Artificial intelligence (AI) has been a game changer in the business landscape, as this technology can analyze massive amounts of data, make accurate predictions, and automate the business process.
However, AI and ethics problems have been in the picture for the past few years and are gradually increasing as AI becomes more pervasive. Therefore, the need of the hour is for chief information officers (CIOs) to be more vigilant and cognizant of ethical issues and find ways to eliminate or reduce bias.
Before proceeding further, let us understand the source challenge of AI. It has been witnessed that the data sets that AI algorithms consume to make informed decisions are considered to be biased around race and gender when applied to the healthcare industry, or the BFSI industry. Therefore, the CIOs and their teams need to focus on the data inputs, ensuring that the data sets are accurate, free from bias, and fair for all.
Thus, to make sure that the data IT professionals use and implement in the software meet all the requirements to build trustworthy systems and adopt a process-driven approach to ensure non-bais AI systems
This article aims to provide an overview of AI ethics, the impact of AI on CIOs, and their role in the business landscape.
Redefining AI Ethics
With the emergence of big data, companies are increasingly focused on driving automation and data-driven decision-making across their departments. The intention of using these technologies is to improve business outcomes, but if not implemented with set rules and regulations, the companies might experience unforeseen consequences in their AI applications due to poor research design or biased datasets.
This unfair outcome has come to light for numerous CIOs and their teams; therefore, new guidelines have emerged with the aid of primary research by data scientists and IT researchers to address the concerns around ethical AI.
Leading IT companies and other industries that are using AI have vested interests in shaping ethical guidelines, as these companies have themselves started to explore the use of AI to uphold ethical standards.
This lack of diligence in AI and AI’s subsets can result in legal, regulatory, and reputational damages, as well as cost penetrations.
On the other hand, the popularity of natural language processing (NLP), machine learning (ML), and deep learning applications have brought challenges that lead to dangers for implementing them in organizations; thus, CIOs need to review the ethical considerations.
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