Kevin McDonnell, a researcher at Lero, the irish Software Research Center, is making significant strides in improving the safety of electric vehicles (EVs). His work blends expertise in computer science, artificial intelligence (AI), and a deep commitment to public good. McDonnell’s journey, from earning degrees in computer science and AI to pursuing a PhD in AI and machine learning, has led him to the forefront of EV safety research.
his recent research, detailed in a published paper, focuses on assessing the risks associated with both electric and traditional gasoline-powered vehicles. This complete analysis provides valuable insights for automakers and policymakers alike, informing the growth of enhanced safety measures. “I’ve always wanted to do something that benefited the public,” McDonnell explains, highlighting his motivation for pursuing this critical research.
Navigating the Challenges of Data-Driven Research
McDonnell’s research isn’t without its hurdles. He emphasizes the significant challenges in obtaining usable data, a common obstacle in many fields of scientific inquiry. Even with access to extensive datasets, ensuring data quality and usability remains a major undertaking. “Data procurement is the hardest part of research,” he states. “Even working with a private industry partner with access to millions of datapoints proved challenging.” This underscores the need for improved data accessibility and standardization within the automotive industry.
He further explains the importance of high-quality data: “Getting access to good and usable data is the core of influential research. Without it, it’s hard to prove our assertions and using synthetic options is always prone to bias.”
The Impact of the Pandemic on Public Engagement
McDonnell’s research began during the COVID-19 pandemic, providing a unique outlook on public engagement with science. He notes the fluctuating nature of this engagement,observing that it can range from complete disinterest to overwhelming interest,with little middle ground. He credits Lero for facilitating effective public engagement strategies, acknowledging the challenges researchers face in reaching broader audiences without such support.
McDonnell’s work on model interpretability in machine learning is also noteworthy. This focus on openness and ethical standards in AI decision-making systems reflects a growing concern within the tech industry and beyond. His research contributes to the development of responsible AI practices, ensuring fairness and accountability in the use of these powerful technologies.
McDonnell’s journey highlights the crucial role of accessible, high-quality data in driving impactful research.His work on EV safety and responsible AI development serves as a model for future researchers seeking to contribute to a safer and more equitable world.
The link to his research paper is available here: Research Paper
