Introduction:
When it comes to fantasy, the Red Woman of Game of Thrones named Melisandre was a mystic priestess and a woman who directed kings and armies with her visions, based on fire. The mystic-camouflaged nature of her prophecies was instrumental in some of the most significant decisions and wars. Yet in our actual world the parts of the prophet have been played not by mystics, but by machines. Nowadays, predictive technologies powered by AI are becoming our digital oracles that lead governments, businesses and societies.
It is possible to contend that the character of Melisandre would be fictitious, however, her narrative is a direct reflection of a potent reality about the current technology environment: we are becoming more dependent on complex systems to inform us of the future.
Artificial Prophecy Epoch
Whether it is a search engine, predictive policing, algorithmic trading, disease forecasting or even whether an individual should get the death penalty, Artificial Intelligence systems are no longer merely analyzing data but predicting the future with an eerie precision. This change in the technology-reactive to the predictability is the same as experienced by Melisandre in Westeros-she was not merely reading what was going to happen but felt that she could actually influence what would be, through this vision.
AI systems in modern society process large volumes of data, including behavioral patterns, changes of the environment, and others, to make predictions in the same way Melisandre used to interpret flames. These involve technologies giving their power to machine learning, deep learning, and neural networks capable of processing billions of data in a second. We no longer use intuition and divine interpretation as was the case by Melisandre, we use algorithms.
The predictive algorithms can be referred to as the flames.
The flames were an advisor and a puzzle to Melisandre. She is usually misunderstanding them, with disastrous results (the burning of Shireen). Predictive models, sophisticated as they are, are simply good as data and assumptions on which they are based.
For example:
- Algorithms based on facial recognition have misidentified persons of colour, because of biased training.
- Predictive policing software has been criticized to reify the systemic biases by concentrating on the communities that are already thoroughly policed.
- The artificial intelligence algorithms of recruitment may negatively discriminate candidates due to gender, racial, or other background.
- All these real world tech comparisons are an echo of the most dangerous trait developed in Melisandre, which is a blinding faith in the flawed vision. Data might be objective but interpretation thereof and subsequent action upon prediction will always be human and fallible.
- Belief, Confidence and Algorithmic Choice
- Trust is something that Melisandre had over Stannis. In the same way, organizations and businesses have developed huge belief in forecasting systems. For example:
- Industrial companies have been entrusting AI to forecast the performance of the stock and automate trades to millions of dollars.
- Patient risk, treatment decisions, and disease outbreak predictions are health care provider points where prediction models are helpful.
The recommendations of e-commerce giants made sales in billions as they involve recommendation engines that declare the preferences of users.
However, similarly to the Red Priestess, in case the predictions turn out to be erroneous, the outcomes may be catastrophical, including even financial losses, failed diagnosis, and social injustices.
Responsibility Rewrafted: The Moral Level
Accountability is one of the main points to take as an example of Melisandre. She is exiled and shunned and in the end wants redemption despite her abilities. Algorithmic accountability is more at the center of an ethical issue in the tech world.
Questions like:
- In case a predictive model harms people, who should be blamed?
- Are algorithms tackling the explainability and transparency issue?
- What can we do to discourage AI-derived prophecy into cyber-dogmatism?
These are not mere theoretical questions and there are real lives behind them. In the world of increasingly autonomous systems, it is necessary to develop ethical considerations, explainable artificial intelligence models and human-in-the-loop procedures to prevent blindly trusting predictions based on data.
The metaphorical use of Melisandre as an evolution of the technology
The case of Melisandre, once a confident but then exhausted redemptor, can also be the lesson that should be remembered by the tech developers, leaders, and individuals using technology. Her early faith in prophecy that is absolute in nature is reminiscent of our initial belief in blind faith as related to algorithms. She later manages to humble herself and admit her mistakes, thus placing emphasis on the balance between technological and human opinion.
Similarly, Melisandre plays no instrumental role in killing the Night King–she lets human actors take care of the job, such as Arya. Artificial intelligence is only but an augmentation of human agency rather than a replacement. Technology is not supposed to be a master but a servant.
The Conclusion: Mystics to Machines
In this age of data and technologies we do not peer into fires anymore to predict the future; we search into data lakes, dashboards and machine learning models. This transformation of mystical priestess to digital predictor implies more than a changing set of tools, a reconstitution in our fundamental interaction with doubt, and choice.
However, it is important to keep in mind that such power without knowledge is treacherous as Melisandre. Predictive technology can be very powerful, yet not infallible. We are not out in the technology frontier of AI and prophecy, we are at the frontiers of prophecy and we always need to balance that with something introspective and we should not forget the lessons taught by those that espoused being able to see the future.
