The rapid adoption of AI technologies around the world has given rise to an equally urgent and concurrent sprint by governing bodies to regulate their use. While good steps in the right direction, these initiatives are largely overdue.
AI technology is not new. Our firm, Converseon, built its first machine learning model in 2008, and for over a decade we’ve advocated for effective and trusted applied AI approaches and standards in the area of classification of unstructured text data. During this period, the demand for “trusted AI” has been largely uneven. Today, however, with greater awareness of these immense powers — and risks — of this technology, “trusted AI” standards are rapidly becoming must haves.
While current concerns about AI are focused on higher risk use cases, the social, VoC and media intelligence space will not remain immune for long.
A core, fundamental goal of regulatory efforts is to eliminate AI bias, increase accuracy and build trust in the systems. This means higher data standards, greater transparency and documentation of AI systems, auditing of its functions (and model performance) and enabling human oversight and ongoing monitoring. Indeed, even without forced regulation, these processes represent important and critical best practices that warrant immediate adoption. In the near future, it is likely that almost every leading organization will have a form of AI policy in place that will adhere closely to these standards.
The EU’s AI Act is the first major proposed AI law and generally represents an evolved and thoughtful approach. It is also likely a harbinger of what’s to come globally. It classifies use cases from “unacceptable” to “high risk” to “low risk.” On a cursory review, while many of the communication industry’s use cases may appear “low risk” from a regulatory standpoint, there is significant risk to the brand itself in making business decisions based on poor quality data and analysis.
The crux of the act is to build trust in the system by requiring strong governance, accuracy and transparency and the elimination of potential bias and harmful impact through human-in-the-loop intervention. Model training has an important and prominent focus. It states:
“High data quality is essential for the performance of many AI systems, especially when techniques involving the training of models are used, with a view to ensure that the high-risk AI system performs as intended and safely and it does not become the source of discrimination prohibited by Union law. High quality training, validation and testing data sets require the implementation of appropriate data governance and management practices ….”
Yet the reality is that the implementation of AI at the media and social platforms, “AI solution,” PR and analytics departments unfortunately broadly falls dangerously short of these standards and has contributed to a lack of confidence in this data and technology.
While the application of some level of AI is pervasive across most social, media and CX platforms, it is highly uneven. According to the Social Intelligence Lab’s 2023 “State of Social Listening” study, data accuracy remains one of the industry’s biggest complaints. In many systems there is negligible human-in-loop oversight or ability to fine tune or modify models. One-size-fits-all model classification generally takes precedence over more accurate domain specific ones.
Poor quality sentiment and opaque systems have created skepticism about the resulting data and insights and has helped contribute to an overall “trust deficit” of measurement and insights that has stifled broader adoption.
Model performance and auditing is mostly opaque or “one off” — if available at all (in most cases it is not). Further, the training processes and data used are most often black box and mostly unavailable to users of the technology (eliminating bias in model training can be a complex task that requires sophisticated end to end processes).
If asked, most users simply do not know the specific performance of their models and accuracy of their data classification, yet they often make business decisions based on this data. If probed on specifics, many providers of AI gloss over details of capabilities. Unclear marketing, promotional materials and other documentation often just muddy the picture.
This state of affairs is simply unsustainable in this new environment.
Aligning with these standards will help reverse these perceptions. But this will require all stakeholders to substantially elevate capabilities and demand.
To their credit, organizations such as the Institute of Public Relations (IPR), ESOMAR and AMEC ((International association for the measurement and evaluation of communication) are working to educate and generate some consensus for action, but those efforts remain mostly in the early stage and aspirational.
Here are some key questions and topics we recommend for consideration when evaluating AI vendors, drafting RFPs or participating in relevant industry groups:
· Conduct a current assessment: Does your team understand this technology well enough to effectively evaluate it and establish the right processes? Do you need to improve education, especially among key stakeholders? Are you asking vendors the right questions? Who are your current vendors and what is the state and quality of their trusted AI technologies and processes, if any? If none, what is their roadmap? How are your data and insights being used from social and media analysis? How does that align to high and low risk categories?
· Ensure accuracy and transparency: How can you know and verify this? Can you access and audit the training data models directly and see the precise performance of your model at any point in time? Is the model evaluation process comprehensive? Does it incorporate standard measures (F1, precision and recall) or more? How often is model performance assessed? And is there an available audit trail of the model performance over time? Can you access domain or industry specific models? And can your team participate in the fine tuning or are you stuck with a static one-size-fits-all model that doesn’t meet your requirements? Is model performance tracked and registered, or is it “train it and forget it”?
· Establish governance system: Can you or your organization provide input or changes to the system? Can you track and see the performance of all your models across the organization near real time? Is there an end-to-end system to build, fine tune, integrate, validate and deploy models efficiently? How does it work and how is it accessed? Is there a process for feedback and model optimization?
Is there a human-in-the-loop capability for oversight and intervention? How does it work? Can you have direct access? And if models do go off course, what processes are in place to help explain why and determine what corrective action to take?
The time for proactive action is now. While initial legislation is not focused squarely on this category, it does not mean the industry should not take aggressive steps to abide by the standards. Indeed, the opposite is true.
Action will not only front-end potential risk and help ensure compliance to internal AI alignment and policies; it will also generate significant positive impact in model effectiveness, leading to broader adoption and even predictive and prescriptive analytics that will better serve your organization and its key stakeholders. It will mean better and more impactful communication strategies. And as trust in the data and technology grows, the insights and solutions will continue to expand across essential areas ranging from corporate sustainability efforts to product innovation, brand reputation and customer experience.
Clearly, the payoffs of being a leader — and not a laggard — in trusted AI are simply too important for the industry at large to wait any longer.
Rob Key is founder and CEO of Converseon, the leading AI powered NLP technology and consulting firm. Its core product, Conversus, provides a full suite of trusted AI features for classification of unstructured text data directly to leading brands around the world and through partnerships with other key platforms in the social, media and VoC industries.