With the emergence of innovations in the AI (Artificial Intelligence), future predictions are determining that it will reshape the world. However, with newer establishments, a lot of training and learning is still needed in industries. Although AI appears to be bright on its surface, to learn, adapt, and enhance human existence, there are dark sides to this field that the general public is unaware of. Concerns of bias, privacy, ethics, and its ultimate control over technology are brought up by these sinister secrets. This article delves into the dark secrets of AI, which is hidden from the public.
1. Bias Embedded in AI
The fact that AI is prone to bias is one of the most worrying features of the technology. Data, which frequently reflects society, is the source of knowledge for AI systems. Regrettably, whether on purpose or accidentally, societal biases like racial, gender, and socioeconomic prejudices frequently find their way into AI algorithms. For instance, it has been demonstrated that individuals with darker skin tones tend to make more mistakes when using facial recognition software. Similar to this, depending on past hiring trends, hiring algorithms may give preference to male applicants over female applicants.
2. Experimental Vehicle
Engineers at Nvidia, a chip maker, built an experimental car that stood apart from other driverless cars and the expanding field of artificial intelligence. It was unlike anything that Tesla, Google, or General Motors had showcased. The car did not follow a single instruction from an engineer or programmer. Instead, all that was required was an algorithm that picked up driving skills through human observation.
It took some skill to get a car to drive in this manner. However, because it's unclear exactly how the car makes its decisions, it's also a little disconcerting. The sensors in the car provide data directly to a massive network of artificial neurons, which process it and output commands that control the brakes, steering, and other systems. As expected from a human driver, the outcome appears to mirror the responses. It could be challenging to determine the ways on how things are working currently.
3. Black Box Dilemma
The following are a few significant effects of the opaqueness of AI decision-making:
a. Lack of Explainability: There are concerns about accountability when AI systems make critical decisions, such approving a loan or identifying a medical ailment, as it is impossible to explain how the AI arrived at that decision. Users and other interested parties have a right to understand the reasoning behind AI results.
b. Fairness and Bias Issues: As AI systems learn from historical data, they may continue to do so and produce biased outcomes if the training data contains biases. It becomes difficult to identify and get rid of biases when one doesn't know how the AI arrived at its conclusions.
c. Regulatory Compliance: In many industries, decision-making procedures must be open and accountable. The Black Box Dilemma can make it challenging for businesses to deploy AI systems in highly regulated areas by impeding efforts to comply with rules.
d. Safety and Trust: In safety-critical applications such as autonomous vehicles, the inability to scrutinize AI decisions could erode trust and system confidence. Users may be reluctant to rely on AI systems if they have doubts about the reasoning behind the systems.
4. AI as Data Sieves
Humans have attempted to construct a complex knowledge hierarchy in which certain details are shared with everyone and others are known only to insiders. The military's classification system is the clearest example of this false hierarchy, but many corporations also use it. CIOs in charge of them as well as the IT department, face difficulties in maintaining these hierarchies.
When it comes to these classifications, LLMs perform poorly. Even while computers are the best rule enforcers and can store catalogues with virtually limitless complexity, the way LLMs are structured makes it difficult to keep certain information private and some accessible. All of it is just a massive set of random walks along Markov chains with probabilities.
5. Unknown Cost
Nobody is aware of the true expense of employing an LLM. Many APIs have a price tag that clearly states the cost per token, but there are hints that venture money is heavily funding the amount as well. The same thing occurred with Uber and other providers. Prior to the investors' money running out, the prices were low; after that, they skyrocketed.
There are hints that the prices that are in effect right now aren't the ones that will ultimately control the market. The cost of renting and maintaining a high-end GPU can be significantly higher. By stockpiling a rack with video cards, you can run your LLMs locally and save a little money, but you will miss all the benefits of turnkey services, such as simply having to pay for the machines when you need them.
While AI has the potentiality for advancing society, its critical to identify some of its dark aspects. Starting with bias in algorithms to lack of transparency in decision-making processes, the ethical, regulatory, and trust-related challenges may well emanate from these hidden challenges. Transparency concerns, safety issues, and unforeseen costs associated with actual deployment of AI systems can, therefore, hinder partial value delivered by AI applications.
The particular obligation of industries is that they should focus on transparency, fairness, and accountability, which align with human values and ensures safety against unintended consequences. It is crucial to maintain a balanced and informed approach.