Mar 15–18, 2020

An Overview of Responsible Artificial Intelligence

Mehrnoosh Sameki (MERS) (Microsoft), Sarah Bird (Microsoft)
9:00am12:30pm Monday, March 16, 2020
Location: LL21 E/F

Who is this presentation for?

Data scientists or analysts

Level

Beginner

Description

[Note: we are open to either options for the session type] Enabling responsible development of artificial intelligent technologies is one of the major challenges we face as the field moves from research to practice. Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learning in many current and future real-world applications. Now there are calls from across the industry (academia, government, and industry leaders) for technology creators to ensure that AI is used only in ways that benefit people and “to engineer responsibility into the very fabric of the technology.” Overcoming these challenges and enabling responsible development is essential to ensure a future where AI and machine learning can be widely used. In this talk we will cover six principles of development and deployment of trustworthy AI systems:  Four core principles of fairness, reliability/safety, privacy/security, and inclusiveness, underpinned by two foundational principles of transparency and accountability. We present on how each principle plays a key role in responsible AI and what it means to take these principles from theory to practice. We will cover open source products across different area of responsible AI umbrella, particularly transparency, fairness, and differential privacy, that aims to empower researchers, data scientists, and machine learning developers to take a significant step forward in this space, building trust between users and AI systems.

Prerequisite knowledge

Basic knowledge of machine learning life cycle

Materials or downloads needed in advance

NA

What you'll learn

Attendees will leave the session with the basic understanding of responsible AI principles, best practices and open source tools around responsible development and deployment of AI systems. They will be able to incorporate the introduced tools and products in their machine learning life cycle, running them on their previously-trained models to 1) understand the factors that went into their model predictions, 2) verify their model fairness across protected attributes and mitigate the existing bias, 3) ensure the privacy of user data.
Photo of Mehrnoosh Sameki (MERS)

Mehrnoosh Sameki (MERS)

Microsoft

Mehrnoosh Sameki is a technical program manager at Microsoft responsible for leading the product efforts on machine learning interpretability within the Azure Machine Learning platform. Previously, she was a data scientist at Rue Gilt Groupe, incorporating data science and machine learning in retail space to drive revenue and enhance personalized shopping experiences of customers. She earned her PhD degree in computer science at Boston University.

Photo of Sarah Bird

Sarah Bird

Microsoft

Sarah Bird is a principle program manager at Microsoft where she leads research and emerging technology strategy for Azure AI. Sarah works to accelerate the adoption and impact of AI by bringing together the latest innovations research with the best of open source and product expertise to create new tools and technologies. She leads the development of responsible AI tools in Azure Machine Learning. She’s also an active member of the Microsoft Aether committee, where she works to develop and drive company-wide adoption of responsible AI principles, best practices, and technologies. Previously, Sarah was one of the founding researchers in the Microsoft FATE research group and worked on AI fairness in Facebook. She’s an active contributor to the open source ecosystem; she cofounded ONNX, an open source standard for machine learning models and was a leader in the PyTorch 1.0 project. She was an early member of the machine learning systems research community and has been active in growing and forming the community. She cofounded the SysML research conference and the Learning Systems workshops. She holds a PhD in computer science from the University of California, Berkeley, advised by Dave Patterson, Krste Asanovic, and Burton Smith.

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