What makes you trust on AI Technology?
Buying a car or a home remains to be an exciting moment of any person's life. Customers can be quite comfortable with data-driven recommendations in the process of search. For instance, websites suggesting homes based on the previously viewed properties. But what if the entire decision of getting an auto loan or mortgage is made through a machine learning algorithm? What if the entire logic behind the decision of the algorithm is unclear and rejects your application?
It is quite difficult to face whenever a loan is denied after you have gone through a traditional process, and getting turned down by an AI or artificial intelligence powered system which can’t be explained can be worst experience. Customers have been left with no choice of knowing how actually to improve their chance of any success in the future. Custom software services have to consider these scenarios to mitigate the issues faced by customers.
Similar is the case in industries like healthcare. The AI programs can provide early detection of any disease at the earlier stages to the patients and the doctors. But when it comes to any medical diagnoses, the stakes are relatively higher, and any type of misdiagnosis can lead to unnecessary treatment and even surgery which can lead to deterioration of the patient’s health. Doctors need to trust the AI systems in order to use them confidently as a diagnostic tool, and the patients need to trust the entire system if they have complete confidence in their own diagnosis.
More companies in different industries are now adopting machine learning as well as advanced Artificial Intelligence algorithms like deep neural networks. Their inherent ability to offer an understandable explanation to all stakeholders have become quite critical. However, some machine learning models which underlie the AI applications actually qualify as black boxes, which means that the people can’t always actually understand how exactly an algorithm is taking any action.
It’s the inherent nature of humans to distrust anything which they don’t understand and similar is the case when it comes down to AI technology. The distrust goes along with the lack of acceptance among the people which has made the companies to open this black box. Custom web development services need to address this distrust and find ways to gain the trust of the customers and increase the acceptance level of AI among them.
Deep Neural Networks and AI
Deep neural networks are essentially complicated and complex algorithms that are modeled after the human brain and are designed to recognize different patterns by means of grouping the raw data intro various discrete mathematical components called as vectors. In the case of medical diagnosis, this particular raw data comes in the form of patient imaging. In the case of bank loan, the raw data is actually made up of defaulted loans, payment history, credit score as well as other demographic information along with various risk estimates. The system continues to learn by processing all of this data, and every layer of this deep neural network actually learns to recognize the more complex features progressively.
With the sufficient amount of training, the AI actually become more accurate. However, the decision processes may not always be transparent. In order to open the AI black box as well as to facilitate trust, the companies need to develop various AI systems which perform reliability and make correct decisions, every time. Various machine learning models on which these systems are actually based on the need to be transparent, and able to achieve different repeatable results. This combination of features in an AI model is called its interpretability. Mobile app development services have to consider this interpretability in their AI system development.
Role of AI system developers
It is vital to know that there is a certain trade-off between interpretability and performance. For instance, a simpler model can be quite easier to understand, but it won’t be actually able to process complicated and complex relationships and data. Getting this particular trade-off right is basically the domain of analysts and developers.
The business owners need to have the basic understanding of what actually determines whether a particular model is interpretable as it is one of the key factors that determine the legitimacy of the system in the eyes of the customers. It is the role of the developers to create AI systems that fulfill the expectations of the customers and maintain their trusts.
Developers working in custom software services often face with challenges of ensuring interpretability, data integrity and consistent performance and they have to work closely with the organization leaders and employees. Developers are responsible for the creating of machine learning model along with choosing the algorithms that are used for the particular AI application as well as verifying that the particular AI system is correctly built and continues to perform as per expectations.
Developers are also responsible for the validation of the AI model that is created in order to make sure that the model actually addresses the requirements of the business. Also, management is responsible for the significant decision to deploy the AI system and needs to be prepared to take the final responsibility for the business impact.
For any company that wishes to get the best out of any AI system, it is vital for people to understand it clearly and also adhere to these responsibilities and roles. Finally, the goal is to design a great machine learning model for any given AI application such that the company can easily maximize the performance while addressing any type of operational concerns comprehensively.
Emerging Regulatory Rules
Businesses need to follow the evolving AI regulatory norms. Such regulatory requirements aren’t currently extensive at present, but they will keep on emerging over time. Last year, there has been the introduction of regulatory rules like GDPR in Europe which requires the companies that do business in Europe to take necessary measures in order to protect the privacy of the customer and ensure the transparency of all the algorithms which impact the customers. Custom web development services have to make sure that the privacy and security of the customer data is kept at the highest priority.
Business leaders need to consider that every AI application actually different in various degrees and there is always a risk to human safety. In case the risk is quite great, and the role of any human operator is reduced significantly then the requirement of the AI model needs to be reliable, clearly understood and easily explained. This is certainly in case of AI applications like self-driving cars, fully automated diagnosis process for cancer.
Other AI applications don’t put health and lives of people at risk. AI also screen mortgage applications and even run a marketing campaign. However, the results can be biased, and there is a reasonable level of interpretability which is still required. The companies need to be comfortable with the AI application and should be able to explain to its customers, the reasons their system has approved any application over another or a particular targeted group of customers in a campaign.
Conclusion
Opening the AI models’ black box has become necessary for companies to ensure that any AI system based on machine learning model to perform according to the standards of the business. The company leaders need to justify the final outcomes. This will help in reducing risks as well as establishing the required trust for AI in order to become a truly accepted method of latest innovations and business goals achievement. These above-mentioned aspects will make you trust in AI technology.
