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Found 12 papers, 2 papers with code
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
We present an auditing framework that offers a holistic assessment of synthetic datasets and AI models trained on them, centered around bias and discrimination prevention, fidelity to the real data, utility, robustness, and privacy preservation.
Accelerating Inhibitor Discovery With A Deep Generative Foundation Model: Validation for SARS-CoV-2 Drug Targets
To perform target-aware design of novel inhibitor molecules, a protein sequence-conditioned sampling on the generative foundation model is performed.
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e. g., high broad-spectrum potency and low toxicity.
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations.
Understanding Unequal Gender Classification Accuracy from Face Images
Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender.
PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences
Our model learns a rich latent space of the biological peptide context by taking advantage of abundant, unlabeled peptide sequences.
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.
FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity
We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers.
Teaching machines to understand data science code by semantic enrichment of dataflow graphs
Your computer is continuously executing programs, but does it really understand them?
Teaching Meaningful Explanations
The adoption of machine learning in high-stakes applications such as healthcare and law has lagged in part because predictions are not accompanied by explanations comprehensible to the domain user, who often holds the ultimate responsibility for decisions and outcomes.
Regularized Co-Clustering with Dual Supervision
By attempting to simultaneously partition both the rows (examples) and columns (features) of a data matrix, Co-clustering algorithms often demonstrate surpris- ingly impressive performance improvements over traditional one-sided (row) clustering techniques.
