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How do practitioners who develop consumer AI products scope, motivate, and conduct privacy work? Respecting pri- vacy is a key principle for developing ethical, human-centered AI systems, but we cannot hope to better support practitioners without answers to that question. We interviewed 35 industry AI practitioners to bridge that gap. We found that practitioners viewed privacy as actions taken against pre-defined intrusions that can be exacerbated by the capabilities and requirements of AI, but few were aware of AI-specific privacy intrusions documented in prior literature. We found that their privacy work was rigidly defined and situated, guided by compliance with privacy regulations and policies, and generally demoti- vated beyond meeting minimum requirements. Finally, we found that the methods, tools, and resources they used in their privacy work generally did not help address the unique pri- vacy risks introduced or exacerbated by their use of AI in their products. Collectively, these findings reveal the need and opportunity to create tools, resources, and support structures to improve practitioners’ awareness of AI-specific privacy risks, motivations to do AI privacy work, and ability to ad- dress privacy harms introduced or exacerbated by their use of AI in consumer products. 

Modern advances in AI have increased employer interest in tracking workers’ biometric signals — e.g., their brainwaves and facial expressions — to evaluate and make predictions about their performance and productivity. These technologies afford managers information about internal emotional and physiological states that were previously accessible only to individual workers, raising new concerns around worker privacy and autonomy. Yet, the research literature on the impact of AI-powered biometric work monitoring (AI-BWM) technologies on workers remains fragmented across disciplines and industry sectors, limiting our understanding of its impacts on workers at large. In this paper, we sytematically review 129 papers, spanning varied disciplines and industry sectors, that discuss and analyze the impact of AI-powered biometric monitoring technologies in occupational settings. We situate this literature across a process model that spans the development, deployment, and usage phases of these technologies. We further draw on Shelby et al.’s Taxonomy of Socio-technical Harms in AI systems to systematize the harms experienced by workers across the three phases of our process model. We find that the development, deployment, and sustained use of AI-powered biometric work monitoring technologies put workers at risk of a number of the socio-technical harms specified by Shelby et al.: e.g., by forcing workers to exert additional emotional labor to avoid flagging unreliable affect monitoring systems, or through the use of these data to make inferences about productivity. Our research contributes to the field of critical AI studies by highlighting the potential for a cascade of harms to occur when the impact of these technologies on workers is not considered at all phases of our process model. 

Privacy is a key principle for developing ethical AI technologies, but how does including AI technologies in products and services change privacy risks? We constructed a taxonomy of AI privacy risks by an- alyzing 321 documented AI privacy incidents. We codifed how the unique capabilities and requirements of AI technologies described in those incidents generated new privacy risks, exacerbated known ones, or otherwise did not meaningfully alter the risk. We present 12 high-level privacy risks that AI technologies either newly created (e.g., exposure risks from deepfake pornography) or exacerbated (e.g., surveillance risks from collecting training data). One upshot of our work is that incorporating AI technologies into a product can alter the privacy risks it entails. Yet, current approaches to privacy-preserving AI/ML (e.g., federated learning, diferential pri- vacy, checklists) only address a subset of the privacy risks arising from the capabilities and data requirements of AI. 

Incremental graphs that change over time capture the changing relationships of different entities. Given that many real-world networks are extremely large, it is often necessary to partition the network over many distributed systems and solve a complex graph problem over the partitioned network. This paper presents a distributed algorithm for identifying strongly connected components (SCC) on incremental graphs. We propose a two-phase asynchronous algorithm that involves storing the intermediate results between each iteration of dynamic updates in a novel meta-graph storage format for efficient recomputation of the SCC for successive iterations. To the best of our knowledge, this is the first attempt at identifying SCC for incremental graphs across distributed compute nodes. Our experimental analysis on real and synthesized graphs shows up to 2.8x performance improvement over the state-of-the-art by reducing the overall memory utilized and improving the communication bandwidth. 

This study compared the mechanical properties of a recyclable flax fiber reinforced polymer composite (FFRP) with a covalent adaptable network (CAN) matrix to an FFRP composite with a conventional (unrecyclable) epoxy resin matrix. The results indicated that composites fabricated via vacuum-assisted resin transfer molding (VARTM) exhibited up to 19% higher tensile modulus and strength compared to those fabricated via hand layup, attributed to reduced air void content and more uniform fiber alignment. Microscopy evidence supported by mechanical property tests revealed superior adhesion of the CAN matrix to flax fibers compared to conventional epoxy resin. Additionally, a solvent-based method was demonstrated for separating fibers from the CAN matrix, facilitating reuse or upcycling. 

Ising superconductivity, observed in NbSe₂and similar materials, has generated tremendous interest. Recently, attention was called to the possible role that spin fluctuations (SF) play in this phenomenon, in addition to the dominant electron–phonon coupling (EPC); the possibility of a predominantly triplet state was discussed and led to a conjecture of viable singlet–triplet Leggett oscillations. However, these hypotheses have not been put to a quantitative test. In this paper, we report first principle calculations of the EPC and also estimate coupling with SF, including full momentum dependence. We find that: (1) EPC is strongly anisotropic, largely coming from the$$K-{K}^{{\prime} }$$$K-K^{\prime }$scattering, and therefore excludes triplet symmetry even as an excited state; (2) superconductivity is substantially weakened by SF, but anisotropy remains as above; and, (3) we do find the possibility of a Leggett mode, not in a singlet–triplet but in ans₊₊–s_±channel.