In the last post, you found out how artificial intelligence thrives on clean, standardized data. In this post, find out how to avoid artificial intelligence bias by training your staff and asking the right questions.
Avoid artificial intelligence bias: train your staff
AI bias is not just caused by bad or incomplete data sets, though. It can also come about as a result of human bias.
AI’s still relatively nascent nature—particularly in network operations—means few engineers have the skills and capabilities to understand AI and machine learning applications. Service providers face a huge skills gap in this area. If they are serious about adopting AI and machine learning applications, they must rethink how they train existing staff, and how they upskill current engineers to implement and train algorithms.
This is important to avoid or minimize the likelihood of AI bias occurring. Human-related AI bias can take various forms. For example, if the person designing the AI system has a goal in mind, implementor bias is likely to skew the results. Training bias may occur if the human designing and training an AI system projects their individual biases or prejudices onto the algorithm.
Reskilling employees with AI training removes a lot of the mystery that surrounds the ‘how’ of AI and gains internal buy-in from more members of operator organizations. This ability to explain the science behind the algorithm will boost adoption and reduce wariness of AI.
On the flipside, inadequate skill sets may lead to AI being used to make decisions in areas where it lacks sufficient experience or exposure. AI applications will only be successful if they are designed in the right way, and that requires a complete understanding of how and why they will be used.
Ask the right questions to design effective AI algorithms
When designing artificial intelligence algorithms, service providers must ask the right questions. But what are the right questions? The distributed nature of telecom networks make figuring that out challenging. Each node (router, switch) only has a very small and partial view over the entire network. In this environment, it’s difficult to design algorithms that have a holistic view of the entire network. Lacking that, is very likely results will be skewed. Understanding how AI and machine learning can be applied to a specific part of network operations is therefore crucial.
For example, consider a telecom network-centric application for AI like predicting faults. AI and machine learning can be very useful in this area of network operations, to diagnose root causes of performance issues, correlate across multiple event sources, and recommend solutions—all in real time. Using AI for service assurance will become even more important as 5G data volumes explode, making quality of service (QoS) and quality of experience (QoE) increasingly complex to manage, monitor, and maintain.
Service providers will need the right AI tools to provide a complete view of customer experience, and to feed recommended actions, based on timely analytics of data and events, into network controllers, orchestrators, and network and service operation centers (NOCs/SOCs). Deploying low-cost, high resolution sensors across the network enables service providers to collect real-time data to detect service quality issues, and using predictive analytics and AI to prevent those issues from impacting customers in the first place. This together with a cloud-based SaaS approach to analytics will further lower barriers to implementation.
Conclusion: how to take on AI
Doubtless, AI could help solve many of the challenges service providers currently face around managing, assuring, and controlling their networks. Widespread AI adoption in the telecom market, however, will only come by taking steps to avoid AI bias, upskilling staff through training, and asking the right questions to understand which AI algorithm should be applied and to what data. These considerations are critical for successful use of AI in network operations. Failure to address current AI challenges will cause service providers to miss out on opportunities today, and could also significantly hinder their future success in the 5G era.