Over the last few decades, AI has made significant advancements and become an integral part of our global society both online and off. Since its inception during the 1980s, Machine Learning programs have slowly infiltrated everything in our economy from finance to robotics, and has exploded even more with the advent of the internet and Deep Learning. 

However, as these systems become more common, their impact on ethics has also become an increasing concern as humans still don’t understand the full-scale implications of Artificial Intelligence (AI). Sensitive industries like healthcare and criminal justice are both examples where AI can be largely beneficial while also majorly concerning with serious consequences that can be the result of faulty AI systems. 

The Evolution of AI and Associated Ethical Concerns

AI as a concept first began millenia ago with humans imaging the idea of automation, but it wasn’t until the middle of the 20th century that those ideas were able to be applied to current technology after Alan Turing established the Turing Test and the Dartmouth Convention occurred both in the 1950s. However, following brief advancements in natural language processing models and industrial robotics, the study of AI took a downfall as modern technology wasn’t strong enough to support further experimentation in the field. 

Fortunately, by the 1980s, advancements in computer technology allowed scientists to develop more sophisticated algorithms using newly conceived methods called Machine Learning. Over the next two decades, these advancements grew exponentially with the internet and Deep Learning, creating massive amounts of data for new models to be trained on. 

As these new, more advanced AI models became more common though, new ethical dilemmas began to arise as people became concerned about how private data was being collected and used. An overreliance on AI models soon emerged as big data became more valuable, causing bias to infect datasets, amplifying issues in hiring practices and credit applications based on discriminatory data that was mistakenly implemented into commercial-ready products. 

Understanding AI Ethics

Ethical AI pertains to the implementation of human morals into the design and deployment of AI models so that they can properly evaluate our societal structures during use which is necessary for AI chatbots and many other AI services that focus on human interaction or impact human lives. 

When discussing these ethics, it is important to note the difference between ethical AI and responsible AI. Ethical AI is meant to train models into understanding the differences between what humans consider right and wrong, an already hotly debated topic that is as old as civilization itself. Responsible AI on the other hand focuses on the practical use of AI and how it is operated. By identifying the differences between how and why an AI model acts helps developers create more intricate algorithms that mitigate unwanted bias. 

Key Ethical Concerns in AI Development

While AI shows considerable promise in nearly all sectors of our economy, there are still many ethical factors that need to be considered. They include:

• Bias and Fairness: An AI model can only be as good as its training data and if that data contains unnoticed bias it will be amplified by the algorithm and create poor results. 

• Privacy and Data Security: Data collection is a huge concern to many as they do not want their information being gathered and used for AI products. 

• Transparency and Accountability: AI models house their decision-making process within a black box and makes it difficult for developers to understand its reasoning. 

• Job Displacement: Many fear that AI will replace jobs to an extreme degree and want to mitigate that possibility to keep people employed. 

• Safety and Control: Autonomous decision-making comes with the inherent fear of making poor choices that can harm humans.

Importance of Ethical Consideration for Businesses

Businesses that want to use AI must also take in serious consideration to avoid many of the potential problems that AI can create while also remaining successful. News spread fast on the internet and companies that use AI wrongly will receive significant backlash to their reputation. 

Lawsuits are another cause for concern as talks of regulations become more prevalent around the world. Now that new legislation is being considered, there are clear possibilities that businesses can be caught using AI for the wrong reasons and subjected to fines and other implications that could harm their business. Although unethical AI practices can help businesses in the short-term before regulations and laws are developed, these methods will need to be abandoned soon before new legislation creates larger legal problems. 

Steps to Ensure Ethical AI Development

Despite the potential for misuse, there are clear steps that businesses can take to avoid ethical harm and instill a sense of trust among their consumers in regards to their AI use:

1. Incorporating diverse teams: Because bias training data can be difficult to identify, it is helpful to create teams that are diverse and can spot faulty data more easily. 

2. Open-source AI guideline: By using open-source AI guidelines, businesses can follow universally accepted ethical guidelines for AI. 

3. Continuous monitoring: Self-learning models can experience a drift in their decision-making based on new information that can be prevented with consistent monitoring. 

4. Encouraging collaborations: Responsible AI development and integration relies on more than just programmers, interdisciplinary teams provide a more comprehensive understanding of ethics to help keep AI models safe. 

Case Studies

There have already been many examples of AI being used incorrectly such as Amazon’s recruitment mishap in 2018 when biased resume data caused the algorithm to discard applications and resumes sent in by females. It was unclear at the time, but the tech industry’s male dominance played a significant role in creating biased training data. 

While Amazon did not have any intention of hiring men over women, the event displayed a clear issue within AI training that needed to be solved. However, many other models, like Google’s DeepMind, have been used to focus on AI ethics and create clear guidelines for its use.