In an era dominated by oceans of data, D^3 hosts a community of practice that offers a discerning perspective on the intricate tapestry of data governance, privacy, security, and regulatory compliance. Participants in this community engage in a comprehensive discourse amongst industry experts, exploring responsible AI and the dynamic realm of regulations, industry standards, and best practices. The community explores how to harness the immense potential of data for strategic business advancement while ensuring the safeguarding of sensitive information, upholding privacy rights, and establishing or integrating resilient and ethical frameworks that underpin success in data-driven endeavors.
As the global economy grapples with the pressing challenges of climate change, a new paradigm is emerging in the world of finance and investment. In their working paper, “Climate Solutions, Transition Risk, and Stock Returns,“ researchers Shirley Lu, Assistant Professor of Business Administration at Harvard Business School (HBS) and an affiliate of the HBS Digital […]
In the fast-paced and competitive world of entrepreneurship, advice can be a powerful tool for success or a potential pitfall. Recent research by Susan Cohen, Associate Professor, Terry College of Business at the University of Georgia, and Rembrand Koning, Associate Professor of Business Administration at Harvard Business School and Principal Investigator in the Tech for […]
As businesses grapple with an ever-growing volume of ideas, products, and solutions to evaluate, decision-making processes are being reshaped by artificial intelligence (AI). Generative AI, in particular, has emerged as a game-changer in creative problem-solving and evaluation, as demonstrated by a recent field experiment described in the working paper “The Narrative AI Advantage? A Field […]
In today’s data-driven world, businesses face the dual challenge of leveraging vast datasets to gain insights while ensuring compliance with stringent data privacy regulations. The concept of machine unlearning, a method for efficiently removing the influence of specific data points from machine learning models, represents a paradigm shift in managing data responsibly. Recent research explores […]
