When applying Trustworthy AI techniques, we always focus on the explainable AI solutions that allow us to unlock what is behind and AI model and make it accessible to different stakeholders, so that we can trust its responses.
The explainability of an AI model can be put in practice in different ways for various industries. Let’s see some examples.
1.Healthcare
When working in healthcare, we are talking about highly regulated environments that need to be certified, trusted and accountable. For instance, when performing patient disease diagnosis, explainable AI can explain the elements and data that was used to diagnose that patient. This way, we help create greater trust between patients and their doctors while mitigating any potential ethical issues when a machine is aiding the detection of a disease.
Typical use cases for this are validation of AI predictions that work with medical imaging data when diagnosing cancer.
2.Manufacturing
Explainable AI can also by applied in a production line to detect, map and explain the causes for unproper machine behaviour or defective product outputs, causing what it is called “nonconformities” on product quality in the production process or highlight the need for maintenance.
This way there is a higher understanding of machine-machine and machine-operator communication and business management policies can be made to decrease costs and gain productivity while keeping trusted and save production standards that need to be complaint and certified.
3.Mobility
Explainable AI is becoming increasingly important in the transport and automotive industry due to the expansion of IoT and smart mobility solutions as well as the potential expansion in use of autonomous vehicles -first in business environments such as logistics self-driven vehicles or trains, later in end-users.
This has placed an emphasis on explainability techniques for AI algorithms, especially when it comes to using cases that involve safety-critical decisions. Explainable AI can be used for autonomous vehicles where it provides increased situational awareness in accidents or unexpected situations, which could lead to more responsible technology operation (i.e., preventing crashes).
4.Recruitment
Resume screening: explainable artificial intelligence could be used to explain why a resume was selected or not. This provides an increased level of understanding between humans and machines, which helps create greater trust in AI systems while mitigating issues related to bias and unfairness.
5.Finance
Fraud detection: Explainable AI is important for fraud detection in financial services. This can be used to explain why a transaction was flagged as suspicious or legitimate, which helps mitigate potential ethical challenges associated with unfair bias and discrimination issues when it comes to identifying fraudulent transactions.
Loan approvals: explainable artificial intelligence can be used to explain why a loan was approved or denied. This is important because it helps mitigate any potential ethical challenges by providing an increased level of understanding between humans and machines, which will help create greater trust in AI systems.
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