8 citations · 13 across the 3 of their papers we have counts for
7 papers · 1 filter
Understanding Unequal Gender Classification Accuracy from Face Images
Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha +7
Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender. Acc…
PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences
Payel Das, Kahini Wadhawan, Oscar Chang +6
Given the emerging global threat of antimicrobial resistance, new methods for next-generation antimicrobial design are urgently needed. We report a peptide generation framework Pep…
TED: Teaching AI to Explain its Decisions
Michael Hind, Dennis Wei, Murray Campbell +5
Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However,…
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind +15
Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and priso…
FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity
Matthew Arnold, Rachel K. E. Bellamy, Michael Hind +10
Accuracy is an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety (which includes fairness and explainabil…
Teaching machines to understand data science code by semantic enrichment of dataflow graphs
Evan Patterson, Ioana Baldini, Aleksandra Mojsilovic +1
Your computer is continuously executing programs, but does it really understand them? Not in any meaningful sense. That burden falls upon human knowledge workers, who are increasin…