The Importance Of Ai Trism and the Three Steps To Implement It

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With about 42 percent of new patents in 2018 featuring Artificial Intelligence, it’s clear that this technology will be shaping our future. But if these new systems are revolutionizing our world, what additional challenges might they bring to the table?

One prominent challenge is bias. Typically data bias is caused by a skewed, incomplete or non-representative data set. If AI systems make important decisions related to things like hiring or medical diagnoses based on parameters including a person’s gender or race, our society could suffer major consequences. Through such biases, these technologies can easily exacerbate existing disparities in ways that run counter to our moral and legal systems.

Governments are already taking an increased interest in regulating AI, looking to support the technology’s potential while keeping it from running amok. While organizations must be aware of any AI regulation, they must also keep their AI systems secure and trustworthy.

Many are doing exactly that. In a September 2021 report, Gartner cited an emerging market aimed at developing trustworthy and secure AI technology — a category the research organization referred to as AI Trust, Risk and Security Management (AI TRiSM).

The Importance of AI TRiSM

While regulatory structures for AI are still in development, organizations still have a strong incentive to embrace AI trust management.

AI is still a new technology and as such, consumers’ AI understanding and trust level is still relatively low. However, organizations using it can take steps to boost consumers’ AI trust level. A recent survey of AI technologists by Deloitte identified several key customer concerns related to AI technologies:

  • Pre-existing bias. Some customers are simply biased against the technology at this point in its evolution.
  • Insufficient oversight. Customers are worried about a lack of human oversight for AI systems.
  • Unexpected behavior. Perhaps influenced by popular science fiction movies, customers are concerned about AI systems “going rogue.”
  • Lack of understanding. Many customers don’t understand how AI works and fear the unknown.

Organizations can help to address these customer concerns by embracing AI TRiSM principles. This approach to AI trust management, when done correctly, can make systems less risky and more transparent; addressing major concerns. The ultimate goal of AI TRiSM is to keep customers secure while still allowing for growth and innovation.

Three Key Steps to Implementing AI TRiSM

Organizations looking to implement AI TRiSM should be considering a comprehensive, multifaceted framework. Very generally speaking, this framework should be driven by three key steps related to documentation, a system of checks, and a high degree of transparency around the technology.

  1. Implement Strong Documentation and Standard Procedures. Having a strong documentation system not only supports trustworthiness by placing a focus on the data used to train an AI system, but it also enables auditing of the technology in the event that something goes wrong. Documentation systems should be based on both legal guidelines and internal risk assessments. These systems should include both standardized documentation processes and document templates. Additionally, a documentation system should be both consistent and intuitive so that it supports both AI TRiSM and use of the technology.
  2. Use a System of Checks and Balances. Organizations must have systems in place designed to monitor potential bias and prevent a corrupted system from causing serious damage. For example, automated features in a documentation system can raise red flags if records in a data set are incomplete, missing, or highly anomalous.
  3. Prioritize AI Transparency. The Deloitte survey revealed how a lack of consumer trust in AI technology stems from a lack of understanding. Many consumers see AI decision-making as taking place within an indecipherable black box. Organizations can address AI trust and transparency by making it easy for non-technical consumers to see how data is collected and how the system makes decisions based on that data.

Supporting Trustworthy AI with TripleBlind

Trustworthy AI systems require access to large datasets. When a system is trained on a dependable and comprehensive data set, it is far less likely to be flawed or biased. Unfortunately, significant amounts of valuable data are trapped behind regulatory barriers and data silos.

Our highly innovative TripleBlind Solution supports the development of trustworthy AI by breaking down data silos and barriers. Through unprecedented privacy-enhancing technology, our clients are able to build AI systems they can confidently stand behind.

If you would like to learn more about how our technology supports AI TRiSM, check out our Blind AI Tools or download our Whitepaper. We remove common barriers to using high-quality data for artificial intelligence, solving key challenges AI professionals face with data access, bias, and prep. Through a combination of privacy-enhancing techniques, the TripleBlind Solution allows for training of new models on remote data –– without compromising the privacy or fidelity of sensitive data. Let us show you how by booking a live demo today.