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Will generative AI transform robotics? Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27
In the current wave of excitement about applying large vision–language models and generative AI to robotics, expectations are running high, but conquering real-world complexities remains challenging for robots.
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Machine learning for micro- and nanorobots Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27 Lidong Yang, Jialin Jiang, Fengtong Ji, Yangmin Li, Kai-Leung Yung, Antoine Ferreira, Li Zhang
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Neuromorphic visual scene understanding with resonator networks Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27 Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, Bruno A. Olshausen, Yulia Sandamirskaya, Friedrich T. Sommer, E. Paxon Frady
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Visual odometry with neuromorphic resonator networks Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27 Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, E. Paxon Frady, Friedrich T. Sommer, Yulia Sandamirskaya
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Direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27 Osama Abdin, Philip M. Kim
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Laplace neural operator for solving differential equations Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-24 Qianying Cao, Somdatta Goswami, George Em Karniadakis
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Challenges, evaluation and opportunities for open-world learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-24 Mayank Kejriwal, Eric Kildebeck, Robert Steininger, Abhinav Shrivastava
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Coordinate-based neural representations for computational adaptive optics in widefield microscopy Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-24 Iksung Kang, Qinrong Zhang, Stella X. Yu, Na Ji
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Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-21 Evan E. Seitz, David M. McCandlish, Justin B. Kinney, Peter K. Koo
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Systematic analysis of 32,111 AI model cards characterizes documentation practice in AI Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-21 Weixin Liang, Nazneen Rajani, Xinyu Yang, Ezinwanne Ozoani, Eric Wu, Yiqun Chen, Daniel Scott Smith, James Zou
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Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-21 Dong Chen, Jian Liu, Guo-Wei Wei
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Reconciling privacy and accuracy in AI for medical imaging Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-21 Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard F. Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
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Machine learning-aided generative molecular design Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-18 Yuanqi Du, Arian R. Jamasb, Jeff Guo, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Liò, Philippe Schwaller, Tom L. Blundell
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Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-17 Huan Yee Koh, Anh T. N. Nguyen, Shirui Pan, Lauren T. May, Geoffrey I. Webb
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Discovering neural policies to drive behaviour by integrating deep reinforcement learning agents with biological neural networks Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-14 Chenguang Li, Gabriel Kreiman, Sharad Ramanathan
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Generic protein–ligand interaction scoring by integrating physical prior knowledge and data augmentation modelling Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-06 Duanhua Cao, Geng Chen, Jiaxin Jiang, Jie Yu, Runze Zhang, Mingan Chen, Wei Zhang, Lifan Chen, Feisheng Zhong, Yingying Zhang, Chenghao Lu, Xutong Li, Xiaomin Luo, Sulin Zhang, Mingyue Zheng
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Learning efficient backprojections across cortical hierarchies in real time Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-06 Kevin Max, Laura Kriener, Garibaldi Pineda García, Thomas Nowotny, Ismael Jaras, Walter Senn, Mihai A. Petrovici
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Distributed constrained combinatorial optimization leveraging hypergraph neural networks Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-30 Nasimeh Heydaribeni, Xinrui Zhan, Ruisi Zhang, Tina Eliassi-Rad, Farinaz Koushanfar
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Empathic AI can’t get under the skin Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-24
Personalized LLMs built with the capacity for emulating empathy are right around the corner. The effects on individual users needs careful consideration.
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Accurate and robust protein sequence design with CarbonDesign Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-23 Milong Ren, Chungong Yu, Dongbo Bu, Haicang Zhang
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Quantum circuit synthesis with diffusion models Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-20 Florian Fürrutter, Gorka Muñoz-Gil, Hans J. Briegel
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Back to basics to open the black box Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-17 Diego Marcondes, Adilson Simonis, Junior Barrera
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Efficient learning of accurate surrogates for simulations of complex systems Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-17 A. Diaw, M. McKerns, I. Sagert, L. G. Stanton, M. S. Murillo
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Does it matter if empathic AI has no empathy? Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-15 Garriy Shteynberg, Jodi Halpern, Amir Sadovnik, Jon Garthoff, Anat Perry, Jessica Hay, Carlos Montemayor, Michael A. Olson, Tim L. Hulsey, Abrol Fairweather
Imagine a machine that provides a simulation of any experience a person might want, but once the machine is activated, the person is unable to tell that the experience isn’t real. When Robert Nozick formulated this thought experiment in 1974 (ref. 1), it was meant to be obvious that people in otherwise ordinary circumstances would be making a horrible mistake if they hooked themselves up to such a
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Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-13 Ning Wang, Jiang Bian, Yuchen Li, Xuhong Li, Shahid Mumtaz, Linghe Kong, Haoyi Xiong
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Diving into deep learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-10 Ge Wang
Understanding Deep Learning Simon J. D. PrinceThe MIT Press: 2023. 544 pp. $90.00 The field of artificial intelligence (AI) has experienced a surge in developments over the past years, propelled by breakthroughs in deep learning with neural networks. This has revolutionized many aspects of society. However, the speed at which AI is advancing highlights the need for textbooks that provide essential
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Augmenting large language models with chemistry tools Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-08 Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D. White, Philippe Schwaller
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Predicting equilibrium distributions for molecular systems with deep learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-08 Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu
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Maximum diffusion reinforcement learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-02 Thomas A. Berrueta, Allison Pinosky, Todd D. Murphey
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The rewards of reusable machine learning code Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-24
Research papers can make a long-lasting impact when the code and software tools supporting the findings are made readily available and can be reused and built on. Our reusability reports explore and highlight examples of good code sharing practices.
