Events

AI Entrepreneur’s Playbook: Defining and Pitching Your AI Startup, a virtual summer Learning Sprint, from Digital Data Design Institute and Harvard Business Analytics Program; currently running from June 26,2024 through July 31, 2024. This learning sprint will equip aspiring entrepreneurs with the essential skills to turn AI ideas into effectively executed projects and captivating pitches. Join our expert speakers Marily […]

Understanding the AI product management field has become more crucial now than ever, particularly in light of recent advancements in artificial intelligence and Machine Learning technologies including the recent proliferation of generative AI large language models. Considering the speed with which AI is becoming pervasive in our lives, having a strong grasp of AI concepts and frameworks is critical. This becomes more pertinent especially for aspiring AI product managers without significant technical (e.g., coding) background. 

Hosted by Harvard Business School and Digital Data Design Institute at Harvard Join us to explore AI’s profound impact on the world today and how it is reshaping the future of business and society. This conference will examine the business implications, ethical considerations, and regulatory practices related to AI, and will offer frameworks for encouraging […]

In this event, Philipp Kandal, the Chief Product Officer of Grab will discuss how Southeast Asia's all-in-one platform (Grab) is leveraging artificial intelligence to enhance its extensive delivery fleet. This session will explore the innovative strategies and cutting-edge AI technologies employed by Grab to optimize delivery routes, ensure timely deliveries, and improve overall efficiency. Join us at this event to understand the role of machine learning algorithms in predicting delivery demand, managing logistics, and enhancing customer satisfaction. As the leader of Grab's engineering, data science, and product design teams, Philipp will share his views on integrating AI in real-world operations, outstanding challenges of doing so, and the positive impacts that AI can have on the experiences of both delivery personnel customers.

This event will explore the ethics of AI by delving into the intricate balance between technological advancement and ethical responsibility. Our guest contributor, Alberto Chierici, Ph.D. will guide us through an exploration of some of the existential questions and moral dilemmas posed by artificial intelligence and how they impact society, governance, and individual rights. Attendees will gain a deeper understanding of the ethical considerations necessary for the responsible development and deployment of AI technologies.

In this capstone webinar in our series on Gen AI in Healthcare, we will highlight key themes from the series and apply them to a specific use case: AI in skin cancer detection.  Our esteemed panel will synthesize central lessons in responsible AI and provide insights into how healthcare stakeholders should navigate emerging technologies. We […]

Join us for an event that promises to shade light on some of the most commonly held misconceptions about generative AI technology. In this "Ask Me Anything" session, our guest contributor, Amanda Fetch, will discuss real-world applications of generative AI--leveraging her 20+ years of experience as a digital transformation expert and a sought-after advisor to leading organizations in the spaces of Artificial Intelligence and Machine Learning--to highlight areas in which the technology is thriving and to identify shortcomings which will require significant technical and infrastructural improvements to mitigate. Join this event to engage in an enlightening conversation with our guest speaker and to have your questions answered about the constantly shifting generative AI space.

We study how home sellers and buyers interact with Zillow's Zestimate algorithm throughout the sales cycle of residential properties, with an emphasis on the implications of such interactions. In particular, leveraging Zestimate's algorithm updates as exogenous shocks, we find evidence for a human-algorithm feedback loop: listing and selling outcomes respond significantly to Zestimate, and Zestimate is quickly updated for the focal and comparable homes after a property is listed or sold. This raises a concern that housing market disturbances may propagate and persist because of the feedback loop. However, simulation suggests that disturbances are short-lived and diminish eventually, mainly because all marginal effects across stages of the selling process---though sizable and significant---are less than one.

In this 4th webinar in our series on responsible artificial intelligence (AI) in healthcare, we will focus on hospitals and healthcare providers. Our expert panel will discuss AI’s current role and how future technologies can meet hospital and provider needs. We will highlight opportunities for AI to boost efficiency, reduce costs, and improve services. We […]

Join Harvard Business School professors and Principal Investigators of the Data Science and AI Operations lab, Iavor Bojinov and Edward McFowland III, as they engage in an enlightening conversation with the international bestselling author QuHarrison Terry on the exciting topic of operationalizing generative AI. Whether you are a business leader, a tech professional, or simply an AI enthusiast, this event will offer you valuable perspectives and actionable insights on making AI a transformative tool in order to drive your organization forward and toward a smarter, more efficient future.

Join us as we discuss the latest report out of the Digital Value Lab led by Professor Suraj Srinivasan, one of the leading research labs at the Digital Data Design Institute at Harvard Business School. Conducted in partnership with Capgemini, this report unveils the rise of the eco-digital economy. In this collaborative study, we surveyed 1500 executives to discover how the expansion of cutting-edge technology, innovative breakthroughs, and sustainability investments are driving this growth.

Information about the number of applicants to a job vacancy might simultaneously signal the degree of competition and vacancy quality. We study how this information affects job search. To do so, we conduct three experiments on a large online job platform in which the treatment varies what information is shown to job seekers. Information about the number of prior applicants to a vacancy increases the number of applications and redirects them to vacancies with few prior applications. Information about vacancy age increases application rates, especially to new vacancies.