Learn About Responsible AI at ODSC Europe with These Sessions

Responsible AI – or Ethical AI – is a hot topic lately. When many organizations are using machine learning and artificial intelligence for their daily tasks and backend processes, there’s an air of mystery surrounding it. What tools are they using? Where did they get the data they’re using? Is there any bias? And so on. At ODSC Europe 2021, we have a focus area devoted to understanding ethical and responsible AI, and these sessions below will help educate anyone on the field as a whole, and how they can make their AI more responsible and ethical.

ODSC Europe 2021 Responsible AI Talks:

Track Keynote: Accountable AI in Europe: Are We Ready for the Artificial Intelligence Act?

Sandra Wachter, PhD | Associate Professor and Senior Research Fellow, Law and Ethics of AI | Oxford Internet Institute, University of Oxford

The EU Commission recently published the Artificial Intelligence Act – the world’s first comprehensive framework to regulate AI. The new proposal has several provisions that require bias testing and monitoring. But is Europe ready for this task? Sandra will talk about how the normative idea and aim behind EU non-discrimination law is “substantive equality” and so to actively dismantle inequality.

Responsible Data Science Using Bias-Dashboards

de Volksbank team

Recently, academics, as well as policymakers, have written many papers, on responsible AI/data science. Moreover, many open-source packages for bias dashboards or tools for `fairness’ have been proposed. This session aims to provide attendees a broad overview as well as the specific technical background to use the available ` fairness’ tools. In addition, a governance framework describing the precise responsibilities of data scientists will be discussed.

Error Analysis for Accelerating Responsible Machine Learning

Besmira Nushi, PhD | Senior Researcher, Adaptive Systems and Interaction, Mehrnoosh Sameki, PhD | Senior Technical Program Manager | Microsoft

This presentation will introduce Error Analysis to the audience, as a tool and methodology for effectively identifying and diagnosing errors in machine learning models, beyond aggregated accuracy scores. The tool provides different views for quick error identification and enables error diagnosis either via active data exploration or model explanations generated using the InterpretML library.

Explainable Artificial Intelligence Explained

Karol Przystalski | CTO | Codete

See his preview blog here.

This talk is dedicated to managers, developers, and data scientists that want to learn how to interpret the decisions made by machine learning models. We explain the difference between white and black box models, the taxonomy of explainable models, and approaches to XAI. Knowing XAI methods is especially useful in any regulated company.

Model Governance: A Checklist for Getting AI Safely to Production

David Talby, PhD | CTO | John Snow Labs

See his preview blog here.

As an industry, we have about forty years of experience forming best practices and tools for storing, versioning, collaborating, securing, testing, and building software source code – but only about four years doing so for AI models. This talk will catch you up on current best practices and freely available tools so that your team can go beyond experimentation to successfully deploy models.

The Missing Link: How AI Can Help Create a Safer Society and Better Businesses

Kamila Hankiewicz | Managing Director | Untrite

In this talk, Kamila will share the specific formula that governments and companies can adopt to successfully employ responsible AI while keeping humans in the loop making them more efficient, productive, and happy.

Machine Learning for Planetary Health: Challenges, Opportunities, and Doing Our Bit

Sara Khalid | Senior Research Associate in Biomedical Data Science and University Research Lecturer | University of Oxford

This talk will introduce the audience to planetary health and some of the most pressing issues facing us (and our planet), cover a review of the state-of-the-art in artificial intelligence and data science methods in planetary health informatics, and present a summary of the latest research.

Mastering Responsible Machine Learning in an Open World

Tamara Fischer | Principal Data Scientist, and Matteo Landrò | Data Scientist | SAS

In this climate of increasing AI adoption, ethics has become a critical concern among consumers, auditors, and governments. We observe a move from “How can Machine Learning help my business?” to “How can I design and deploy Machine Learning solutions at scale and responsibility?”

Benefits of Convolutional Neural Network for Healthcare Shortage Classification in Underserved Community

Ayemya Moe | Marketing Operations Lead | Project Management Institute

In addition to Convolutional Neural Network (CNN) applications in the computer vision field, the goal of this presentation is to show how to utilize CNN’s ability to better classify and capture the underlying structures, relationships, and abnormalities on tabular datasets to both complement the performance of machine learning (ML) models and save time plus resources in feature engineering.

Register now and see all ODSC Europe Responsible AI Talks

These are just a few of the training sessions, workshops, and talks on ethical and responsible AI that will be featured at ODSC Europe next month. Register now and gain access to all of the best ODSC Europe responsible AI talks live and on-demand following the event. And remember – the General Pass is now free so you can see all talks for free. Training and workshops will require the All-Access Pass.