Schedule: Ethics, Privacy, and Security sessions

Purpose, a well-defined problem, and trust from people are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment. Read more.

Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making. Read more.

Simon Crosby details an architecture for learning on time series data using edge devices, based on the distributed actor model. This approach flies in the face of the traditional wisdom of cloud-based, big-data solutions to ML problems. You'll see that there are more than enough resources at “the edge” to cost-effectively analyze, learn from, and predict from streaming data on the fly. Read more.

As AI grows its reach throughout society and makes decisions that affect people, any business looking to capitalize on AI’s potential must also acknowledge its impact. Kishore Durg and Teresa Escrig explain why businesses must teach and test their AI systems to act as responsible representatives so that they reflect business and societal norms of responsibility, fairness, and transparency. Read more.

Data-driven companies making intelligent products must design for security and privacy to be competitive globally. Amanda Casari details the high-level changes that EU General Data Protection Regulation (GDPR)-compliant businesses face and how this translates to teams designing products driven by machine learning and artificial intelligence. Read more.

The public dialogue around artificial intelligence takes place at extremes, with some focusing on how robots will take everyone’s job and others speaking of a utopia where AI will do everything from cure cancer to solve world hunger. Irmak Sirer offers an alternative to these extreme views of AI: instead of artificial intelligence, we instead must focus on augmenting people’s intelligence. Read more.

David Martinez discusses an AI canonical architecture suitable for a number of different classes of applications and shares examples focused on cybersecurity to illustrate an application area that benefits from an end-to-end AI architecture. Read more.

Allison Duettmann offers an overview of AI philosophy and explains why traditional approaches need updating, because they pave the way for a singleton AI that will hardly be benevolent. Allison then discusses potential alternative AI safety strategies and their shortcomings and shares a brief survey of interesting problems in AI safety and what we can hope for if we get it right. Read more.

What does it mean to explain a machine learning model, and why is it important? Armen Donigian addresses those questions while discussing several modern explainability methods, including traditional feature contributions, LIME, and DeepLift. Each of these techniques offers a different perspective, and their clever application can reveal new insights and solve business requirements. Read more.

Susan Etlinger explores how AI fundamentally changes the relationship between people and businesses, lays out its risks and opportunities, and demonstrates emerging best practices for designing customer-centric and ethical products and services. Read more.

In recent years, there's been a quantum leap in the performance of AI, as deep learning made its mark in areas from speech recognition to machine translation and computer vision. However, as artificial intelligence becomes increasingly popular, data privacy issues also gain traction. Yishay Carmiel reviews these issues and explains how they impact the future of deep learning development. Read more.

Jake Porway sheds light on AI’s true potential to impact the world in a positive way. Drawing on his experience as the head of DataKind, an organization applying AI for social good, Jake shares best practices, discusses the importance of using human-centered design principles, and addresses ethical concerns and challenges encountered when using AI to tackle complex humanitarian issues. Read more.

Machine learning algorithms are being developed and improved at an incredible rate but are not necessarily accessible to the broader community. A. Besir Kurtulmus offers an overview of DanKu, a new blockchain-based protocol for evaluating and purchasing ML models on a public blockchain such as Ethereum that provides everyone access to high-quality, objectively measured machine learning models. Read more.

AI-powered chatbots are increasingly becoming viable solutions for customer service use cases. Technology leaders must consider adopting a multichannel chatbot strategy to avoid siloed chatbot solutions. Sharad Gupta shares a framework to ensure long-term strategic investment in chatbots. Read more.

AI possesses an incredible potential to help address the challenges of our planet. Drawing on her experience as the head of AI foundations and codirector of Science for Social Good at IBM Research, Aleksandra Mojsilovic shares innovative examples of applying AI to humanitarian problems and discusses gaps that challenge us from making larger impact with our work. Read more.

Machine learning is eating software. As decisions are automated, model interpretability must become an integral part of the ML pipeline rather than an afterthought. In the real world, the demand for being able to explain a model is rapidly gaining on model accuracy. Mayukh Bhaowal discusses the steps Salesforce Einstein is taking to make machine learning more transparent and less of a black box. Read more.

Lydia Liu discusses the results of research on how static fairness criteria interact with temporal indicators of well-being. These results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria and suggest a range of new challenges and trade-offs. Read more.