AI and ethics: 5 ethical concerns related to AI and how to address them

Ethical concerns about an emerging technology are not new, but with the rise of generative AI and increasing user adoption, the conversation takes on new urgency. Is AI fair? Does this protect our privacy? Who is responsible when AI makes a mistake – and is AI the ultimate job killer? Businesses, individuals and regulators are grappling with these important questions.

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

1. Is AI biased?

AI systems can be biased, producing discriminatory and unfair results in hiring, lending, law enforcement, healthcare, and other important aspects of modern life. Biases in AI usually come from the training data used. If the training data contains historical biases or lacks representation of diverse groups, then the results of the AI ​​system are likely to reflect and perpetuate these biases.

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

AI system designers can proactively combat bias by employing a few best practices:

• Use diverse and representative training data.
• Implement mathematical processes to detect and mitigate bias.
• Develop transparent and explainable algorithms.
• Establish or adhere to ethical standards that prioritize fairness.
• Perform regular system audits to continually monitor for bias.
• Participate in learning and improvement to further reduce bias over time.

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

2. Does AI compromise data privacy?

Many artificial intelligence models are developed by training on large datasets. This data comes from various sources and may include personal data that the data owners have not consented to provide. AI’s great appetite for data raises ethical concerns about how data is collected, used and shared.

Privacy and data protection are generally not improved by AI systems. When developers store and process large sets of data that could attract fraudsters, it increases the risk of a data breach. Data may be misused or potentially accessed without authorization.

Developers of AI systems have an 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:

• The AI ​​model collects and processes only the minimum data necessary.
• Your data is used transparently and only with your consent.
• Data storage and transmission are encrypted to protect against unauthorized access.
• The data is anonymized or pseudonymized wherever possible.
• Access controls and authentication mechanisms strictly control access to data.
• Users benefit from as much control over their data as possible.

Do current generative AI models employ these best practices? With the secrecy and mystery surrounding the latest deployments, it’s hard to be sure.

3. Who is responsible for AI decisions?

If you or a company uses a generative AI tool and it makes an error, who is responsible for that error? What happens if, for example, a healthcare system’s AI makes a false diagnosis or a loan is unfairly denied by an AI algorithm? The use of artificial intelligence in making consequential decisions can quickly obscure accountability, raising important questions about AI and accountability.

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

Accountability is important to building widespread trust in AI systems. AI developers can address liability issues by taking proactive steps:

• Follow ethical design principles that specifically prioritize responsibility.
• Define and document the responsibilities of all stakeholders in an AI system.
• Ensure the system design includes meaningful human oversight.
• Engage stakeholders to understand concerns and expectations around AI accountability.

However, if you are one of the millions of ChatGPT users, you may have noticed the disclaimer telling you that the generative AI tool makes mistakes. And it is, so be sure to verify all the information you receive. In other words, you are responsiblethe user.

4. Is AI harmful to the environment?

Training and operating artificial intelligence models can be very energy intensive. AI models can require significant computing power, which can result in significant greenhouse gas emissions if the energy source is not renewable. The production and disposal of hardware used in AI systems can also worsen problems with e-waste and natural resource depletion.

It is worth noting that AI can also benefit the environment by optimizing energy consumption, reducing waste, and facilitating environmental monitoring. But this does not erase the eco-ethical concerns linked to the use of AI. Systems designers can play a partial role by:

• Design energy-efficient algorithms that use minimal computing power.
• Optimize and minimize data processing requirements.
• Choose equipment with maximum energy efficiency.
• Use data centers powered by renewable energy sources.
• Comprehensive assessment of the carbon footprint of an AI model.
• Support or engage in research into sustainable artificial intelligence.

Since the Industrial Revolution, we have transformed fossil fuels into a source of economic growth. But there are associated negative externalities that need to be addressed.

5. Will AI steal my work?

You may be paying special attention to artificial intelligence because you care about your work. It’s relevant! The ability for AI to automate tasks or perform them more efficiently creates a serious ethical concern with broad economic implications.

Businesses have a moral, even legal, responsibility to use artificial intelligence in ways that enhance rather than replace their workforce. Employers that integrate AI while providing opportunities to reskill, upskill, and transition employees into new AI-enabled roles are companies that use AI in an ethically defensible way.

The fear that AI will “steal” jobs is real. And that probably won’t be abated any time soon. Designers of AI systems cannot entirely mitigate this risk, but they can use a few tactics to discourage companies from using AI in economically disastrous ways. Strategies include:

• Develop complementary AI designs that augment human labor rather than replacing it.
• Gradually deploy AI tools in a way that gradually improves workforce efficiency.
• Focus on developing AI tools for tasks that are too dangerous or impractical for humans.
• Actively engage with stakeholders of an AI tool to ensure all perspectives are heard.

The essentials

The ethical deployment of AI is crucial for the economy and all its stakeholders. When used ethically, AI can support economic growth by driving innovation and efficiency. AI used solely to improve profitability could have many unintended consequences. As the adoption of artificial intelligence continues, these ethical questions are likely to become more important to us all.