Four years ago, a study found that some facial recognition programs incorrectly classify less than 1 percent of light-skinned men but more than one-third of dark-skinned women. The producers claimed that the program is proficient, but the data set they used to assess performance was more than 77 percent male and more than 83 percent white. An overreliance on AI technology could result in the loss of human influence — and a lack in human functioning — in some parts of society. Using AI in healthcare could result in reduced human empathy and reasoning, for instance. And applying generative AI for creative endeavors could diminish human creativity and emotional expression. Interacting with AI systems too much could even cause reduced peer communication and social skills.
“If we’re not thoughtful and careful, we’re going to end up with redlining again.”
Yes, language models based on GPT-4 and many other models are already circulating widely. But the moratorium being called for is to stop development of any new models more powerful than 4.0—and this can be enforced, with force if required. Training these more powerful models requires massive server farms and energy. In testing GPT-4, it performed better than 90 percent of human test takers on the Uniform Bar Exam, a standardized test used to certify lawyers for practice in many states. That figure was up from just 10 percent in the previous GPT-3.5 version, which was trained on a smaller data set. They found similar improvements in dozens of other standardized tests.
Similarly to the point above, AI can’t naturally learn from its own experience and mistakes. Humans do this by nature, trying not to repeat the same mistakes over and over again. However, creating an AI that can learn on its own is both extremely difficult and quite expensive. Perhaps the most notable example of this would be the program AlphaGo, developed by Google, which taught itself to play Go and within three days started inventing new strategies that humans hadn’t yet thought of. That’s not always a bad thing, but when it comes to producing consistent results, it certainly can be.
- Once it learns well enough, we turn AI loose on new data, which it can then use to achieve goals on its own without direct instruction from a human.
- “There’s no businessperson on the planet at an enterprise of any size that isn’t concerned about this and trying to reflect on what’s going to be politically, legally, regulatorily, or ethically acceptable,” said Fuller.
- But then you run into the problem of having to train humans on these new jobs, or leaving workers behind with the surge in technology.
- Efforts to detect and combat AI-generated misinformation are critical in preserving the integrity of information in the digital age.
Socioeconomic Inequality as a Result of AI
In fact, AI algorithms can help investors make smarter and more informed decisions on the market. But finance organizations need to make sure they understand their AI algorithms and how those algorithms make decisions. Companies should consider whether AI raises or lowers their confidence before introducing the technology to avoid stoking fears among investors and creating financial chaos. The financial industry has become more receptive to AI technology’s involvement in everyday finance and trading processes. As a result, algorithmic trading could be responsible for our next major financial crisis in the markets.
Are optimists the realists?
Once AI can improve itself, which may be not more than a few years away, and could in fact already be here now, we have no way of knowing what the AI will do or how we can control it. Some recent AI progress may be overlooked by observers outside the field, but actually reflect dramatic strides in the underlying AI technologies, Littman says. One relatable example is the use of background images in video conferences, which became a ubiquitous part of many people’s work-from-home lives during the COVID-19 pandemic. Eric Horvitz, chief scientific officer at Microsoft and co-founder of the One Hundred Year Study on AI, praised the work of the study panel. Some suggest self-aware AI may become a helpful counterpart to humans in everyday living, while others suggest that it may act beyond human control and purposely harm humans.
The biggest and most obvious drawback of implementing AI is that its development can be extremely costly. One estimate says that the cost for a fully implemented AI solution for most businesses ranged from $20,000 to well in the millions. Similarly, using AI to complete particularly difficult or dangerous tasks can help prevent the risk of injury or harm to humans. An example of AI taking risks in place of humans would be robots being used in areas with high radiation. Humans can get seriously sick or die from radiation, but the robots would be unaffected.
That has enabled better web search, predictive text apps, chatbots and more. accounting equation wikipedia Some of these systems are now capable of producing original text that is difficult to distinguish from human-produced text. A report by a panel of experts chaired by a Brown professor concludes that AI has made a major leap from the lab to people’s lives in recent years, which increases the urgency to understand its potential negative effects. The limited experiences of AI creators may explain why speech-recognition AI often fails to understand certain dialects and accents, or why companies fail to consider the consequences of a chatbot impersonating historical figures. If businesses and legislators don’t exercise greater care to avoid recreating powerful prejudices, AI biases could spread beyond corporate contexts and exacerbate societal issues like housing discrimination. Speaking to the New York Times, Princeton computer science professor Olga Russakovsky said AI bias goes well beyond gender and race.
To make matters worse, AI companies continue to remain tight-lipped about their products. Former employees of OpenAI and Google DeepMind have accused both companies of concealing the potential dangers of their AI tools. This secrecy leaves the general public unaware of possible threats and makes it difficult for lawmakers to take proactive measures ensuring AI is developed responsibly. It’s crucial to develop new legal frameworks and regulations to address the unique issues arising from AI technologies, including liability and intellectual property rights. Legal systems must evolve to keep pace with technological advancements and protect the rights of everyone.