Awareness of biases increases productivity in AI workflows.
With upfront effort to address biases in your data, these biases can either be mitigated before an application goes into production, reducing problematic and costly errors, or a company can ensure that there is appropriate oversight and human intervention where known biases exist. In the long run, this upfront effort to address model biases will lead to better and more scalable performance while reducing the need to sift through the outputs of your model.
There are compounding effects to programmatic bias detection and strong data governance policies, which allow companies to build more confidently across all use cases.
For any company looking to integrate AI applications across their business, it is wise to make these upfront investments, as errors, biases, and poor data governance and tracking can propagate over time and eventually become unmanageable.
We’re reaching a point with our AI models where decisions are being made that impact everything from media consumption to business strategy and, most certainly, public policy.
Transparency and Verifiability in AI Models
Currently, several debates are taking place at the US State level that have the power to impact the development of AI.
While these discussions will take several twists and turns, one thing is crystal clear: the only way for an AI model to be successful is if its output is transparent, verifiable, and grounded in reliable data across use cases. Without this, we risk deploying black-box systems that only reinforce bias, spread misinformation, and generate non-factual outputs.
Transparency starts with the data on which an AI application is trained and the data made available to that AI application, in use cases such as RAG.
It is critical that when building an AI application, people have an understanding of where the data comes from, where the data may be skewed, and how the AI application will ultimately be leveraged. Companies must also have processes in place to track where their data is being used so they can update AI applications if their data becomes inaccurate or irrelevant over time.
A comparable methodology would be how teachers are taught to teach by other teachers and professors. It is impossible to eliminate all lived experiences from translating to new teachers, and some educators might absorb their professors’ own biases about how a classroom should be run. Teachers can change their way of thinking based on new experiences, while an AI model can be updated by new data.
Understanding the Cost of Ineffective Transparency
A lack of transparency about the origins of an AI model leads to more issues in the long run. If users are unsure of the biases the model was trained on, they will end up with unreliable information and spend more time checking or correcting the outputs rather than using them to speed up functionality. Without the initial transparency and governance, you might end up with a company that spends more time serving as an AI babysitter rather than growing revenue.
It’s important to develop your model carefully from the beginning because it will increase productivity down the road. Enterprises must also ensure that the data sets used to train their models are publicly available and eligible – a task that requires thoroughness and tenacity but will pay off in the future.
Though biases and hallucinations – mistakes that an AI model makes – are generally depicted as flaws, models trained on small data sets are not useless. Small enterprises have smaller data sets, which can be used to fine-tune a model for a specific function, leading to enterprise-level performance at a much lower cost than a generalist model.
These small, fine-tuned models lower the costs of AI adoption – and, therefore, make the playing field more level.
With the Right Data, Small Models Can Have a Big Impact
Easy-to-use tools can make model fine-tuning fast, simple, and affordable. Increased adoption of high-performing AI applications by businesses of all sizes is similar to what we saw with the Internet and retail websites around 2000. Initially, only the largest retailers could afford to set up online stores. Over time, the cost of development and operations fell so sharply that practically every store of every size now has some online presence.
Soon, every enterprise will have AI automating part of its business.
Transparency in an AI model’s development shouldn’t be a chore but a goal and asset. Enterprises need to assess AI’s role in their organization, ensure that humans are heavily involved in its development and usage, and familiarize themselves with data governance policies.
Enterprises will not get what they want out of models trained on unfamiliar data; if they do, the benefits may be short-lived.
The future of AI depends on models that don’t just generate content—but validate, verify, and justify their reasoning. Like people, AI models must collect information from various sources to produce well-thought-out solutions and answers. We must pay careful attention to data governance and the origins of AI models.
