5 ethical questions about artificial intelligence

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Can AI understand fairness?

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Are you wondering about the ethical implications of artificial intelligence? You’re not alone. AI is an innovative, powerful tool that many fear could produce significant consequences—some positive, some negative, and some downright dangerous.

Ethical concerns about an emerging technology aren’t new, but with the rise of generative AI and rapidly increasing user adoption, the conversation is taking on new urgency. Is AI fair? Does it protect our privacy? Who is accountable when AI makes a mistake—and is AI the ultimate job killer? Enterprises, individuals, and regulators are grappling with these important questions.

Let’s explore the major ethical concerns surrounding artificial intelligence and how AI designers can potentially address these problems.

AI systems can be biased, producing discriminatory and unjust outcomes pertaining to hiring, lending, law enforcement, health care, and other important aspects of modern life. Biases in AI typically arise from the training data used. If the training data contains historical prejudices or lacks representation from diverse groups, then the AI system’s output is likely to reflect and perpetuate those biases.

Bias in AI systems is a significant ethical concern, especially as the use of AI becomes more common, because it can lead to unfair treatment. Biased AI systems may consistently favor certain individuals or groups, or make inequitable decisions.

Designers of AI systems can proactively combat bias by employing a few best practices:

Granted, there’s a lot of subjectivity in determining fairness and bias, and to some degree a generative AI model needs to reflect the world as it is (not as we wish it to be). For today’s models, it’s still a work in progress.

Many artificial intelligence models are developed by training on large datasets. That data comes from a variety of sources, and it may include personal data that the data owners did not consent to provide. AI’s heavy appetite for data raises ethical concerns about how the data is collected, used, and shared.

Data privacy and protection are generally not enhanced by AI systems. When developers store and process large datasets that may be attractive to scammers, it boosts the risk of data breaches. The data can be misused or potentially accessed without authorization.

AI systems developers have the ethical responsibility to prevent unauthorized access, use, disclosure, disruption, modification, or destruction of data. Here’s what you can expect from an AI system that prioritizes users’ best interests for their data:

Are today’s generative AI models employing these best practices? With the secrecy and mystique surrounding the latest rollouts, it’s difficult to know for sure.

If you or an enterprise uses a generative AI tool and it makes a mistake, who is accountable for that error? What if, for example, the AI in a health care system makes a false diagnosis, or a loan is unfairly denied by an AI algorithm? The use of artificial intelligence in consequential decision-making can quickly obscure responsibility, raising important questions about AI and accountability.

This accountability problem in AI stems partly from the lack of transparency in how AI systems are built. Many AI systems, especially those that use deep learning, operate as “black boxes” for decision-making. AI decisions are frequently the result of complex interactions with algorithms and data, making it difficult to attribute responsibility.

Accountability matters to build widespread trust in AI systems. AI developers can address issues of accountability by taking proactive measures:

Still, if you’re one of the millions who use ChatGPT, then you may have noticed the disclaimer telling you that the generative AI tool makes mistakes. And it does—so be sure to fact-check all of the information you receive. In other words, the accountable party is you, the user.

Training and operating artificial intelligence models can be highly energy intensive. AI models may require substantial computational power, which can result in significant greenhouse gas emissions if the power source isn’t renewable. The production and disposal of hardware used in AI systems may also worsen the problems of electronic waste and natural resource depletion.

It’s worth noting that AI also has the potential to benefit the environment by optimizing energy usage, reducing waste, and aiding in environmental monitoring. But that doesn’t erase the eco-ethical concerns of using AI. System designers can play a partial role by:

Since the Industrial Revolution, we have been turning fossil fuels into economic growth. But there are associated negative externalities that must be addressed.

You may be paying close attention to artificial intelligence because you’re concerned about your job. That’s relatable! The potential for AI to automate tasks or perform them more efficiently creates a serious ethical concern with broad economic implications.

Enterprises have a moral—if not legal—responsibility to use artificial intelligence in a way that enhances rather than replaces their workforces. Employers who integrate AI and simultaneously provide opportunities for retraining, upskilling, and transitioning employees to new AI-based roles are the enterprises using AI in an ethically defensible way.

The fear that AI will “steal” jobs is real. And it likely won’t be assuaged anytime soon. AI system designers cannot entirely mitigate this risk, but they can use a few tactics to discourage enterprises from using AI in economically disastrous ways. Strategies include:

The ethical deployment of AI is crucial to the economy and all of its participants. When used ethically, AI can support economic growth by driving innovation and efficiency. AI that’s used only to enhance profitability could produce many unintended consequences. As the adoption of artificial intelligence continues, these ethical questions are likely to become more important to all of us.