OPINION: Generative AI – the good, the bad and the risky

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The last year has seen significant announcements and global public attention toward emerging artificial intelligence (AI) and large language model (LLM) platforms like ChatGPT. These transformative technologies have captured the public’s imagination and sparked fears around their rapid evolution. Now, many industries are speculating about the future of their products and services in a shifting technological landscape.

Chatbots and virtual assistants are among the most common use cases of customer-facing generative AI in the air transportation industry. Their use expanded in the 2010s with the likes of Siri and Alexa, and, like many other customer-facing industries, the air transportation industry began leveraging the trend for AI-driven chatbot customer services.

Before the availability of generative AI technology, chatbots were relatively limited in capability and required significant investment in development, training and tuning. Generative AI, and in particular LLMs such as ChatGPT, can make the user experience much richer without the need for expensive machine learning training. They also provide a much broader knowledge base to work from.

Back in 2017, a small percentage of airlines and airports used AI-driven chatbots. We predicted that by 2020, 68% of airlines and 42% of airports would have plans to adopt AI-driven chatbot services offering customer support.

Sita’s latest Air Transport IT Insights report finds that the adoption of AI will continue to increase: airlines (76%) and airports (68%) are planning major programs or R&D for AI by 2025.

Some of the programs underway today are directly customer-facing. For example, Etihad plans to use AI to enable passengers to book flights: Etihad Becomes The Latest Airline To Embrace AI Chatbots (simpleflying.com).

Clearly, although AI and generative AI are still relatively new, they have the potential to transform travel.

Harnessing AI in the air transportation industry today

As a global IT and communications provider for the travel and transportation industry, Sita continuously explores and leverages emerging technologies to transform business models and processes to help the industry reduce costs, overcome operational hurdles and improve the passenger experience.

Starting with the SITA Lab innovation team and expanding across our product portfolios, we thrive on solving the industry challenges of today and tomorrow. SITA Lab explores new technology and drives innovations for the air transportation community, working independently and in partnership with others on pilot projects in robotics, big data, AI, wearable technology and many others.

Although there are risks, generative AI’s opportunities for the air transportation industry are immense. As part of our continuing work around AI, we are exploring numerous use cases to streamline processes, drive new operational insights and improve collaboration between airlines, airports, governments and other stakeholders.

For instance, much process interaction between these stakeholders is through text-based document exchange (for legacy reasons). LLMs make it possible to extract meaning and intent from these human-readable documents and make them into machine-readable and interpretable information. This bridge from human-readable text to digital computer interfaces will enable greater collaboration and knowledge sharing, speeding up processes and enhancing industry efficiencies.

Today, we use AI, including machine learning, for data analytics in several ways. Here are a few examples:

(1) We offer SITA OptiClimb as part of our SITA OptiFlight suite of solutions, the industry’s only machine-learning solutions that analyze aircraft data and weather to optimize fuel and flight paths. The SITA OptiClimb solution, aimed at airlines, delivers fuel savings of 5% for each flight while reducing annual CO2 emissions by thousands of tons and operational costs by millions of dollars.

(2) Our SITA WorldTracer Lost and Found Property leverages machine learning and several other emerging technologies to solve the global multi-million lost property problem by handling lost and found issues promptly and accurately, reuniting passengers with their lost property and ensuring GDPR compliance.

The technology behind the solution searches a global database of images and descriptions to match the found item to a missing item report. The solution uses image recognition to identify details such as the missing item’s brand, material and color. It also recognizes similar words in the description to make a definitive match.

Lost and Found Property cuts the cost of repatriating lost items by 90%. Airline employees can register a found item, create a missing item report and validate a match in under two minutes. The solution also dramatically speeds up the time taken to find and return items, with 60% returned within the first 48 hours.

(3) Our border technologies are leveraging AI too. For example, we use it in our biometric identification technologies that support more modern border control procedures to increase security, improve border agencies’ operational efficiency and generally provide a more pleasurable immigration experience for the traveler.

We use AI to rapidly improve the performance of face recognition software to a point where it meets and even exceeds the performance of other biometric modalities, such as iris and fingerprint, while being more convenient. A combination of more powerful edge processors with machine learning models is enabling face recognition on devices like mobile phones and smart security cameras.

(4) In our SITA Lab, we are developing next-generation digital assistants that leverage LLMs to create chatbot-type interfaces that are much richer than the first-generation chatbots. These bots have access to a combination of airport manuals, passenger points of interest and real-time operational data. These assistants can be deployed at the airport to augment existing information service desks or within airline and airport apps.

We are also developing digital assistants to help with airport operations systems. The assistants can provide advice on routine operational decisions, freeing up time for the airport staff to deal with more complex scenarios.

(5) AI is helping us resolve our airport customers’ technical issues, enhance communications and increase customer self-service through virtual agents.

Here at SITA, we see great potential for generative AI across the entire travel and transportation industry and we will leverage more of it to improve the effectiveness of our solutions and services to support the industry.

Assessing the risks of AI

We are exploring many opportunities with AI. However, there are several potential risks, from privacy violations to discrimination.

In addition to the traditional machine-learning risks, generative AI brings a new category of risk. The most commonly known is when ChatGPT ‘hallucinates’ and provides answers that sound convincing but are wrong or just invented. In the case of LLMs, careful use of prompt engineering and limiting the LLM to a specific data source (such as an airport’s operations manual) can prevent this.

Until trust in these systems is established, it is important to have a human in the decision-making process and to build guardrails into autonomous systems.

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About Author

Gus is the director of SITA Lab, the company’s strategic research and development arm. Created in 2008, SITA Lab explores new technology and drives innovations for the air transportation community, working independently and in partnership with others on pilot projects in the areas of robotics, big data, AI, wearable technology and many others. With 25 years in the IT industry, Pina has always been passionate about transforming business processes with emerging technologies. Prior to SITA, he spearheaded key roles in digital transformations across FedEx, Delta Air Lines and Macy's.

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