The Berkman Klein Center has appointed Hae Jin (Hayley) Song as a Fellow, where she will focus on the geometric foundations of **AI interpretability** and **safety**. Song, who also serves as an **AI Research Fellow** at **ThoughtWorks**, aims to explore the internal behaviors of AI systems, particularly modern **generative models**. Her research is pivotal as the landscape of artificial intelligence continues to evolve rapidly, affecting various sectors from creative industries to public safety.
Rather than viewing AI models as opaque “black boxes,” Song investigates their internal structure and dynamics. Her work delves into how information is represented within these systems, how models form patterns, and how minor internal changes can yield significantly different outputs. By applying concepts from geometry, Song aims to map out and clarify these complex AI systems, which could lead to a better understanding of their operational mechanisms and decision-making processes.
Song’s overarching goal is to establish reliable, scalable methods for describing and influencing AI behaviors. By identifying geometric “fingerprints” within models, she seeks to elucidate the reasons behind specific behaviors and steer these systems toward safer, more predictable outcomes. Her research holds practical implications for critical areas such as detecting **deepfakes**, understanding inherent **bias**, diagnosing failures, and improving our capabilities to align AI systems with human values.
In her pursuit, Song expresses a particular desire to engage with policymakers, platform designers, and researchers dedicated to responsible AI governance. Her ambition is to provide them with principled and scalable tools to analyze, attribute, and control generative models, including large language models (LLMs) and video/image generators. Additionally, she aims to connect technical and public-interest communities to ensure that **safety**, **accountability**, and **trustworthiness** are integral to how AI systems are comprehended and utilized.
People might be surprised to learn that generative models leave subtle yet identifiable “fingerprints” in their outputs, which can be traced back to their source models. These fingerprints carry valuable insights about a model’s internal behavior and can serve as tools for data and model attribution, allowing for accountability without necessitating access to training data or proprietary source code.
Song’s work is particularly relevant today as generative AI finds its way into various facets of society, ranging from creative applications to potential tools for misinformation and deepfakes. The urgent need for reliable methods to understand and govern these systems cannot be overstated. Without robust mechanisms for attribution and control, it becomes increasingly challenging for regulators and platforms to enforce standards, leaving users vulnerable to untrustworthy content.
If Song’s model attribution methods were widely adopted, they could initiate significant changes in policy and platform operations. Potential outcomes might include stronger provenance labels, more effective content moderation policies, and clearer accountability standards across AI platforms. This shift could facilitate a landscape where the origins of generative content are routinely verified, making misuse more difficult and enhancing transparency for users, regulators, and developers alike.
However, the integrity of a model’s fingerprint can be compromised when it is tampered with through methods such as jailbreaks or backdoors. Such alterations may distort the geometric fingerprint; nonetheless, the underlying structure often remains detectable. By studying how fingerprints alter under these adversarial conditions, researchers like Song aim to develop robust attribution and defense mechanisms that can withstand attacks.
As generative AI continues to integrate into everyday life, the significance of understanding its implications grows. Song’s pioneering work in exploring the geometric aspects of AI models not only sheds light on their internal dynamics but also provides essential tools for fostering trust and accountability in an increasingly digital landscape.
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