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.
Understanding the AI Life Cycle From an Ethical Perspective
Identify the Ethical Guidelines
The foundation of ethical AI responsibility is to develop a robust AI lifecycle. CIOs can establish ethical guidelines that merge with the internal standards applicable to developing AI systems and further ensure legal compliance from the outset. AI professionals and companies misidentify the applicable laws, regulations, and on-duty standards that guide the development process.
Conducting Assessments
Before commencing any AI development, companies should conduct a thorough assessment to identify biases, potential risks, and ethical implications associated with developing AI systems. IT professionals should actively participate in evaluating how AI systems can impact individuals’ autonomy, fairness, privacy, and transparency, while also keeping in mind human rights laws. The assessments result in a combined guide to strategically develop an AI lifecycle and a guide to mitigate AI challenges.
Data Collection and Pre-Processing Practice
To develop responsible and ethical AI, AI developers and CIOs must carefully check the data collection practices and ensure that the data is representative, unbiased, and diverse with minimal risk and no discriminatory outcomes. The preprocessing steps should focus on identifying and eliminating the biases that can be found while feeding the data into the system to ensure fairness when AI is making decisions.
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