The top 10 uses of Responsible AI are creating new ways to improve the lives of people around the world.
AI not only provides companies with unprecedented opportunities but also brings incredible responsibility. The outcomes of these Top 10 uses of Responsible AI on people’s lives raise serious questions about AI ethics, data stewardship, trust, and legality. The more decisions a company puts into the hands of AI, the more it accepts significant risks such as reputation, employment/staff, privacy, health, and safety. Responsible AI is the design, development, and deployment of AI to empower employees and businesses and have a fair impact on customers and society. These Top 10 uses of Responsible AI allow businesses to build trust and scale AI with confidence.
Let’s take a look at the Top 10 uses of Responsible AI that we all can incorporate in 2022.
1. Accelerate governance
Accelerating governance is one of the most important and responsible AI applications in 2022. Artificial intelligence is dynamic and is constantly being improved and developed. Organizations need governments to work very quickly with this technology. One of the uses of Responsible AI is to improve corporate governance efficiently and effectively, eliminating errors and risks.
2. Measurable work
Responsible AI helps make your work as measurable as possible. Responsibility can be subjective, so in this case, AI ensures that there are measurable processes such as visibility, accountability, and an auditable technical framework or an ethical framework is important.
3. Improved ethical AI
One of the most important applications of Responsible AI is to improve the ethical AI of your organization. This helps create an intelligent framework that enables you to evaluate AI models and plan fairly and ethically in relation to your business strategic goals.
4. Further development of AI models
Another application of Responsible AI offers the potential to further nurture AI models to improve productivity and efficiency. Organizations can use responsible AI principles to develop AI models to meet the needs and desires of end-users.
5. Introducing bias test
More and more companies will implement bias testing to eliminate inadequate tools and processes. There are several open-source machine learning tools and frameworks with stronger ecosystem support. Responsible AI can be leveraged in these unregulated use cases with these tools focused on mitigating bias assessment.
Responsible AI is enabling new experiences and abilities for people around the globe. Beyond recommending books and television shows, Responsible AI can be used for more critical tasks, such as predicting the presence and severity of a medical condition, matching people to jobs and partners, or identifying if a person is crossing the street. Such computerized assistive or decision-making systems have the potential to be fairer and more inclusive at a broader scale than decision-making processes based on ad hoc rules or human judgments. The risk is that any unfairness in such systems can also have a wide-scale impact. Thus, as the impact of AI increases across sectors and societies, it is critical to work towards systems that are fair and inclusive for all.
Automated predictions and decision-making can improve lives in several ways, from recommending music you might like to monitor a patient’s vital signs. Interpretability is crucial to being able to question, understand, and trust Responsible AI. Interpretability also reflects our domain knowledge and societal values, provides scientists and engineers with better means of designing, developing, and debugging models, and helps to ensure that Responsible AI is working as intended.
ML models learn from training data and make predictions on input data. Sometimes the training data, input data, or both can be quite sensitive. Although there may be enormous benefits to building a model that operates on sensitive data (e.g., a cancer detector trained on a dataset of biopsy images and deployed on individual patient scans), it is essential to consider the potential privacy implications in using sensitive data. This includes not only respecting the legal and regulatory requirements but also considering social norms and typical individual expectations.
Safety and security entail ensuring Responsible AI behaves as intended, regardless of how attackers try to interfere. It is essential to consider and address the security of a Responsible AI before it is widely relied upon in safety-critical applications. There are many challenges unique to the security of uses of Responsible AI. For example, it is hard to predict all scenarios ahead of time, especially when ML is applied to problems that are difficult for humans to solve.
10. Benefits clients and markets
By creating an ethical underpinning for responsible AI, you can mitigate risk and establish systems that benefit your shareholders, employees, and society at large.