Categories: Industries

AI trust and governance: driving value-centric transformation for telcos

AI trust and governance: driving value-centric transformation for telcos

Jeffrey Eyestone

Jeffrey Eyestone, AI Industry & Solutions Specialist at Tecnotree

The telecommunications industry has driven unparalleled innovation through Artificial Intelligence (AI). However, a robust trust and governance programme is required to realise the full potential of AI-powered solutions. This holds especially true when delving into the latest tech innovation – Generative AI.

Telcos and application developers must continuously evaluate their AI models and decision-making systems for trust. Moreover, they must meet the rigorous governance requirements of regulators, compliance officers, risk managers, and auditors to ensure responsible AI implementation.

Whilst using applications like ChatGPT has created a lot of excitement for various use cases, including customer service and support, sales and marketing, network optimisation, fraud detection and prevention, and predictive maintenance, it also comes with certain trust and governance risks.

Technical assessments for AI trustworthiness

The responsible use of AI in telecommunications demands continuous technical assessments to ensure trust in AI models and decision-making systems. There are several critical considerations, such as fairness, privacy and accuracy.

For example, AI models can inadvertently perpetuate bias in training data, leading to irreparable reputational damage if the risk isn’t continuously evaluated. In fact, multiple companies have been found extending different contract terms, pricing, or credit rates and limits to various segments of the population due to bias in models. Therefore, AI systems must maintain high accuracy to provide reliable and unbiased services, and organisations should regularly monitor and calibrate models to prevent accuracy degradation over time.

In addition, ensuring customer data privacy should be a priority for telcos. They must comply with data protection regulations and implement strong security measures to safeguard sensitive information. Having transparent AI models is crucial, as decisions can significantly impact users. Therefore, implementing explainable AI techniques helps organisations understand the reasoning behind AI decisions and enhances accountability.

Finally, telecom AI systems should be resilient to unforeseen scenarios and adversarial attacks. A robust testing strategy helps identify vulnerabilities and strengthens the system’s ability to handle unexpected challenges.

Pillars of AI governance

Effective governance is critical to responsible AI deployment in telecommunications companies. AI-powered transformation solutions must meet the requirements of compliance officers, risk managers, and auditors, therefore, telcos must stay up to date with evolving AI regulations and standards. Compliance ensures that AI implementations align with legal requirements, reducing the risk of regulatory penalties.

Telcos should also ensure they are developing and adhering to ethical AI frameworks, including setting data collection, use, and retention guidelines. Conducting thorough risk assessments helps identify potential AI-related risks and implement mitigation strategies. This includes evaluating the impact of AI failures and data breaches. Organisations should regularly monitor AI systems for compliance and performance and have appropriate auditing and reporting mechanisms to track system behaviour and identify deviations.

Implementing a trust and governance discipline in AI/ML teams

In the rapidly evolving telecommunications landscape, where AI and machine learning (ML) play increasingly vital roles, establishing a robust trust and governance discipline within a company’s AI/ML team is imperative. Organisations should begin by selecting dedicated teams comprising experts in AI solution development (data scientists, data engineers, application developers, I.T. staff, etc.), ethics, compliance, and risk management.

Many countries are instituting AI governance frameworks to ensure responsible AI deployment. For instance, the European Union’s General Data Protection Regulation (GDPR) has significant implications for AI, emphasising data privacy and transparency. Telcos need to stay abreast of country-specific AI regulations and ensure they are compliant, especially if they have international operations. Collaborating with local AI governance bodies, data protection authorities, standardisation organisations, auditing and compliance specialists, and employee training companies specialising in local and regulatory compliance can be helpful.

To harness the transformative power of AI, especially innovations like Generative AI, in telecommunications, a robust trust and governance structure is non-negotiable. Organisations should develop comprehensive ethical guidelines that govern data usage, model training, and decision-making. In their guidelines, they must explicitly address sensitive issues such as bias, fairness, and discrimination.

Embracing this structured approach not only unlocks AI’s vast potential but also ensures sustainable, value-driven growth for key transformation projects. In essence, trust and governance are the gatekeepers of AI’s promise in telecommunications, enabling a future where innovation and responsible use coalesce seamlessly to realise the full potential and value of AI.

Staff GBAF Publications Ltd

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