The Risks of Machine Learning
By Carson Taylor
In recent years, the field of machine learning (ML) and artificial intelligence (AI) has not only captured the imagination of the public but has also become a cornerstone of modern technology. From powering search engines and recommendation systems to managing financial risk and transforming healthcare, AI is omnipresent. However, with great potential for reward comes great risk of peril, and there are significant risks and challenges associated with the development and implementation of these technologies.
Distinguishing AI from ML
Understanding the distinction between AI and ML is important when considering the risk factors. While AI is a broad field that inherits from ML, each has its applications and limitations. AI’s goal is to replicate human cognitive abilities and is explains by its name, intelligence that is artificial, or not human. Without careful oversight, it can lead to unforeseen outcomes, especially when decisions made by AI systems can affect human lives directly. Machine learning is a term which refers to the algorithms and processes which allow machines to learn, and drive tools such as artificial intelligence.
Economic Implications
The intersection of machine learning and economics, as discussed in "The Economics of Artificial Intelligence: An Agenda," is transforming the field. Susan Athey even states, "I believe that machine learning (ML) will have a dramatic impact on the field of economics within a short time frame." However, it brings forth the challenge of ensuring that economic models remain valid and beneficial to all. The data-driven model selection process can lead to the optimization of some groups' outcomes at the expense of others, contributing to social inequality. For example, one of the most prominent and popular risks know to be associated with ML and AI, is job loss, specifically large corporations would rather employ AI, as they do not cost as much, and require less liability. Many jobs have already been claimed by machines and this number will only increase with time.
Practical Applicability and Assumptions
It is still widely considered impractical to deploy artificial intelligence into a number of different fields. The reason is simple, when it comes to very complex algorithms used in machine learning, it can be difficult to pinpoint why AI has come to the conclusion it has. For this reason, the implementation of artificial intelligence is at a standstill in fields where the risk of potentially damaging misinformation produced by AI and ML could be catastrophic. For example, medical diagnosis.
Data Abundance and Privacy Concerns
The article "Machine Learning: Algorithms, Real-World Applications and Research Directions" states that "We live in the age of data, where everything around us is connected to a data source." (Sarker). Data abundance introduces significant privacy concerns and the potential for misuse of data. The sheer scale of data collection and processing poses risks of mass surveillance and individual profiling. For example, many large companies use Artificial Intelligence utilizing machine learning to target advertisements to you based on a number of factors decided by an algorithm. The machines you use could also potentially be learning about your shopping habits, day-to-day schedule, home life, etc. This could potentially lead to unethical business practices and or exploitation.
Ethical and Societal Implications
In "Machine Learning, Explained", Sarah Brown states: "Machines are trained by humans, and human biases can be incorporated into algorithms." This potential for bias outcomes cannot be overlooked. ML algorithms trained on historical data can perpetuate and amplify biases. Any forms of bias, however small, found within an algorithm, can produce biased results. This has profound implications for societal equity and justice, particularly in sectors like law enforcement, hiring, and lending. For example, an AI might potentially discriminate against ethnic sounding names in the hiring process if the programmer implemented such biases, even if it is not purposeful.
The Pervasiveness of Machine Learning
With machine learning and artificial intelligence growing at a rate faster than law makers can control, the risks associated with artificial intelligence and machine learning are far reaching across numerous sectors. Artificial intelligence is readily available and thus, potential misuse is a large concern.
Need for Expertise
Due to the significant risks associated with artificial intelligence and machine learning, it is imperative to public safety that policy makers and people in positions of leadership globally, are well informed about the inner mechanisms of technologies before they are implemented into the public sector, which could have potentially drastic results. The development of such high-capacity machines should also be incubated and restricted until implementation can be fully understood and the machines mechanisms are fully transparent.
In conclusion, while machine learning and AI hold the promise of innovation and efficiency, they are not without significant risks. The concerns range from the ethical and biased outcomes to the opaque nature of algorithmic decision-making and the potential for widespread societal impacts. It is the responsibility of developers, researchers, and policymakers to ensure these technologies are implemented with caution, transparency, and a robust ethical framework. Only by recognizing and addressing these risks can we harness the full potential of AI and ML for the greater good.
References
Athey, Susan. “The Impact of Machine Learning on Economics.” The Economics of Artificial Intelligence: An Agenda, University of Chicago Press, Chicago, Illinois, pp. 507–547.
Sarker, Iqbal H. “Machine Learning: Algorithms, Real-World Applications and Research Directions.” SN computer science vol. 2,3 (2021): 160. doi:10.1007/s42979-021-00592-x
Brown, Sara. “Machine Learning, Explained.” MIT Sloan, 21 Apr. 2021, mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained.
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