A Responsible Rise of Artificial Intelligence

“Ethics is knowing the difference between what you have a right to do and what is right to do”, these are the words of Potter Stewart, an American Lawyer and Judge. It is 100% true and makes more sense today as we are surrounded by technology and connected virtually with everything and everyone on the internet as a product through social media services in the age of Artificial Intelligence. 

As we have petabytes and zettabytes of information floating around and easily accessible, it makes it more important to have proper data handling principles and policies to be in place to make sure that the data is not driving the wrong decisions. As these data sets can be corrupted easily and still will have the power to influence decisions of algorithms based AI applications which can prove to be hazardous to 1 person or for all humans as a race and could negatively impact the environment as well with bad decisions. 

Below, I am providing some examples of how data driven automation can impact the processes if not handled properly and why there is a need for ethics, transparency, and traceability driven through Responsible AI in the field of Artificial Intelligence where we see a positive impact on the society, businesses, and environment through the implementation of policies, principals and governance across different industries.

In the Banking industry (in the recent past) we have seen that majority of the female credit card applicants either got rejected or chosen few were given fewer credit limits because the algorithms making the decisions in the backend were trained on biased data sets thus were, unemotionally, introducing BIAS. 

This has to go hand in hand with proper governance in IT and Business teams to make sure Accountable and Responsible stakeholders are identified to define the Responsible AI principles and accountable to make sure they are implemented properly supported with the actions or to-do steps to ensure recovery and damage control at both Organizational and Reputational level.

Apart from the bias in the data set, we have also seen that during any application or transactional data processing there is no transparency as to find out why this decision was taken, which parameter influenced it and why did the algorithm take additional steps to mitigate it? All these can be easily answered by embedding explainability and transparency in the AI design processes to provide the understandability of the context and interpretability of the decision by AI. This is a good opportunity to gain knowledge on AI by joining AI Course.

Thus we need Responsible AI which is the practice of using AI with good intention to empower employees and businesses, and fairly impact customers and society – allowing companies to engender trust and scale AI with confidence along with the purpose of providing a framework to ensure the ethical, transparent and accountable use of AI technologies consistent with user expectations, organizational values, and societal laws and norms.

Responsible AI is not just a technological discipline rather it impacts and require considerations at – 

1. Operational Level – It requires setting up of governance and systems that will enable AI to flourish  with proper definitions of design principles and methodologies
2. Technical level – Ensure systems and platforms are trustworthy and explainable by design and leveraging shift-left approach to embed best practices and principles being infused during the inception of a project itself
3. Organization level – Democratize this new way of working and ensure human + machine collaboration and defining new roles with proper responsibilities and accountabilities for proper functioning 
4. Reputational Level – Articulate the responsible AI mission and ensure it’s anchored to a company’s core values, ethical guardrails, and accountability structure. 

This has to go hand in hand with proper governance in IT and Business teams to make sure Accountable and Responsible stakeholders are identified to define the Responsible AI principles and accountable to make sure they are implemented properly supported with the actions or to-do steps to ensure recovery and damage control at both Organizational and Reputational level.

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