CIOs agree that many of their clients are skeptical about artificial intelligence and machine learning models interacting real-time with them
Machine learning models and artificial intelligence solutions have been rapidly adopted as part of enterprise solutions and consumer products. AI tech has quickly advanced, and is now applicable for diverse and large-scale use cases. It’s imperative that organizations look to ethics for guiding the tech’s development, applications, and deployment in such a scenario. Such guidelines are critical when it finds application in sectors like education, enterprise solutions for healthcare, justice, public welfare, etc.
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CIOs believe that it’s essential that AI tech and application be transparent and trustworthy as they are widely implemented at both enterprise and consumer levels.
Overcoming the mistrust around the technologies
Clients mistrust AI and ML solutions that directly interact with them or their customers on their platform. Very few of them are comfortable with enterprises that deploy AI solutions for interaction. As per the current trend, enterprise AI technology utilization will increase exponentially in the coming years. Most prominent enterprise and government contracts that deploy AI solutions will require the process to adhere to ethical AI guidelines.
Business leaders are now more concerned with the transparency and ethics of the AI solutions, largely because these tools and solutions are integrated with platforms in business critical operations. They need to be reassured that the applications will help improve the pace of innovation, and benefit individuals, society, and the community, in addition to the enterprise.
Ensuring unbiased data sets for AI model training
CIOs say that ethical AI applications measure the tech itself and the data that drives the tech. If the AI system’s input isn’t a representation of diversity or is biased, then the results will reflect the bias. Such scenarios are very concerning in areas like AI and ML tech that are deployed in critical community use cases – like facial recognition or police use.
Technology leaders- right from the software developers to product managers, need to ensure that data sets for AI training and ML models are a complete representation of the audience base or the end-users who intend to use it.
Complete data security and privacy across all applications
Technology leaders and enterprises across the domains also need to prioritize security concerns and data privacy. They need to be sure that solutions adhere to the industry-specific regulations relevant to utilizing client data use. Enterprises need to be transparent with clients regarding data utilization and sharing, sale, or rent to third-party companies. Clients and end-users should have the full ability to provide informed consent before the data is shared, used, or transmitted in any format. Organizations must mandatorily inform the clients if their data has been used for Machine learning models training.
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Limited and strategic deployment of AI for critical use cases
AI should be sparingly used for the critical decision-making process. When used, enterprise leaders should preferably ensure that it’s for strategic purposes, and human intelligence has intervened in the final decision.
Informing clients when they interact with AI
CIOs acknowledge that advanced AI chatbot interaction has significantly been nuanced, become more granular, and of late- customized. In fact, so much so that today it is quite difficult for clients and potential customers to differentiate if they interact with AI or human employees. AI and NLP tech have helped tailor the interactions that respond to an individual’s intent, meaning, content, and sentiment. Organizations that deploy AI chatbots should consider leaving space for human interaction and inform the clients about it. This will also help to ensure that customers, both internal and external, know the tool to be dependable.