Exploring Ethical Frontiers: AI and Its Impact

Profit is a key driver in the decision-making process for the vast majority of businesses. When a new opportunity arises, assessing its potential to increase the business’s profits is central to determining if it will be embraced. However, profit isn’t the only factor businesses must weigh. An opportunity’s potential impact on people is also important since that is where business ethics comes into play. Ethics seek to ensure business behavior is both profitable and positive for people.

The rise of artificial intelligence and the potential it brings to the business world has sparked several key concerns regarding business ethics. Many experts argue that technological concerns are just one side of the coin when it comes to developing and deploying AI. To be responsible, those exploring and leveraging the power of AI must also be mindful of its ethical implications.

Ethical concerns surrounding data privacy

Collecting data has become a core component of doing business in the digital age. Most businesses collect and store not only personal identifying information on customers but also data on their customers’ activity. Protecting that data from unauthorized access and misuse is an ethical responsibility that, in many cases, is also a regulatory obligation.

The adoption of AI in the business world has increased the ethical concerns surrounding data privacy. Training and developing AI requires vast amounts of data. In some cases, this has led businesses to collect more data. In others, it has led to data being repurposed to assist in AI training. Overall, the increased demand for data has resulted in an increased risk of privacy violations.

The ethical debate surrounding data privacy is focused on the steps businesses should take to collect and safeguard data. Most businesses agree that ethics demand they protect data against breaches, though whether or not businesses should utilize customer data for AI training is an emerging ethical debate.

Ethical concerns surrounding bias and fairness

AI’s potential to perpetuate biases has emerged as one of the primary ethical concerns surrounding its use. AI learns from the data upon which it is trained, so if the data contains biases, they can be perpetuated and amplified by AI-driven platforms, leading to discrimination, social feedback loops, and other damaging outcomes.

A simple example of the issues AI bias can cause is found in the training of AI for face-recognition applications. If data used for training doesn’t include a broad representation of races and genders, the resulting AI can cause problems for underrepresented groups, as when Facebook’s AI labeled some people as “primates”. Recent research shows that people who interact with biased AI unconsciously absorb these biases, long after using the AI in question. More serious repercussions could result if similar discriminatory biases are inherent in the training used for AI platforms that assist with hiring or financial lending.

Using AI-driven platforms to assist with healthcare diagnoses is another example of an application where biases can result in dangerous consequences. If the training protocols used to develop AI algorithms do not address biases, diagnoses can be skewed in ways that reduce the rate of accuracy for certain demographics. If the skewed findings are relied upon, the treatments prescribed to patients can result in serious harm.

An ethical approach to AI requires that biases be identified and addressed. Ideally, biases will be caught in training data and corrected before they are passed on to AI. Removing them from the machine learning models that result from training is a more difficult task than protecting against their insertion.

Ethical concerns surrounding accountability in AI

ChatGPT, which is now just one of a growing number of AI-driven chatbots, has over 100 million weekly users who pose more than 10 million queries per day. The potential for its answers to be factually incorrect or misleading is well known. In fact, the term “AI hallucination” has been coined to describe these responses.

A key ethical question: Who is accountable for those wrong answers, especially if they result in harm or loss? If an AI-driven platform provides an incorrect diagnosis for a medical condition, who is responsible — the doctor involved, the company that developed the AI platform, or the company that trained it? The ethical approach requires someone to take responsibility for the problems that flow from the use of AI.

Providing adequate transparency in the development of AI is an ethical issue closely related to accountability. The rationale behind AI’s decision-making is often unclear, even to its developers, and this “black box problem” makes it difficult to identify the cause of biases in AI’s results or assign accountability for the issues they cause.

Ironically, AI can serve as a powerful instrument in promoting ethical practices. The detection and mitigation of biases are now being enhanced by AI-driven software, which aids in identifying and correcting skewed data. Furthermore, AI plays a pivotal role in demystifying the decision-making processes of other AI systems, offering insights into their internal logic. Ethical judgments could potentially be integrated within sophisticated Large Language Models, allowing these systems to weigh ethical considerations in their outputs.

Additionally, AI contributes to the protection of training data through methods like differential privacy, where it fine-tunes the balance of noise addition and facilitates the creation of synthetic data that maintains privacy while being analytically useful. AI may be the cause of, and solution to, many of these ethical issues.

An ethical approach to AI development acts as a catalyst for innovation, ensuring that advancements are sustainable, socially responsible, and aligned with long-term regulatory visions, thereby accelerating progress.

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About Author

Dev Nag is the CEO and Founder at QueryPal. He was previously CTO and Founder at Wavefront (acquired by VMware) and a Senior Engineer at Google where he helped develop the back-end for all financial processing of Google ad revenue. He previously served as the Manager of Business Operations Strategy at PayPal where he defined requirements and helped select the financial vendors for tens of billions of dollars in annual transactions. He also launched eBay's private-label credit line in association with GE Financial.