RESPECT AI™

Advancing responsible AI adoption

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RBC Responsible AI Principles

  • Blue icon of a security lock

    At RBC, we are committed to responsible data practices – from how we use data to how we protect it. We maintain data integrity and confidentiality through robust information security and data handling practices. 

    Borealis AI focuses on differential privacy in our research, and built Private Data Generation™ toolbox to offer synthetic ML data samples, a method that allows scientists to use large data sets without risking the exposure of personal identifiable information. This tool can be used by researchers to advance the field of AI privacy by proposing novel solutions to this critical issue. We have also published tutorials on differential privacy, among other topics.

    We have also built Advertorch™, a well-established adversarial robustness research code, which implements a series of attack and defence strategies that can be used to protect against risks. This tool is offered to AI researchers and scientists that aim to advance the field of robustness in machine learning.

In Numbers

92%

According to the survey, conducted on behalf of RBC by Maru/Matchbox, companies currently using AI/analytics agree it is important for businesses to implement AI in an ethical way. However, 92 per cent have concerns in dealing with the ethical challenges that AI represents, and just over half have someone responsible for ethical development of data and AI technology.

88%

The results of the survey also highlighted some significant challenges that businesses face in terms of bias such as race and gender. The vast majority (88 per cent) of companies believe they have bias within their organization, but almost half of them do not understand the challenges that bias presents in AI.

Tutorials & Research

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Open Source Tools 🧰

AdverTorch

This toolbox provides machine learning practitioners with the ability to generate private and synthetic data samples from real world data. It currently implements 5 state of the art generative models that can generate differentially private synthetic data.

GitHub

LiteTracer

LiteTracer acts as a drop-in replacement for argparse, and it can generate unique identifiers for experiments in addition to what argparse already does. Along with a reverse lookup tool, LiteTracer can trace-back the state of a project that generated any result tagged by the identifier.

GitHub

Private Synthetic Data Generation

This toolbox provides machine learning practitioners with the ability to generate private and synthetic data samples from real world data.It currently implements 5 state of the art generative models that can generate differentially private synthetic data.

GitHub