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Borealis AI Launches RESPECT AI Program to Bring Ethical and Responsible AI to All

New survey shows that businesses want to practice more ethical AI, but face barriers in doing so

A new survey has found that, while the majority of Canadian businesses believe it is important to implement artificial intelligence (AI) in an ethical and responsible way, 93 per cent experience barriers in doing so, citing cost, time and lack of understanding as the main issues.

As part of its commitment to advancing the development of responsible AI and Machine Learning (ML), Borealis AI has developed RESPECT AI, a new online hub that brings open source research code, tutorials, academic research and lectures to the AI community, helping to make ethical AI available to all.

“Responsible and safe AI is critical to maintaining trust and accountability, but as this new survey shows, many companies and developers do not have the resources to implement AI safely and ethically,” explains Dr. Foteini Agrafioti, Head of Borealis AI and RBC’s Chief Science Officer. “RESPECT AITM will help enable secure, fair, ethical and trusted AI products and a more responsible adoption of AI technology across industries.”

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According to the survey, conducted on behalf of RBC by Maru/Matchbox, 77 per cent of those 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 53 per cent have someone responsible for ethical development of data and AI technology. Only 23 per cent of businesses are able to fully explain the decisions and actions taken by their AI models.

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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 (44 per cent) do not understand the challenges that bias presents in AI.

“At RBC, we see a world where every client interaction and business decision is informed by AI. Because our relationship with our clients is built on a foundation of trust, practicing ethical and responsible AI is not an option – it’s the only way we do business,” says Bruce Ross, Group Head, Technology & Operations, RBC. “RESPECT AITM is proof of our commitment to building a healthy technology ecosystem within and beyond financial services.”

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RESPECT AITM will focus on five critical areas that contribute to responsible AI, and include resources in the following areas:

  • Robustness: The ability of an AI system to defend against adversarial attacks. This component of RESPECT AITM includes Advertorch, Borealis AI’s well-established adversarial robustness research code, with more than 1,000 downloads per month, which implements a series of attack and defense 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.
  • Data Privacy: Maintaining trust and integrity while leveraging large data sets is an essential component of responsible AI. This section includes scientific publications and research code on Private Synthetic Data Generation, 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.
  • Fairness: The ability to mitigate the challenge of bias in AI models. This area includes technical tutorials on bias as well as guidance for organizations to address bias.
  • Model Governance: Stakeholders need to trust that models are safe, compliant and robust. This speaks to ensuring accountability and reliability in AI, which is critical to the successful application of AI models. This section includes expert interviews and guidance for businesses and developers navigating this complex area.
  • Explainability: An understanding of how the ML algorithm learns and makes decisions. Our program includes technical articles and business blogs outlining how explainability can help build trust in AI.

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