Imagine you are asked to solve a Rubik’s Cube. For most of us, the task is clear: rotate the cube’s faces to make each side a single color.
What happens, however, when someone unfamiliar picks up the iconic puzzle and is asked to make each side one color? Do they shift the sides around like one of us, or do they approach the cube another way? Do they rearrange the colored stickers of the cube? How about painting each side a single color? Do they look for a button that magically changes the color of each face?
When a user’s task is not clearly defined, unintended consequences can occur. That is what American University Professor Heng Xu—one of the world’s foremost cybersecurity experts—reveals through her current research on how algorithms respond to tasks.
A professor of information technology and analytics in AU’s Kogod School of Business, Xu also serves as director of the Kogod Cybersecurity Governance Center, overseeing the university’s initiative centered on cybersecurity and privacy research.
Through her work in artificial intelligence (AI) governance, privacy protection, data ethics, and fairness in machine learning, Xu is redefining corporate social responsibility in today’s AI era. Her expertise is so interdisciplinary that it doesn’t come as a surprise Xu was recently introduced at a professional conference as a unicorn. Her work bridges disciplines beyond business, including computer science, law, and psychology.
Professor Xu and a team of collaborators are currently in the middle of a $1 million three-year project examining fairness and bias in AI hiring systems. Funded by the National Science Foundation and Amazon, Kogod is the only business school among the awardees’ lead institutions.
Xu’s team combines their data ethics, machine learning, and human resource management expertise to find solutions valuable to the technology and business communities. By focusing on AI in the business domain of human resource management, their work has the potential to effect positive change in hiring practices worldwide.
As more businesses wish to implement AI to improve efficiency, Xu’s study becomes even more essential to ensure results that are both fair and without unintended consequences.
Just like an unfamiliar person solving a Rubik’s Cube, if the task and objective are not made clear, AI can respond in unexpected ways.