Responsible AI


As the world is progressing at a break-neck pace, we are in a dire need of ethical fundamentals and their implementation. With the flow of information at a bizarre rate, it is all the more important to have proper data handling principles and policies to be in place to ensure that data is not driving the wrong decisions.  Responsible AI is the latest solution.

AI and Ethics

 As these data sets can be corrupted easily and still have the power to influence decisions of algorithm-based AI applications. It can prove to be hazardous to people and could negatively impact the environment as well with bad decisions. There is a need for ethics, transparency, and traceability driven through Responsible AI in the field of Artificial Intelligence where we often observe a positive impact on society, businesses, and the environment through the implementation of policies, principles, and governance across different industries.

Apart from the bias in the data set, usually, there is no transparency during any application or transactional data processing to find out why this decision was taken, which parameter influenced it, etc. All these can be answered by embedding explainability and transparency in the AI design processes to provide the correct understanding of the context. 

Responsible AI

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

Responsible AI is not just a technological discipline; it impacts and requires considerations at the Operational Level, technical level, organization level, and reputational level. TRUST is the acronym that mainly defines Responsible AI.

T – Trustworthiness – unbiased and diverse in nature

R – Reliable – thoroughly tested and proven to be able to support the right decision making

U – Understandable – explainable and transparent in nature

S – Secure – having the right security to protect personal or critical information along with supporting regulations

T – Teachable – human-centric in design, flexible to adapt, and easy to adapt in a complex environment

All these aspects can be impactful only when they become part of the day-to-day practices of Data Scientists, Data Engineers, and Business Stakeholders. This has to go hand in hand with proper governance in IT and Business teams to ensure accountable and responsible stakeholders are identified to define the Responsible AI principles.

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