Artificial Intelligence has come a long way in the last couple of years. The development of newer, more powerful AI models, generative AI applications, and LLMs has revolutionized virtually every business. Today’s intelligent systems support countless use cases, from helping businesses deliver exceptional customer service, to creating unique content.
For eComms surveillance, given the rapid growth in electronic communications and the increase in number of channels on which we communicate, "AI based monitoring is no longer a nice to have but a must have," says Chris Stapenhurst from Veritas, a leading eComms surveillance provider.
However, rapid worldwide adoption of generative AI has also raised various concerns related to accuracy, bias, intellectual property protection, data privacy, and exploitation. As a result, various governments and regulatory bodies have begun introducing new governance guidelines for AI usage.
Staying ahead of these regulations, earning consumer trust, and protecting data requires companies to take a new approach to data governance, and surveillance.
Governing AI Solutions: The Evolving Regulations
There’s no doubt that AI, and generative AI in particular, can deliver countless benefits to businesses. In the communications and analytics space, for instance, GenAI can automate the generation of reports, summarize huge volumes of data, and assist businesses in making informed decisions.
Leading solutions can leverage advanced algorithms and machine learning models to optimize supply chains, predict customer behavior, and even accelerate the development of new products and solutions. Plus, generative AI’s ability to analyze data rapidly and search for relevant patterns can even help businesses fight back against security issues and fraud.
However, generative AI suffers from a range of issues. It can confidently deliver incorrect answers to queries, demonstrate bias when it’s trained on incomplete data, and be used by malicious actors for data theft and other nefarious purposes.
As a result, regulators like FINRA, as well as groups like the Biden administration in the US, and the European Union, are introducing new guidelines for safe generative AI use.
AI Safety Basics: The Core Concepts of Governing AI
Though there’s no globally approved set of governance guidelines for generative AI yet, the US Executive Order Of AI Safety, the EU AI Act, and even FINRA’s regulations focus on a few overlapping areas. Companies embracing AI will need to ensure they’re prioritizing:
AI Transparency and Reliability
The fight against “black box AI” is growing. Regulators want organizations to show they understand how models arrive at specific conclusions. This means leveraging clear documentation and explanations of how models work.
Additionally, many governance guidelines suggest that AI models should be able to consistently produce dependable, accurate results. Leading surveillance tools, such as those offered by Veritas, can assist companies in maintaining the transparency and reliability of their models.
With end-to-end insights into how AI systems perform, companies can more rapidly identify potential issues where AI systems make errors in their responses. They can then dive into the AI model they’re using, to identify how those systems came up with those responses, and optimize algorithms.
"The solution must be able to demonstrate how the AI is coming up with its determinations. Failure do so, leads can lead to hallucinations, bias, security, privacy and other compliance breaches as well as limit our ability to defend AI’s use in the first place," continues Stapenhurst.
Bias Mitigation
Bias is a common issue for generative AI models. While these systems aren’t inherently biased, training with the wrong data can cause them to arrive at biased conclusions. Overcoming the issue of bias in AI models requires a comprehensive approach. Throughout the AI lifecycle, from the point of data collection, through to pre-processing and model development, companies will need to implement testing strategies, data governance efforts, and regular audits.
Ensuring models are trained with comprehensive, accurate, and high-quality data sets is a good first step. With the right data governance strategy, companies can implement data management practices to help ensure data is accurate, consistent, and compliant.
Monitoring AI conversations, with an end-to-end surveillance tool, will allow organizations to identify instances of bias, and determine how to train and fine-tune systems to eliminate these issues.
