With an impact on a wide variety of fields, artificial intelligence is expanding rapidly. Processing AI systems are reaching a significant size with the amount of computing power it requires and its data. While AI can have many benefits, the expansion of artificial intelligence still has risks and raises ethical concerns.
Risks of Artificial Intelligence
Due to the scalability of artificial intelligence, concerns now range from mass surveillance by the government to potential bias. AI ethics attempts to balance these concerns by creating a set of values and principles to follow in the development of AI technology. One of the main functions of AI ethics is to investigate this technology that continues to make a more significant impact in day-to-day human life. The main risk factors of AI include the impact on the environment, mass government surveillance, and the perpetuation of bias in large language models.
Negative Environmental Impacts
AI consumes a massive amount of energy to operate. This creates carbon dioxide. For instance, Google’s Transformer emitted over 284 tons of carbon dioxide in a year. This is 57 times more CO2 than a human releases into the environment. The ever-increasing cost of use creates risks for communities likely to experience the negative impacts of climate change. When using AI technology, one must consider these concerns and how it has an impact on the entire world.
Mass Government Surveillance
While AI surveillance can keep people safe, it can also pose threats if it’s used by overreaching governments. Mass surveillance is a privacy concern. Facial recognition isn’t foolproof. When a government conducts mass surveillance, they overreach against their civilians. These troubling trends only highlight the risks of AI in the hands of a corrupt government.
Invisible Bias – What AI Data is Secretly Telling You
Another major concern of AI technology use is the tendency to perpetuate bias, especially within the training set of data. AI in machine learning form must extensively use statistics. Bias will occur if an estimation is incorrect by not matching the true quantity.
Bias can occur in countless number of ways. For example, in an attempt to understand voters’ preferences, a political pollster may use a poll but will only get responses from those willing to talk with poll takers. This is also known as response bias. Ultimately, this creates an inaccurate poll in determining voters’ preferences for a specific candidate. It doesn’t represent the preference of the much broader population.
The propagation of text that repeats biases in society is numerous. For example, there are algorithms that classify human faces into an “attractive” or “unattractive” category. The data from these generative algorithms can be used to reproduce a narrow formula of what’s considered attractive to create a specific aesthetic while excluding other appearances.
The rapid expansion of the data and an increase in scale obscures the composition. This makes it difficult to identify existing bias in the data. These programs are also generative. This means that they are rapidly creating technological artifacts, like the automatic creation of writing. Bias can often be replicated in these artifacts. If this is the case, this will amplify the entire process and further expand such biases.
If any of the data collected by AI contain biases, the system will further amplify these biases in the generated output. The fundamental issue with this problem is scale. Due to their massive size, these training sets can be difficult to properly document or remove bias, for instance. In short, these large language models often encode and reinforce biases that can harm marginalized populations.
Combating the Risks of AI
Although there are many risks of AI, there is also hope that it can play a key role in solving some of society’s greatest problems. Although using technology does have carbon emissions, with further advancement, AI technology has the potential to combat climate change by helping to regulate sustainability systems.
Despite the bias that can result from AI algorithms, there is still potential for machine learning to uncover hidden biases. For example, it can track whether job applicants are receiving offers based on their ethnicity and gender. If this becomes the case, inequity can significantly reduce the chance of minority groups from receiving job offers. This can all be uncovered with the help of machine learning technology.
Closing Thoughts
Artificial intelligence has a dual nature – it can amplify the spread of false information or help people filter through false information. Regardless, it will continue to make rapid changes and impact countless industries. Understanding how to manage the good and bad of AI technology is key to handling AI ethical issues. The social and ethical issues of artificial intelligence will continue to pose significant concerns. People across the world will need to work together in harnessing this innovative technology, while also looking for ways to avoid the many risks of artificial intelligence.