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The benefits, risks and bounds of personalizing the alignment of large language models to individuals Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-23 Hannah Rose Kirk, Bertie Vidgen, Paul Röttger, Scott A. Hale
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Dangers of speech technology for workplace diversity Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-22 Mike Horia Mihail Teodorescu, Mingang K. Geiger, Lily Morse
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Artificial intelligence tackles the nature–nurture debate Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-19 Justin N. Wood
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The synergy complement control approach for seamless limb-driven prostheses Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-19 Johannes Kühn, Tingli Hu, Alexander Tödtheide, Edmundo Pozo Fortunić, Elisabeth Jensen, Sami Haddadin
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Synthetic Lagrangian turbulence by generative diffusion models Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-17 T. Li, L. Biferale, F. Bonaccorso, M. A. Scarpolini, M. Buzzicotti
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Equivariant 3D-conditional diffusion model for molecular linker design Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-11 Ilia Igashov, Hannes Stärk, Clément Vignac, Arne Schneuing, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael Bronstein, Bruno Correia
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A neural speech decoding framework leveraging deep learning and speech synthesis Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-08 Xupeng Chen, Ran Wang, Amirhossein Khalilian-Gourtani, Leyao Yu, Patricia Dugan, Daniel Friedman, Werner Doyle, Orrin Devinsky, Yao Wang, Adeen Flinker
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Tandem mass spectrum prediction for small molecules using graph transformers Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-05 Adamo Young, Hannes Röst, Bo Wang
Tandem mass spectra capture fragmentation patterns that provide key structural information about molecules. Although mass spectrometry is applied in many areas, the vast majority of small molecules lack experimental reference spectra. For over 70 years, spectrum prediction has remained a key challenge in the field. Existing deep learning methods do not leverage global structure in the molecule, potentially
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A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-05 Yanyi Chu, Dan Yu, Yupeng Li, Kaixuan Huang, Yue Shen, Le Cong, Jason Zhang, Mengdi Wang
The 5′ untranslated region (UTR), a regulatory region at the beginning of a messenger RNA (mRNA) molecule, plays a crucial role in regulating the translation process and affects the protein expression level. Language models have showcased their effectiveness in decoding the functions of protein and genome sequences. Here, we introduce a language model for 5′ UTR, which we refer to as the UTR-LM. The
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Geometry-enhanced pretraining on interatomic potentials Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-05 Taoyong Cui, Chenyu Tang, Mao Su, Shufei Zhang, Yuqiang Li, Lei Bai, Yuhan Dong, Xingao Gong, Wanli Ouyang
Machine learning interatomic potentials (MLIPs) describe the interactions between atoms in materials and molecules by learning them from a reference database generated by ab initio calculations. MLIPs can accurately and efficiently predict such interactions and have been applied to various fields of physical science. However, high-performance MLIPs rely on a large amount of labelled data, which are
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The curious case of the test set AUROC Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-04 Michael Roberts, Alon Hazan, Sören Dittmer, James H. F. Rudd, Carola-Bibiane Schönlieb
The area under the receiver operating characteristic curve (AUROC) of the test set is used throughout machine learning (ML) for assessing a model’s performance. However, when concordance is not the only ambition, this gives only a partial insight into performance, masking distribution shifts of model outputs and model instability.
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Reusability report: Uncovering associations in biomedical bipartite networks via a bilinear attention network with domain adaptation Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-04-04 Tao Xu, Haoyuan Shi, Wanling Gao, Xiaosong Wang, Zhenyu Yue
Conditional domain adversarial learning presents a promising approach for enhancing the generalizability of deep learning-based methods. Inspired by the efficacy of conditional domain adversarial networks, Bai and colleagues introduced DrugBAN, a methodology designed to explicitly learn pairwise local interactions between drugs and targets. DrugBAN leverages drug molecular graphs and target protein
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Invalid SMILES are beneficial rather than detrimental to chemical language models Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-29 Michael A. Skinnider
Generative machine learning models have attracted intense interest for their ability to sample novel molecules with desired chemical or biological properties. Among these, language models trained on SMILES (Simplified Molecular-Input Line-Entry System) representations have been subject to the most extensive experimental validation and have been widely adopted. However, these models have what is perceived
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The new NeuroAI Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-22
After several decades of developments in AI, has the inspiration that can be drawn from neuroscience been exhausted? Recent initiatives make the case for taking a fresh look at the intersection between the two fields.
