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How AI Will Shape the Future of Data Science

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Meet the Expert: Jeff Gill, PhD, MBA

Jeff Gill

Dr. Jeff Gill is a distinguished professor in the Department of Government and the Department of Mathematics and Statistics 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.

The Most Significant Shift Has Been The Pace of Change

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.”

The Need For Regulation AI-Driven Data Science

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.

Positive Aspects of AI Data Science

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.”

Negative Consequences of AI in Data Science

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.

How To Future Proof Your Data Science Career In The Age Of AI

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.

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