What is Responsible AI, and how it’s implemented?
The article explores the potential benefits of using R.AI to analyze human behavior and its impact in various industries, including banking, insurance, and organizational management. Highlights that studying a human being cannot be done the same way as analyzing a machine, as a change in behavior in a person can be due to various factors, including emotional and personal experiences.
Responsible AI is the development, deployment, and use of artificial intelligence systems and algorithms in an ethical and socially responsible manner. This involves considering AI’s potential social, honest, and legal implications and taking steps to mitigate negative consequences and promote positive outcomes.
Responsible AI involves several vital principles and practices, including:
1. Transparency: AI systems should be transparent and explainable, allowing individuals to understand how decisions are made and why.
2. Bias and fairness: AI systems should be designed to minimize bias and discrimination and to treat all individuals fairly, regardless of race, gender, religion, or other personal characteristics.
3. Privacy: AI systems should protect individuals’ personal information and maintain privacy.
4. Responsibility: Organizations should be held accountable for the impact of their AI systems and take responsibility for any negative consequences that may arise.
5. Human oversight: AI systems should be designed and deployed to allow human management and intervention to ensure that they are aligned with ethical and social norms.
By implementing these principles, organizations can develop and use AI systems that benefit society, protect individuals’ rights and interests, and advance the responsible use of AI.
To see the discrete individual as a whole within an environment that lives and changes at any moment. A shift in sound in the machine could indicate wear. Therefore, it is impossible to analyze a human being using AI in how a device is analyzed.
A change in a person’s tone can be due to various factors (fatigue, personal crisis, stress, trauma, heightened emotion, etc.)
An enterprise can get detailed insights into an individual using R.AI as it can analyze a person not only when looking at culture, race, and gender but taking it to the next step by knowing how you will react and how you respond currently. Let us take, for example, a new employee joining an organization; when time passes, he changes his behavior due to his manager, co-workers, teammates, customers, and projects he is involved with. This is called the uncertainty factor. If we analyze the damage the new employees leave and the organization within a year and provide the organization a tool to predict that, it will save the time and resources of the organization. Another example is analyzing potential investors’ profiles, most people will claim they are risk-takers, but reality shows if they win. Therefore, a deep analysis will indicate both sides of the accurate investor profile.
In baking loans, the bank is looking for low risk, and analysis will provide the customer’s current reaction to the loan, where he feels confident, and where not.
The connection between banking and insurance exists, as both industries require a high level of certainty to minimize risk. Insurance salespeople create a contradiction with the insurance firm, as they have sales targets while the insurance firm seeks low-risk customers. They are in a competitive market and are looking at how to cut costs and lower their services to the customers. In insurance, data provided during the purchase is crucial to analyze costs.
In insurance claims, identifying fraud became a high priority, and using the investigation is expected. Revealense analysis can cut insurance costs by identifying cons and using the technology as a supporting tool for insurance claim investigation.