Data Democratization and Governance for Responsible AI
Empowerment without defined responsibility and accountability has got no meaning. The potential of data is limitless. When it comes to making AI (Artificial Intelligence or Augmented Intelligence) responsible, explainable and trustworthy, data democratization and governance will need to be discussed in parallel as they are the two sides of the same coin. Explainable AI is also very important to understand and interpret the predictions and how to further improve the predictions to ensure better decision-making and to balance it with risk and accuracy.
Essentially, data democratization is around the easy accessibility of digital data and information to the average end-user. But to manage its accessibility, usability and protection, data governance procedures are required to be implemented as it ensures that data is used in the right way, by the right user and at the right time. It also brings the focus on responsibility and accountability in case something goes wrong.
Data democratization and governance also lays down the foundation for managing bias, potential risks, trust and transparency, and accuracy issues in AI. It is crucial that data or information access is accompanied by a supervisory and governance framework to ensure that the information is used in compliance with operational and regulatory controls and to keep the information reliable and up to date. It needs a simple way for individuals to interpret and appreciate the information so that they can use it to speed up the decision-making process and uncover growth opportunities.
The social impact or the human augmentation is one such area that is gaining greater attention when the transformative journey of AI and its implications are analyzed with the evolution and changes to the human genome. The businesses and government/federal social responsibility cannot be shifted to an artificial system just because it is a self-learning system and evolving based on the training data it receives from the outside world or generates on its own through reinforcement learning.
No doubt, the lack of availability of required data cannot make the model robust and bias-free; but the user/business policies for the data usage must be established along with the debiasing techniques for low-frequency and high-frequency decisions. It is also very important to establish the evidence in order to confirm the benefits realized so that it can be improved further as the AI or ML (Machine Learning) model becomes more mature.
AI Fairness 360 is one such open-source metrics toolkit to search and minimize the unnecessary bias in datasets and machine learning models.
It checks data and model bias at three different stages- training data level, an algorithm that generates the classifier and at testing and deployment stage when the prediction is made. It continuously learns through the feedback to improve the model further, thus, making the AI system more explainable.
