Misinformation, trust and AI – my experience

4

October

2024

5/5 (1)

A few days ago I saw a video that inspired me to write this blog post. The viral video presented an interview with a famous actress, Jennifer Aniston. She was explaining how despite being in her 50s, she didn’t gain weight by special exercise and diet. What I found out soon after is that the image from that video came from a totally different interview, in which Jennifer Aniston was discussing her acting methods and her experience. The voice in the video was a voice generated using generative AI text-to-voice technology that learned her voice characteristics.

Although this example isn’t that important to me, it represents a larger problem. In my opinion that problem is trust towards AI and usage of AI for fake news. Further examples of that can be found in politics. As you can see below social media (in this example X) can be easily flooded with bots spreading made up information. These are just two examples, but many more can be easily found online. It amazed me how quickly a personalized message can be spread and how inconspicuous these accounts are. For most it might be very difficult to differentiate between an AI account and a committed activist.

Example 1
Example 2

AI generated content is now used to intensify social unrest during difficult times. During Covid-19 pandemic, bots originating in Russia were used to spread conspiracy theories and fear among those most vulnerable. Examples include false claims about cures, vaccine side effects, and governmental responses. These bots often used AI to automatically generate tweets, comments, or fake “experts” that made the information seem more legitimate.
Nowadays, many social media platforms and websites are trying to introduce AI-detection methods and technologies, for which they also use AI. Machine learning models are being trained to identify fake news by analyzing language patterns, cross-referencing facts with reliable sources, and recognizing misleading headlines. AI can also assist in real-time verification by scanning vast amounts of online content to compare it with trusted databases. Some AI models are specifically trained to recognize the digital fingerprints left by deepfake generation processes, allowing them to detect even well-crafted deep fakes that evade human detection. AI-based detection systems analyze video and audio data to look for subtle artifacts, such as unnatural facial movements, irregular lighting, or voice modulations, which indicate manipulation.

These tools developed quickly in recent years. The problem remains, as AI used for misinformation is developed at the same rate. As AI improves at creating fake content, it also gets better at detecting it, leading to an AI arms race.

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Why did I pay so much? – about airline price discrimination methods

17

September

2024

5/5 (2)

Most of us had probably bought a plane ticket and wondered where the price came from. For many it might see like airlines set their price at random, as for each flight prices change from day to day. Despite how it looks from the outside, various revenue management systems and pricing strategies make sure that each customer pays the most optimised price. The goal of these is to make sure that revenue is maximised with a limited seat capacity. The question is, can we say that airlines are getting close to a perfect price discrimination? In the long term, fuel prices are the single most significant factor influencing ticket prices. In the short term, which is usually more relatable for customers, there are numerous other factors, a few of which I would like to name below.

For easier customer segmentation airlines use versioning and allow two primary choices. Firstly, airlines offer different price points for classes (first, business, premium and economy). Secondly, fare bundles are offered to further optimise pricing. Consumers, depending on their needs and willingness to pay, choose baggage options, fast track access, seat selection, refundability and many more. For airlines these are called ancillary products, which are usually sold in bundles with the primary product, an airline ticket (Boin et al., 2017).

One of the basic ways that airlines segment their customer base is leisure and business travel. This division is not connected with classes as both groups fly in all classes. Airlines use a number of criteria to assign consumers to the correct group. Firstly, how many days before departure was the ticket bought. Corporate travel often has a shorter booking window (buying tickets closer to departure), as plans often change when many stakeholders are included. Other criterias are whether flights are on business days or weekends, how long the stay at the destination is and how big the group is. 

Time is another aspect you need to think about when looking for the cheapest ticket. It is not only about when you buy the ticket, but also when you fly. The most visible difference is between off-peak and peak seasons, including Christmas, New Years. Some peaks are local, most commonly for events or local holidays. Depending on your destination prices can vary significantly among months. On some connections, for example to major academic centres, the beginning and end of the academic year is reflected in prices. Travel to leisure destinations will be more expensive between June and September, but during the same time business destinations are usually cheaper. Even during the same day prices may differ between the same route at different hours. As some flights are used more for point to point travel and some are feeder flights for other routes (Abdelhady & Abou-Hamad, 2020). There are many other factors on which a ticket price depends. Such as group discounts, student programs, frequent flyer programs, geographical price differences and many more, but I will not elaborate on these here.

Nowadays, most traditional airlines use systems based on Reservation Booking Designators (RBDs) to assign prices to each customer. This results in limitations, as the number of RBDs is limited (standard number of RBDs is 26)(Fiig et al., 2018). Continuous pricing is expected to transform that situation and offer airlines unlimited opportunities to personalise prices to the willingness to pay of each customer. Despite having a lot of personalisation possibilities, airline pricing is not a perfect price discrimination example. It is still far from name-your-own-price models, but looking at improving usage of algorithms and possibly AI it is getting close.

Sources

Abdelhady, M. R. R., & Abou-Hamad, M. M. H. (2020). Airlines’ Pricing Strategies and O-D Markets: Theoretical and Practical Pricing Strategies. Journal of Travel, Tourism and Recreation2(3), 19–36. https://doi.org/10.22259/2642-908x.0203004

Boin, R., Coleman, W., Delfassy, D., & Palombo, G. (2017). How airlines can gain a competitive edge through pricing. McKinsey. https://www.mckinsey.com/industries/travel-logistics-and-infrastructure/our-insights/how-airlines-can-gain-a-competitive-edge-through-pricing

Fiig, T., Le Guen, R., & Gauchet, M. (2018). Dynamic pricing of airline offers. Journal of Revenue and Pricing Management17(6), 381–393. https://doi.org/10.1057/s41272-018-0147-z

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