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Generative AI for designing and validating easily synthesizable and structurally novel antibiotics Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-22 Kyle Swanson, Gary Liu, Denise B. Catacutan, Autumn Arnold, James Zou, Jonathan M. Stokes
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A collective AI via lifelong learning and sharing at the edge Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-22 Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Vladimir Braverman, Eric Eaton, Benjamin Epstein, Yunhao Ge, Lucy Halperin, Jonathan How, Laurent Itti, Michael A. Jacobs, Pavan Kantharaju, Long Le, Steven Lee, Xinran Liu, Sildomar T. Monteiro, David Musliner, Saptarshi Nath, Priyadarshini Panda, Christos Peridis, Hamed Pirsiavash, Vishwa Parekh, Kaushik Roy, Shahaf Shperberg, Hava T. Siegelmann, Peter Stone
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Challenges and opportunities in translating ethical AI principles into practice for children Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-20 Ge Wang, Jun Zhao, Max Van Kleek, Nigel Shadbolt
AI systems are becoming increasingly pervasive within children’s devices, apps and services. The concern over a world where AI systems are deployed unchecked has raised burning questions about the impact, governance and accountability of these technologies. Although recent effort on AI ethics has converged into growing consensus on a set of high-level ethical AI principles, engagement with children’s
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Federated learning is not a cure-all for data ethics Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-18 Marieke Bak, Vince I. Madai, Leo Anthony Celi, Georgios A. Kaissis, Ronald Cornet, Menno Maris, Daniel Rueckert, Alena Buyx, Stuart McLennan
Although federated learning is often seen as a promising solution to allow AI innovation while addressing privacy concerns, we argue that this technology does not fix all underlying data ethics concerns. Benefiting from federated learning in digital health requires acknowledgement of its limitations.
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Foundation model for cancer imaging biomarkers Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-15 Suraj Pai, Dennis Bontempi, Ibrahim Hadzic, Vasco Prudente, Mateo Sokač, Tafadzwa L. Chaunzwa, Simon Bernatz, Ahmed Hosny, Raymond H. Mak, Nicolai J. Birkbak, Hugo J. W. L. Aerts
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Connecting molecular properties with plain language Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-13 Glen M. Hocky
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Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-11 Rodrigo Bonazzola, Enzo Ferrante, Nishant Ravikumar, Yan Xia, Bernard Keavney, Sven Plein, Tanveer Syeda-Mahmood, Alejandro F. Frangi
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PocketFlow is a data-and-knowledge-driven structure-based molecular generative model Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-11 Yuanyuan Jiang, Guo Zhang, Jing You, Hailin Zhang, Rui Yao, Huanzhang Xie, Liyun Zhang, Ziyi Xia, Mengzhe Dai, Yunjie Wu, Linli Li, Shengyong Yang
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The democratization of global AI governance and the role of tech companies Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-08 Eva Erman, Markus Furendal
Can non-state multinational tech companies counteract the potential democratic deficit in the emerging global governance of AI? We argue that although they may strengthen core values of democracy such as accountability and transparency, they currently lack the right kind of authority to democratize global AI governance.
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Learning high-level visual representations from a child’s perspective without strong inductive biases Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-07 A. Emin Orhan, Brenden M. Lake
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Reusability report: Leveraging supervised learning to uncover phenotype-relevant biology from single-cell RNA sequencing data Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-03-05 Yingying Cao, Tian-Gen Chang, Sahil Sahni, Eytan Ruppin
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Generating mutants of monotone affinity towards stronger protein complexes through adversarial learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-02-28 Tian Lan, Shuquan Su, Pengyao Ping, Gyorgy Hutvagner, Tao Liu, Yi Pan, Jinyan Li
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AI protein shake-up Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-02-23
One of the most successful areas for deep learning in scientific discovery has been protein predictions and engineering. We take a closer look at four studies in this issue that advance protein science with innovative deep learning approaches.
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Codon language embeddings provide strong signals for use in protein engineering Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-02-23 Carlos Outeiral, Charlotte M. Deane
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Neural multi-task learning in drug design Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-02-20 Stephan Allenspach, Jan A. Hiss, Gisbert Schneider
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Mitigating allocative tradeoffs and harms in an environmental justice data tool Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-02-16 Benjamin Q. Huynh, Elizabeth T. Chin, Allison Koenecke, Derek Ouyang, Daniel E. Ho, Mathew V. Kiang, David H. Rehkopf