,” says Dr. Gill. “To succeed, students must have a firm grasp of the technical basics. Unless you have the basics, you have no chance of understanding real machine learning.”
In addition to mastering the technical basics, data science students can future-proof their careers by honing their soft skills. Analytical thinking, problem-solving, and effective communication are crucial skills in the modern business environment. Also, having a solid understanding of ethical principles and the ability to evaluate AI systems critically will be highly valuable in ensuring the responsible use of AI in data science.
With a keen understanding of the challenges and opportunities in higher education, Kimmy Gustafson regularly contributes insightful articles to OnlineEngineeringPrograms.com, providing readers with a comprehensive view on the evolving landscape of engineering education since 2019. She has interviewed many engineering experts on a range of subjects, including geoengineering.
Kimmy has been a freelance writer for more than a decade, writing hundreds of articles on a wide variety of topics such as startups, nonprofits, healthcare, kiteboarding, the outdoors, and higher education. She is passionate about seeing the world and has traveled to over 27 countries. She holds a bachelor’s degree in journalism from the University of Oregon. When not working, she can be found outdoors, parenting, kiteboarding, or cooking.
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