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:
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.
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.
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.
Karol Przystalski | CTO | Codete
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.
David Talby, PhD | CTO | John Snow Labs
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.
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.
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.
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?”
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.