14–17 Oct 2019

Schedule: Ethics, Security, and Privacy sessions

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11:0511:45 Wednesday, 16 October 2019
Location: Westminster Suite
Adithya Hrushikesh (Vodafone)
Average rating: ****.
(4.00, 4 ratings)
Every day, millions of Vodafone Germany customers reach out through various social media channels about issues related to mobile, internet, signal issues, etc. Adithya Hrushikesh details how to build and deploy an ensemble model to classify 26 (originally 56) complaint classes using machine learning over deep learning. He also touches on the business case, data product development, and GDPR. Read more.
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11:0511:45 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Alex Ingerman (Google)
Average rating: ****.
(4.29, 7 ratings)
Federated learning is the approach of training ML models across many devices without collecting the data in a central location. Alex Ingerman explores learning concepts and the use cases for decentralized machine learning, drawing on Google's real-world deployments. You'll learn how to build your first federated models with the open source TensorFlow Federated. Read more.
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11:5512:35 Wednesday, 16 October 2019
Location: Westminster Suite
Martin Benson (Jaywing)
Machine learning has been used in credit scoring for three decades. Martin Benson discusses the history of machine learning in credit scoring and the need for explainable and justified decisions made by machine learning systems. Come find out if it's possible to overcome the black box problem and learn how machine learning systems are evolving and how to bypass the challenges to adoption. Read more.
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11:5512:35 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Chang Liu (Georgian Partners ), Ji Chao Zhang (Georgian Partners)
Average rating: ****.
(4.33, 3 ratings)
The world is increasingly data driven, and people have developed an awareness and concern for their data. Chang Liu and Ji Chao Zhang examine differential privacy—the component of the TensorFlow Privacy library that allows users to train differentially private logistic regression and support vector machines—along with real-world use cases and demonstrations for how to apply the tools. Read more.
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13:4514:25 Wednesday, 16 October 2019
Location: Westminster Suite
Martin Goodson (Evolution AI)
Average rating: ****.
(4.75, 4 ratings)
Data leakage occurs when the model gains access to data that it shouldn't have. AI systems can fail catastrophically in production if leakage is not dealt with properly. Martin Goodson details the four main manifestations of data leakage and explains how to recognize the warning signs. By mastering several key scientific principles, you can mitigate the risk of failure. Read more.
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13:4514:25 Wednesday, 16 October 2019
Location: Windsor Suite
Average rating: *****
(5.00, 3 ratings)
In the rapidly changing world of AI, adopting the right design principles is key. From data scientists and business users to client end users, IBM Watson always seeks to augment their capabilities. Ariadna Font Llitjós examines how IBM Watson applies ethical AI and user-centered design principles from the beginning and leverages them throughout the product development cycle. Read more.
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16:0016:40 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Rajib Biswas (Ericsson)
Average rating: ****.
(4.00, 2 ratings)
Rajib Biswas outlines the application of AI algorithms like generative adversarial networks (GANs) to solve natural language synthesis tasks. Join in to learn how AI can accomplish complex tasks like machine translation, write poetry with style, read a novel, and answer your questions. Read more.
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16:0016:40 Wednesday, 16 October 2019
Location: Westminster Suite
Danielle Deibler (MarvelousAI)
Average rating: ****.
(4.57, 7 ratings)
Danielle Deibler examines an approach to detecting bias, fine-grained emotional sentiment, and misinformation through the detection of political narratives in online media. As building blocks, the methodology uses human-in-the-loop, alongside other natural language processing and computational linguistics techniques, with examples focused on the 2020 US presidential election. Read more.
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9:5510:10 Thursday, 17 October 2019
Location: King's Suite
Marta Kwiatkowska (University of Oxford)
Average rating: ****.
(4.79, 19 ratings)
Machine learning solutions are revolutionizing AI, but Marta Kwiatkowska explores their instability against adversarial examples—small perturbations to inputs that can catastrophically affect the output—which raises concerns about the readiness of this technology for widespread deployment. Read more.
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11:0511:45 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Zaid Tashman (Accenture Labs)
Average rating: *****
(5.00, 3 ratings)
Today traditional approaches to predictive maintenance fall short. Zaid Tashman dives into a novel approach to predict rare events using a probabilistic model, the mixed membership hidden Markov model, highlighting the model's interpretability, its ability to incorporate expert knowledge, and how the model was used to predict the failure of water pumps in developing countries. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: Windsor Suite
Katharine Jarmul (KIProtect)
Average rating: *****
(5.00, 2 ratings)
Katharine Jarmul sates your curiosity about how far we've come in implementing privacy within machine learning systems. She dives into recent advances in privacy measurements and explains how this changed the approach of privacy in machine learning. You'll discover new techniques including differentially private data collection, federated learning, and homomorphic techniques. Read more.
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14:3515:15 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Average rating: ****.
(4.00, 4 ratings)
Alejandro Saucedo demystifies AI explainability through a hands-on case study, where the objective is to automate a loan-approval process by building and evaluating a deep learning model. He introduces motivations through the practical risks that arise with undesired bias and black box models and shows you how to tackle these challenges using tools from the latest research and domain knowledge. Read more.
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14:3515:15 Thursday, 17 October 2019
Location: King's Suite - Balmoral
Ilya Feige (Faculty)
Average rating: *****
(5.00, 2 ratings)
Ilya Feige explores AI safety concerns—explainability, fairness, and robustness—relevant for machine learning (ML) models in use today. With concepts and examples, he demonstrates tools developed at Faculty to ensure black box algorithms make interpretable decisions, do not discriminate unfairly, and are robust to perturbed data. Read more.
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16:0016:40 Thursday, 17 October 2019
Location: Westminster Suite
Walter Riviera (Intel)
What are the essentials steps to take in order to develop an AI solution? How long would this process would take? As machine learning is teaching us, the answers can be learned from previous experience. Walter Riviera walks you through a collection of real-life stories, looking for successful and misleading behavioral patterns. Read more.
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16:0016:40 Thursday, 17 October 2019
Location: Blenheim Room - Palace Suite
Tuhin Sharma (Binaize), Bargava Subramanian (Binaize)
Average rating: ****.
(4.50, 2 ratings)
There's an exponential growth in the number of internet-enabled devices on modern smart buildings. IoT sensors measure temperature, lighting, IP camera, and more. Tuhin Sharma and Bargava Subramanian explain how they built anomaly-detection models using federated learning—which is privacy preserving and doesn't require data to be moved to the cloud—for data quality and cybersecurity. Read more.
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16:5017:30 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Tom Sabo (SAS)
Average rating: ****.
(4.50, 2 ratings)
Efforts to counter human trafficking internationally must assess data from a variety of sources to determine where best to devote limited resources. Tom Sabo explores text-based machine learning, rule-based text extraction to generate training data for modeling efforts, and interactive visualization to improve international trafficking response. Read more.

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