Artificial Intelligence (AI) has become intertwined in almost all facets of our daily lives, from personalized recommendations to critical decision-making. It is a given that AI will continue to advance, and with that, the threats associated with AI will also become more sophisticated. As businesses enact AI-enabled defenses in response to the growing complexity, the next step toward promoting an organization-wide culture of security is enhancing AI’s explainability.
While these systems offer impressive capabilities, they often function as “black boxes“—producing results without clear insight into how the model arrived at the conclusion it did. The issue of AI systems making false statements or taking false actions can cause significant issues and potential business disruptions. When companies make mistakes due to AI, their customers and consumers demand an explanation and soon after, a solution.
But what is to blame? Often, bad data is used for training. For example, most public GenAI technologies are trained on data that is available on the Internet, which is often unverified and inaccurate. While AI can generate fast responses, the accuracy of those responses depends on the quality of the data it’s trained on.
AI mistakes can occur in various instances, including script generation with incorrect commands and false security decisions, or shunning an employee from working on their business systems because of false accusations made by the AI system. All of which have the potential to cause significant business outages. This is just one of the many reasons why ensuring transparency is key to building trust in AI systems.
Building in Trust
We exist in a culture where we instill trust in all kinds of sources and information. But, at the same time, we demand proof and validation more and more, needing to constantly validate news, information, and claims. When it comes to AI, we are putting trust in a system that has the potential to be inaccurate. More importantly, it is impossible to know whether or not the actions AI systems take are accurate without any transparency into the basis on which decisions are made. What if your cyber AI system shuts down machines, but it made a mistake interpreting the signs? Without insight into what information led the system to make that decision, there is no way to know whether it made the right one.
While disruption to business is frustrating, one of the more significant concerns with AI use is data privacy. AI systems, like ChatGPT, are machine-learning models that source answers from the data it receives. Therefore, if users or developers accidentally provide sensitive information, the machine-learning model may use that data to generate responses to other users that reveal confidential information. These mistakes have the potential to severely disrupt a company’s efficiency, profitability, and most importantly customer trust. AI systems are meant to increase efficiency and ease processes, but in the case that constant validation is necessary because outputs cannot be trusted, organizations are not only wasting time but also opening the door to potential vulnerabilities.
Training Teams for Responsible AI Use
In order to protect organizations from the potential risks of AI use, IT professionals have the important responsibility of adequately training their colleagues to ensure that AI is being used responsibly. By doing this, they help to keep their organizations safe from cyberattacks that threaten their viability and profitability.
However, prior to training teams, IT leaders need to align internally to determine what AI systems will be a fit for their organization. Rushing into AI will only backfire later on, so instead, start small, focusing on the organization’s needs. Ensure that the standards and systems you select align with your organization’s current tech stack and company goals, and that the AI systems meet the same security standards as any other vendors you select would.
Once a system has been selected, IT professionals can then begin getting their teams exposure to these systems to ensure success. Start by using AI for small tasks and seeing where it performs well and where it does not, and learn what the potential dangers or validations are that need to be applied. Then introduce the use of AI to augment work, enabling faster self-service resolution, including the simple “how to” questions. From there, it can be taught how to put validations in place. This is valuable as we will begin to see more jobs become about putting boundary conditions and validations together, and even already seen in jobs like using AI to assist in writing software.
In addition to these actionable steps for training team members, initiating and encouraging discussions is also imperative. Encourage open, data driven, dialogue on how AI is serving the user needs – is it solving problems accurately and faster, are we driving productivity for both the company and end-user, is our customer NPS score increasing because of these AI driven tools? Be clear on the return on investment (ROI) and keep that front and center. Clear communication will allow awareness of responsible use to grow, and as team members get a better grasp on how the AI systems work, they are more likely to use them responsibly.
How to Achieve Transparency in AI
Although training teams and increasing awareness is important, to achieve transparency in AI it is vital that there is more context around the data that is being used to train the models, ensuring that only quality data is being used. Hopefully, there will eventually be a way to see how the system reasons so that we can fully trust it. But until then, we need systems that can work with validations and guardrails and prove that they adhere to them.
While full transparency will inevitably take time to achieve, the rapid growth of AI and its usage make it necessary to work quickly. As AI models continue to increase in complexity, they have the power to make a large difference to humanity, but the consequences of their errors also grow. As a result, understanding how these systems arrive at their decisions is extremely valuable and necessary to remain effective and trustworthy. By focusing on transparent AI systems, we can ensure that the technology is as useful as it is meant to be while remaining unbiased, ethical, efficient, and accurate.
The post Opening the Black Box on AI Explainability appeared first on Unite.AI.
