Miss Frances Ding (2017)
Born in Canada and raised in both Vancouver, BC, and Nashville, Tennessee, I’ve seen a wide spectrum of people’s life experiences, which go on to build drastically different world views. These world views dictate societal structures, often overlooking the perspectives of the marginalized. How is this relevant to my studies? I believe that while the artificial intelligence revolution has the potential to greatly improve lives, it also presents a pressing risk: machine learning algorithms may entrench the assumptions and biases of the global elite in systems ranging from gendered job advertising to racially discriminatory loan decisions. As an undergraduate at Harvard University, my extracurricular involvement with Partners in Health Engage and Effective Altruism taught me that even the most well-intentioned plans to improve the world can fail if they aren’t empirically tested in different cultures and contexts. Thus at Cambridge, I’ll be undertaking an MPhil in Machine Learning, Speech and Language Technologies, with a particular interest in the interpretability of machine learning algorithms, inverse reinforcement learning of human values, and the development of algorithms robust to many contexts. My hope is that soon, algorithms will be able to work alongside humans to make better loan decisions, text analyses, medical diagnoses, and improve lives around the world.