Adoption of AI and ML is becoming necessary for industries to solve real world complex problems and to achieve their growth objectives. Industries are exploring "AI in Everything".
AI requires large amount of real and accurate data to train the machine learning model.
AI usecases are different from the normal rule-based software as in case of Rule-based, control is in the hand of us where we can ensure 100% accuracy but this is not the case with AI usecases.
Chances:
With the adoption of big data and cloud, large amount of customer data is readily available for analytics. Leveraging and utilizing this data in AI, generating lot of opportunities to build and offer personalize product to customer.
one of the examples includes customer segmentation where companies can offer wide range of product based on specific group of customers.
Challenges
Along with Chances and new opportunities, adoption of AI is also generating many challenges. Companies need to make sure customer data is handled securely and properly. It must be compliance as per the government regulation (For example, GDPR). They also need to make sure if ML model is giving the correct and fair prediction on real world data.
Consequences
If customer data is not handled properly and securely, it will break customer trust and directly impact company performance.
For many critical use cases, where AI models are decision maker, small false positive rate can inversely impact the customer badly. Take the example of healthcare industry where Model is predicting whether a Person has chronic disease or not. "How one wrong prediction can impact the patient life!!"
Mitigation:
We recommend below guidelines to handle data privacy in AI Use cases:
Risk Based Approach: Organizations should follow the risk-based approach for each AI use cases by:
Analyze Individual Risk
Determine Risk Level
Probability of harm
Severity of harm
Risk Mitigation
Human in Loop: for high-risk AI use cases, we recommend involving human in loop or Augmented AI on the top of AI Prediction to ensure AI prediction is not biased.
Explainable AI: To ensure, AI decision is not biased and improve user satisfaction, AI use cases should be explainable. for example: if customer loan application is not approved, AI should be able to explain which features are impacting the decision like user credit score, Salary, Other Loan.
Model Monitoring: Monitoring Model in production is also help to ensure model performance and retrain the model at right time.
Model Tracking: Before deploying the model in production, we need to ensure model tracking and versioning mechanism is in place.
Most of the AI challenges can be mitigated with MLOps mindset and setup the MLOps practice from the POC stage.
For details on MLOps , you can go through : https://www.linkedin.com/pulse/mlops-operationalize-machine-learning-ruchi-agarwal/?trackingId=N4KqafCRTfyrGSf0RtSR9Q%3D%3D
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