At Rise of AI 2019 in Berlin, Norberto Andrade, Privacy and Public Policy Manager at Facebook, presents three perspectives on how Facebook builds AI: design, product and research. He introduces Facebook's design team's "four quadrants" framework, which plots products from small-scale pixels to full ecosystems, and from individual users to global society, arguing ethical risk grows as products scale up in both dimensions. He describes AI as foundational to Facebook, powering newsfeed ranking and translating over 6 billion posts a day, and previews the Codec Avatars project, which uses machine learning to animate ultra-realistic avatars for remote interaction in AR/VR, while flagging open questions on diverse training data and avatar authentication. He explains that AI removes 99.6 percent of fake accounts before users report them, and outlines Facebook's fairness process across people, data and algorithms, including subgroup calibration and documented decision thresholds. He closes describing the Facebook AI Research lab's open-research mission and Facebook's support for an AI ethics institute at the Technical University of Munich.
Key takeaways
- Facebook's "four quadrants" framework maps products by scale (pixels to ecosystem) and audience (individual to humanity) to locate ethical risk.
- Facebook processes over 6 billion translated posts a day using AI-powered automatic translation.
- AI finds and flags 99.6 percent of fake accounts before users report them, per Andrade.
- The Codec Avatars project uses machine learning to animate realistic avatars for real-time remote interaction in AR/VR.
- Facebook's fairness process spans people, data and algorithms, including subgroup calibration and documented decision thresholds.
Detailed notes
Three perspectives: design, product, research
Andrade structures the talk around three perspectives on ethics at Facebook: design, AI product and AI research. He frames Facebook's ethical responsibility as amplifying the good and minimizing the bad, which in product terms means considering what the company is able to build (technical constraints and resources), what it is allowed to build (company policy and law), and, most importantly, what it should or should not build, given the impact on people across the world.
The four quadrants of design responsibility
Andrade presents a framework from Facebook's design team plotting products on two axes: scale, from a single pixel to the full ecosystem, and audience, from one individual to all of humanity. He argues designers are most comfortable in the bottom-left quadrant, where the audience and use case are well understood, but that moving toward ecosystem-scale, society-wide products increases unintended consequences that only reveal themselves at scale, citing Facebook's use both for organizing movements like the Women's March and for election interference and spreading hoaxes.
AI as foundational to Facebook's products
Andrade says Facebook could not exist without AI, which powers features including newsfeed ranking, using signals like reactions, shares and comments, and automatic translation, which processes over 6 billion translated posts a day so users can communicate across languages. He also says AI is central to Facebook's mission of bringing people closer together and to keeping communities safe, citing that AI finds and flags 99.6 percent of fake accounts before users report them.
Realistic avatars for AR and VR
Andrade describes Facebook's vision for AR and VR letting people feel physically present with distant others, requiring lifelike avatars that convey facial expressions, voice tone and gesture. He presents the Codec Avatars project, which uses machine learning trained on sensor data from prototype headsets to animate a realistic representation of a person's face in real time, illustrated by a demo of two people, Nadia and Dan, conversing through avatars, and an early prototype of two remote users playing a shared virtual soccer game. He flags open ethical questions, including the need for diverse training data across body shapes, sizes and skin tones, and using face recognition or fingerprints to ensure only the real person can control their avatar.
Building fairness into people, data and algorithms
Andrade outlines Facebook's AI ethics approach as investing in three pillars: the people who build and review AI systems, the data used to train them, and the algorithms themselves. He argues fairness cannot be solved purely technically and describes a three-step process of surfacing hard fairness questions, such as whether to optimize for procedural consistency or equal outcomes across groups, resolving them for a given product, and documenting the decisions for accountability. He notes bias can emerge at each step, from human annotators' labeling practices to algorithmic over- or under-prediction to the decision thresholds used, for example, in classifiers that detect false news, and says Facebook performs subgroup calibration to apply consistent standards across groups.
Open research and academic partnerships
Andrade describes the Facebook AI Research (FAIR) lab, then five years old and spread across seven locations, whose mission is advancing AI through open research: publishing peer-reviewed results, releasing data, code and models, and forming research agreements with academic institutions. He gives two examples of research directions: teaching machines to understand physical properties of the world, such as predicting how a falling object will land, and training generative models for creative tasks like designing clothing. He closes by citing Facebook's support for a new AI ethics institute at the Technical University of Munich, saying some ethical questions go beyond Facebook and even beyond AI as a technical discipline.
Quotes
Facebook without AI could not even exist.
Norberto Andrade
Fairness is something that you cannot solve merely through an exclusive technical standpoint.
Norberto Andrade
Questions answered in this talk
How does Facebook use AI in its newsfeed?
According to Norberto Andrade at Rise of AI 2019, Facebook's newsfeed ranking is powered by machine learning, using signals such as how many people react, share and comment on a post to determine its placement.
What is Facebook's Codec Avatars project?
Norberto Andrade describes Codec Avatars as a project using machine learning trained on facial expression, voice tone and movement data to animate ultra-realistic avatars, letting two remote people interact together in a shared virtual reality space in real time.
How does Facebook approach fairness and bias in AI?
Norberto Andrade explains that Facebook treats fairness as a process across people, data and algorithms, involving surfacing hard fairness questions, deciding on a definition of fairness for a given product, documenting the decision, and performing subgroup calibration to apply consistent decision thresholds across groups.