In May 2021, Facebook launched the Engineering Approaches to Responsible Neural Interface Design request for proposals (RFP). Today, we’re announcing the winners of this award.
Facebook Reality Labs (FRL) has been exploring neural interfaces as potential input paradigms for controlling augmented reality and/or virtual reality systems. In keeping with Facebook’s Responsible Innovation principles, FRL’s neurotechnology researchers are dedicated to surfacing and considering neuroethical considerations in tandem with system design. As part of these efforts, FRL solicited proposals that leverage engineering to address the following Responsible Innovation principles: considering everyone by promoting inclusivity in system design, putting people first by treating data with care, and providing controls that matter by developing tools and methods for data management and privacy.
Through the awards issued under this RFP, Facebook’s neurotechnology researchers aim to deepen their relationships with the academic community and to champion innovative ideas that promote the ethical development of neurotechnology. The areas of interest for this call for proposals focused on the following topics:
The team reviewed 50 high-quality proposals and are pleased to announce the six winning proposals below. Thank you to everyone who took the time to submit a proposal, and congratulations to the winners.
The effect of hair type and skin pigmentation on fNIRS signal quality
Meryem Ayse Yucel, Bernhard Zimmermann, David Boas, Parya Farzam (Boston University)
Framework for diverse EMG gesture recognition
Jennifer Mankoff, Momona Yamagami (University of Washington)
Privacy-preserving federated learning for minimized fNIRS data
Xiali Hei (University of Louisiana at Lafayette)
Privacy via federated learning with Gaussian processes
Ethan Fetaya, Gal Chechik, Jose Zariffa (Bar-Ilan University)
Racially inclusive optical tech: Develop fNIRS for dark skin & curly hair
Sossena Wood, Jana Kainerstorfer, Pulkit Grover (Carnegie Mellon University)
User-guided EMG data collection that is inclusive across physical abilities
Jacob A. George (University of Utah)