Ethical AI for Telecoms: Why it Matters Now

Ethical AI for Telecoms: Why it Matters Now

Given the complexity of delivering ethical AI across functions, and the importance of data and AI in telecommunications, industry leaders would be wise to start mobilizing now:

There has been a lot of buzz lately about ethical artificial intelligence (AI) and its impact on various industries. The telecommunications industry is no exception, as a forthcoming policy in the European Union is expected to have a significant impact on the industry. Companies and investors are recognizing the need to develop an ethical AI strategy, not only to comply with regulatory requirements but also to differentiate themselves in the eyes of consumers, employees, and investors.

The Risks of Ethical AI

One of the biggest challenges with AI is the risk of unwanted bias. Machine learning algorithms, which are the key strength of AI, tend to learn from observations, which means that any pattern in the observed data will influence the results of the algorithms. This is usually what developers want the AI to do, but the problem is that the underlying data can be inherently biased in ways that are often unknown. The key strength of machine learning technologies – the ability to independently learn from observations– implies that any pattern in the observed data influences the results of these algorithms. 

These biases are often detected and amplified by machine learning algorithms, resulting in unwanted decision outcomes. For example, when tasked to identify the recruiting candidates with the best chances to succeed in an open position, AI technologies have tended to short-change women. Why? The data used in training the algorithms is often dominated by profiles of successful men.

There are many other examples of what can go wrong with AI, and ethical AI has quickly evolved into a complex management challenge for executives and boards. It’s not only a matter of complying with expected regulatory requirements (and avoiding potentially significant financial penalties), but it also represents a chance for fast-moving carriers to differentiate themselves in the eyes of consumers, employees, and investors, as well as to advance the company’s business and ESG strategies.

Why care now?

The issues associated with ethical AI are complex, and so are the solutions. Why should telecom business leaders care now? The answer is simple: the world is changing. Regulators, investors, and consumers are demanding more transparency and accountability from companies on how they use AI. In the European Union, for example, the General Data Protection Regulation (GDPR) already requires companies to disclose when AI is used for decision-making and to provide explanations for the results. Soon, the EU will likely introduce a comprehensive legal framework for AI, which will include specific requirements for transparency, robustness, and explainability.

Similarly, investors are asking companies to report on their ethical AI strategies as part of their broader risk management and ESG initiatives. And consumers are increasingly concerned about the use of their data and the potential for bias in AI systems.

Developing an ethical AI strategy

Given the complexity of delivering ethical AI across functions, and the importance of data and AI to succeed in a fast-changing industry, leaders would be wise to start mobilizing now. Here are five steps that telecom business leaders can take to develop an ethical AI strategy, according to consulting giant Bain & Co.:

  • Assess the current AI landscape: Understand where and how AI is used across your organization and identify potential risks and opportunities.
  • Establish governance: Create a cross-functional team responsible for developing and implementing your ethical AI strategy, including data governance, risk management, and compliance.
  • Define ethical principles: Establish clear ethical principles that guide your AI development and deployment, such as equality, robustness, privacy, explainability, and transparency.
  • Identify and mitigate bias: Train your teams on how to identify and mitigate bias in AI systems and data, and implement monitoring and reporting mechanisms to detect and address bias.
  • Communicate and educate: Communicate your ethical AI strategy to stakeholders, including employees, clients and investors.

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