270 citations · 752 across the 12 of their papers we have counts for
13 papers
Quantitative AI Risk Assessments: Opportunities and Challenges
David Piorkowski, Michael Hind, John Richards
Although AI systems are increasingly being leveraged to provide value to organizations, individuals, and society, significant attendant risks have been identified and have manifest…
Better Together? An Evaluation of AI-Supported Code Translation
Justin D. Weisz, Michael Muller, Steven I. Ross +5
Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from co…
Evaluating a Methodology for Increasing AI Transparency: A Case Study
David Piorkowski, John Richards, Michael Hind
In reaction to growing concerns about the potential harms of artificial intelligence (AI), societies have begun to demand more transparency about how AI models and systems are crea…
Using Document Similarity Methods to create Parallel Datasets for Code Translation
Mayank Agarwal, Kartik Talamadupula, Fernando Martinez +5
Translating source code from one programming language to another is a critical, time-consuming task in modernizing legacy applications and codebases. Recent work in this space has…
AI Explainability 360: Impact and Design
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.…
Perfection Not Required? Human-AI Partnerships in Code Translation
Justin D. Weisz, Michael Muller, Stephanie Houde +5
Generative models have become adept at producing artifacts such as images, videos, and prose at human-like levels of proficiency. New generative techniques, such as unsupervised ne…
Quality Estimation & Interpretability for Code Translation
Mayank Agarwal, Kartik Talamadupula, Stephanie Houde +5
Recently, the automated translation of source code from one programming language to another by using automatic approaches inspired by Neural Machine Translation (NMT) methods for n…
Towards evaluating and eliciting high-quality documentation for intelligent systems
David Piorkowski, Daniel González, John Richards +1
A vital component of trust and transparency in intelligent systems built on machine learning and artificial intelligence is the development of clear, understandable documentation.…
A Methodology for Creating AI FactSheets
John Richards, David Piorkowski, Michael Hind +2
As AI models and services are used in a growing number of highstakes areas, a consensus is forming around the need for a clearer record of how these models and services are develop…
Business (mis)Use Cases of Generative AI
Stephanie Houde, Vera Liao, Jacquelyn Martino +5
Generative AI is a class of machine learning technology that learns to generate new data from training data. While deep fakes and media-and art-related generative AI breakthroughs…
Experiences with Improving the Transparency of AI Models and Services
Michael Hind, Stephanie Houde, Jacquelyn Martino +4
AI models and services are used in a growing number of highstakes areas, resulting in a need for increased transparency. Consistent with this, several proposals for higher quality…
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their…
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…