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The constant innovation and development with artificial intelligence (AI) have transformed the workforce of so many industries with new opportunities to boost productivity. Thus, organizations need to be responsible with AI with Responsible AI as a governance framework to document the challenges around artificial intelligence. Organizations should follow emerging trends of Responsible AI to achieve fairness and trust in the highly competitive market. Let's explore some of the top Responsible AI predictions to look out for in 2022.
Accelerating governance is one of the top Responsible AI predictions for 2022. Artificial intelligence is dynamic in nature with constant improvements and developments. Organizations need their government to function at a rapid speed like this technology. Responsible AI toolkit should be all-time on track of AI model performances and look for new potential risks throughout the process. One of the trends of Responsible AI is to boost company governance efficiently and effectively to eliminate errors and risks.
One of the top Responsible AI predictions is the enhanced Ethical AI in organizations. It will help in creating smart frameworks that can assess and plan for AI models to be fair and ethical towards the goals of company strategies. Being responsible means being more ethical towards the products and services in the global tech market. End-users should have a strong understanding of their ethical concerns or doubts about artificial intelligence.
Another trend of Responsible AI is providing an opportunity to cultivate AI models more to enhance productivity and boost efficiency. Organizations can utilize the principles of Responsible AI to cultivate AI models as per the needs and wants of end-users. Employees need to focus on appropriate real-time data and seek improvement to fulfill all the needs to have a successful Responsible AI in a company.
One of the top Responsible AI predictions is that more companies will adopt bias testing and eliminate inadequate tools and processes. There are multiple open-source machine learning tools and frameworks with stronger ecosystem support. Responsible AI can be leveraged with these tools focusing on bias assessment with mitigation, especially in non-regulatory use cases.
Organizations need to put more focus on explainability to follow Responsible AI efficiently. There cannot be a complex AI model performing that is difficult to explain to stakeholders. Responsible AI needs organizations to have a strong understanding of artificial intelligence algorithms and the process to provide predictions. Thus, if a company puts more focus on the explainability of AI models, it is easier to follow Responsible AI principles and meet customer satisfaction in a long run.
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