Michael Hind
Programming languageVirtual machineComputer scienceJavaTheoretical computer science
What is this?
Publications 84
#1Michael HindH-Index: 25
#2Dennis WeiH-Index: 14
Last. Yunfeng ZhangH-Index: 5
view all 3 authors...
Many proposed methods for explaining machine learning predictions are in fact challenging to understand for nontechnical consumers. This paper builds upon an alternative consumer-driven approach called TED that asks for explanations to be provided in training data, along with target labels. Using semi-synthetic data from credit approval and employee retention applications, experiments are conducted to investigate some practical considerations with TED, including its performance with different cl...
#1Michael HindH-Index: 25
#2Stephanie HoudeH-Index: 3
Last. Kush R. VarshneyH-Index: 18
view all 7 authors...
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 and more consistent documentation of AI data, models, and systems have emerged. Little is known, however, about the needs of those who would produce or consume these new forms of documentation. Through semi-structured developer interviews, and two document creation exercises, we have assembled a clearer picture of thes...
#1Rachel K. E. Bellamy (IBM)H-Index: 23
#2Kuntal Dey (IBM)H-Index: 8
Last. Yunfeng Zhang (IBM)H-Index: 5
view all 17 authors...
Today, machine-learning software is used to help make decisions that affect people's lives. Some people believe that the application of such software results in fairer decisions because, unlike humans, machine-learning software generates models that are not biased. Think again. Machine-learning software is also biased, sometimes in similar ways to humans, often in different ways. While fair model- assisted decision making involves more than the application of unbiased models-consideration of app...
#1Nelson Kibichii Bore (IBM)H-Index: 3
#2Ravi Kiran Raman (IBM)H-Index: 3
Last. Komminist Weldemariam (IBM)H-Index: 9
view all 11 authors...
Policy decisions are increasingly dependent on the outcomes of simulations and/or machine learning models. The ability to share and interact with these outcomes is relevant across multiple fields and is especially critical in the disease modeling community where models are often only accessible and workable to the researchers that generate them. This work presents a blockchain-enabled system that establishes a decentralized trust between parties involved in a modeling process. Utilizing the Open...
May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Ravi Kiran Raman (IBM)H-Index: 3
#2Kush R. Varshney (IBM)H-Index: 18
Last. Michael Hind (IBM)H-Index: 25
view all 7 authors...
In previous work, we proposed a scalable multi-party verification scheme for expensive iterative computations on a Blockchain substrate by appropriate storage and endorsement of frames of iterates. In this work, we extend the framework to verify sets of complete computations with different unordered hyperparameters and develop frame ordering and compression algorithms to enable scalability in the system. We illustrate the efficacy of the proposed approach by verifying the OpenMalaria epidemiolog...
How good are you at explaining your decisions? Are you better than a machine? Today, AI systems are being asked to explain their decisions. This article explores the challenges in solving this problem and approaches researchers are pursuing.
1 CitationsSource
Jan 27, 2019 in AAAI (National Conference on Artificial Intelligence)
#1Michael Hind (IBM)H-Index: 25
#2Dennis Wei (IBM)H-Index: 14
Last. Kush R. Varshney (IBM)H-Index: 18
view all 8 authors...
Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However, as many of these systems are opaque in their operation, there is a growing demand for such systems to provide explanations for their decisions. Conventional approaches to this problem attempt to expose or discover the inner workings of a machine learning model with the hope that the resulting explanations will be mean...
3 CitationsSource
#1Noel C. F. CodellaH-Index: 18
#2Michael Hind (IBM)H-Index: 25
Last. Aleksandra MojsilovicH-Index: 24
view all 8 authors...
Using machine learning in high-stakes applications often requires predictions to be accompanied by explanations comprehensible to the domain user, who has ultimate responsibility for decisions and outcomes. Recently, a new framework for providing explanations, called TED, has been proposed to provide meaningful explanations for predictions. This framework augments training data to include explanations elicited from domain users, in addition to features and labels. This approach ensures that expl...
#1Vijay AryaH-Index: 1
Last. Yunfeng ZhangH-Index: 19
view all 20 authors...
As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, present different requirements for explanations. Toward addressing these needs, we introduce AI Explainability 360 (this http URL), an open-source software toolkit featuring eig...
4 Citations
#1Noel C. F. Codella (IBM)H-Index: 18
#2Chung-Ching Lin (IBM)H-Index: 7
Last. J. Mylroie-Smith (IBM)H-Index: 139
view all 6 authors...
Automated dermoscopic image analysis has witnessed rapid growth in diagnostic performance. Yet adoption faces resistance, in part, because no evidence is provided to support decisions. In this work, an approach for evidence-based classification is presented. A feature embedding is learned with CNNs, triplet-loss, and global average pooling, and used to classify via kNN search. Evidence is provided as both the discovered neighbors, as well as localized image regions most relevant to measuring dis...
5 CitationsSource