Redefining Ownership: Navigating Rights and Governance in the Digital Era
Summary and Highlights from Panel
Rapid advancements in artificial intelligence (AI) technology are reshaping our understanding of economics, ownership, and social well-being. The blackbox Lab at the Digital Data Design Institute at Harvard brought together Christopher T. Bavitz, WilmerHale Professor of Law at Harvard Law School, Jessica Silbey, Professor of Law at Boston University School of Law, and Nien-hê Hsieh, Kim B. Clark Professor of Business Administration at Harvard Business School to explore these topics. The conversation, moderated by James W. Riley, Assistant Professor of Business Administration at Harvard Business School and Principal Investigator at the blackbox Lab, unpacked the complex relationships between ownership, governance, and legal frameworks in the digital era. Through the lens of digital ownership and rights, the panelists highlighted the complexities of the digital economy and the need to contemplate the meaning of “fairness” in an AI-driven society.
Professor Hsieh began by interrogating the idea of “ownership” beyond traditional bounds, emphasizing its role in promoting fair access to assets and control over resources. Ownership, he argued, is foundational to achieving inclusivity and fairness, as it determines who can benefit from economic activities. This idea challenges the assumption that ownership is merely a byproduct of economic organization, instead positioning it as central to addressing broader social values in the digital era.
Building on these ideas, Professor Bavitz explores how ownership has shifted in the digital age. He highlighted that with many of our subscriptions to digital media, ownership remains under platform control, where our access is only temporary and can be revoked, unlike the freedom afforded by physical assets that can be owned and transferred per the user’s discretion. This shift poses significant questions about consumer rights and limits the agency traditionally afforded by ownership.
In addition to considerations around ownership in the digital subscription era, Professor Silbey addressed the complexities of copyright in generative AI, explaining how AI training models rely on data scraping for building underlying models. These uses are often deemed transformative under fair use, as they do not replicate the original experience for users but instead serve as tools. However, this legal framing leaves unresolved questions on compensation and control for original creators, raising fairness concerns in AI’s expansive data usage.
The panel concluded by emphasizing the importance of ethical practices in AI development. They highlighted blockchain, data portability, and fair compensation for creators as promising steps, while also acknowledging the challenges. Ultimately, they argued, fostering a transparent and socially responsible digital economy is critical to achieving a future where technological progress aligns with inclusivity and fairness.
Nien-hê Hsieh on Defining the Ownership Project
Professor Nien-hê Hsieh explains that the Ownership Project seeks to explore how rethinking ownership in broad terms can advance fairness, inclusivity, and well-being in economic activity. In this clip, Dr. Hsieh discusses how ownership — of financial assets, productive capital, and resources — plays a key role in shaping who has access, control, and who ultimately benefits, emphasizing that a more thoughtful approach to ownership is essential for building a fair and inclusive economy.
Christopher Bavitz on Ownership in the Digital Age
Professor Christopher Bavitz explores how ownership concepts shift in the context of digital media. In this clip, he contrasts traditional ownership of physical books, records, and DVDs — where consumers have control over their purchased items — with today’s subscription-based model for digital content. Unlike physical assets, subscribing to services like Spotify means that ownership is replaced by temporary access, which ends as soon as the subscription does.
Jessica Silbey on Copyright under Generative AI
Professor Jessica Silbey discusses the use of copyrighted works in AI training models. In this clip, Silbey explains how AI systems scrape the internet to create datasets, comparing it to Google Search or Google Books. She notes that the law generally views that when use cases are in the aggregate, seeking information, as either transformative under fair use or as non-copyright uses entirely — similar to using sheet music as wallpaper, where it doesn’t replace the original work’s expressive value.
Christopher Bavitz on AI Training and Development
Professor Bavitz discusses the challenges of inclusivity in generative AI systems. In this clip, Bavitz argues that relying solely on voluntary contributions of content for AI training will lead to skewed, non-inclusive systems, only reflective of those who contributed. He asserts that one potential benefit of the broad scope of material on which AI systems are trained is that it is more likely to reflect society as a whole.
