The Machine Learning for Health and Well-Being (MLwell) Lab is a research lab at the Bio-Medical Engineering department at Tel-Aviv University. Our vision is to create the technology to allow everyone and everywhere access to personalized medicine and precision psychology that is: (i) effective (ii) respects the biological, cultural and behavioral differences between people (iii) respects privacy and other ethical requirements (iv) affordable. Our mission is to improve the state in the art in machine learning algorithms for personalized medicine and precision psychology.
Fake, deep-fake, the video law and the principle of narrow interpretation: what is the law?
How should courts act upon deep-fakes? should legislators modify existing laws to address it? In a new paper (in Hebrew) we study these issues and suggest that adding clauses to existing legislation specific to deep-fake might create problems in laws that do not add such clauses due to the principle of narrow interpretation.
Congratulations to Daniel, Neta, Lotan, Yuval and Yuval on their great undergrad projects.
Congratulations to Daniel Sarusi, Neta Biran, Lotan Hacohen, Yuval Reingold, and Yuval Argoetti on very successful presentations of their undergrad projects. Click the logo ← to see a short video (in Hebrew) about Yuval A.'s project.
The Case Against Explainability
Explainability has been proposed as a possible solution to some of the risks emerging from recent advances in AI. In this paper, we study explanations from legal point of view and show that many of the reasons for requiring explanations cannot be fulfilled by AI systems. Moreover, in some cases, these explanations can increase risks instead of mitigating them.
Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson’s Disease
Remote assessment of the gait of older adults (OAs) during daily living using wrist-worn sensors has the potential to augment clinical care and mobility research. However, hand movements can degrade gait detection from wrist-sensor recordings. To address this challenge, we developed an anomaly detection algorithm and compared its performance to four previously published gait detection algorithms. Multiday accelerometer recordings from a wrist-worn and lower-back sensor (i.e., the “gold-standard” reference) were obtained in 30 OAs, 60% with Parkinson’s disease (PD). The area under the receiver operator curve (AUC) and the area under the precision–recall curve (AUPRC) were used to evaluate the performance of the algorithms. The anomaly detection algorithm obtained AUCs of 0.80 and 0.74 for OAs and PD, respectively, but AUPRCs of 0.23 and 0.31 for OAs and PD, respectively. The best performing detection algorithm, a deep convolutional neural network (DCNN), exhibited high AUCs (i.e., 0.94 for OAs and 0.89 for PD) but lower AUPRCs (i.e., 0.66 for OAs and 0.60 for PD), indicating trade-offs between precision and recall. When choosing a classification threshold of 0.9 (i.e., opting for high precision) for the DCNN algorithm, strong correlations (r > 0.8) were observed between daily living walking time estimates based on the lower-back (reference) sensor and the wrist sensor. Further, gait quality measures were significantly different in OAs and PD compared to healthy adults. These results demonstrate that daily living gait can be quantified using a wrist-worn sensor.
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits
When interacting with humans, boredom/novelty should be considered when selecting content. In this paper, recently presented at AIstats we present a novel version of the multi-armed bandit problem that defines this problem. We present theoretical analysis of the problem and algorithms.
Congratulations to Amnon Catav for defending his thesis
Congratulations to Amnon Catav for successfully defending his Master's thesis