Why AI Governance must go above and beyond auditing machine learning pipelines

The fast speed of artificial intelligence reception in business could be setting out toward some significant hindrances. AI administration and how others will screen and control the utilization of information in their AI stages are arising as critical obstacles. AI administration is a somewhat new idea, as computer-based intelligence itself is still just in the beginning phases of improvement, yet there are complexities arising which must be identified and governed with urgency. 

Within current tech silos, typical AI pipelines involve in following development and governance steps: 

  1. Model development and training  
  2. Model assessment with built- in fairness modules 
  3. ML model deployment 
  4. Model monitoring 

For AI teams to create impactful models, a deeper analysis of the pipeline is required. The metrics used by AI developers do not show the full picture and only capture short-term trends. Traditional AI metrics are Accuracy, Loss, Confusion Matrix, AUC (Area Under ROC curve), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R Square. Looking at these metrics, AI practitioners might deploy the model to production; oblivious to the question whether this model will generalize well in the real world. What companies miss is the conceptual correctness of their AI model. An AI using racial background and appearance to predict the likelihood of a person committing crime is bizarre, unacceptable, and reputationally disastrous.  

In a study published in the journal Science in 2019 found that AI insights from Optum, a digital healthcare company were prompting medical professionals to pay more attention to white people than to black people to spot high-risk patients. Here comes the necessity to rope in human experts to review, validate, audit and build confidence in your AI use-cases. What needs to be highlighted is that for so many, this validation expertise needs to be outsourced to an independent AI Governance organization. An independent organization brings in the “one standard for all” approach along with the dense data-driven insights. Getting outsourced expertise also demystifies AI from the tech developers and brings forward ownership from business and compliance perspectives. Explainability, and fairness can be effective and attainable if proper context and knowledge is available. However, the expertise which must be acquired makes it impossible for small or ill-informed companies to audit their own model.  

This is what we do at anch.AI, we provide a one stop solution to fix your AI use-case and make it market ready. We provide personalized recommendations and steps to accelerate the acceptance of your use-case in the market. The tech innovation side is already developed matured beyond – to the point that organizations are overwhelmed and confused by the jungle and tangled web of AI governance tools. The anch.AI user journey and workflow is designed to guide the user to the appropriate actions, consolidating the risk management and AI governance requirements. https://www.brandeis.edu/teaching//chatgpt-ai/ethical-concerns.html, https://www.wired.com/story/bias-statistics-artificial-intelligence-healthcare/