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Beating AI Bias in Network Operations

Artificial Intelligence (AI) is becoming an important part of service providers’ digital and network transformation efforts. According to a recent Heavy Reading survey, commissioned by Accedian, confidence in AI has increased over the past year among over half (55%) of service providers, with no apparent loss of confidence in AI. 

Although AI-driven live networks is still in the early stages, it is encouraging that more than 60% of CSPs are currently either testing or using AI tools and systems in network operations.

CSPs are serious about using AI in network operations

Communications service providers are serious about using AI in network operations | Accedian
Source: Heavy Reading AI service provider survey, n = 73, Q2 2019

Service providers see the greatest opportunity for AI in service assurance. ‘Anomaly detection for operations, administration, maintenance and provisioning’ was ranked as the largest AI opportunity, closely followed by ‘predicting network faults’, ‘alert/alarm suppression and automated root-cause analysis’. 

But despite these high levels of confidence, CSPs admit to concerns about AI bias; nearly 60% of service providers believe that AI bias is a concern, with nearly one-third viewing bias as a major concern. 

Types of AI bias include:

Four types of AI bias | Accedian

The biggest concern is AI bias due to ‘bad or incomplete data”

Service providers (68%) are most concerned about the bias impact of ‘bad or incomplete data sets’, since effective AI requires clean, high quality, unbiased data. 

CSPs are generally confident they can manage bias and that it’s not likely to delay their AI progress. But the majority agree that AI bias, for example basing decisions on poor quality data, would affect network operations, most notably for ‘prediction of future events’, and ‘pattern detection and correlation’. 

Reaching the wrong conclusions due to not detecting AI bias could impact future network planning and a worst-case scenario of investing capital to upgrade capacity in the wrong areas. Likewise, failing to address AI bias in anomaly and pattern detection data can mean completely missing ‘undetected’ performance issues that are impacting customer experience and should in fact be prioritized.

What can service providers do to beat AI bias?

There’s absolutely no doubt that AI is becoming an integral part of network operations, but it’s early days and there’s still a long way to go if we are to see AI deliver its full potential. AI bias is and remains a huge topic of contention. Telecom networks generate significant amounts of data, which will only increase with the move to 5G. 

This data needs to be collected, cleaned and categorized effectively before it can be used to train an AI system. It’s important that the issues around AI bias are addressed and that CSPs have high quality data and the right analytics tools to unleash the potential of AI on their networks.

Service providers can address some of these opportunities using Accedian Skylight, which helps proactively ensure that networks meet increasingly stringent performance requirements and delivers precise, intelligent performance data to confidently automate service assurance. 

Further discussion of AI bias in network operations and recommendations are available in the Heavy Reading research paper, ‘Trust in AI: Beating the Bias Barrier’.