Understanding AI ethics: What every professional needs to know

In January 2025, for example, the book-tracking app Fable drew controversy when its AI generator produced insensitive annual summaries for readers. Meanwhile, in December 2024, Italy ordered OpenAI to pay a €15 million penalty for data privacy violations.

These real-world examples reflect broader concerns about the ethical implications of AI in the workforce. According to Multiverse’s The ROI of AI report, 36% of workers believe their organization lacks responsible and ethical AI practices. Despite this, 93% feel confident they have used this technology ethically. But without the proper training, many people fail to recognize how AI can be misused.

This article explores key AI principles and emerging ethical dilemmas. We’ll also share practical strategies for upskillers who want to learn how to follow AI ethical frameworks in the workplace.

What is AI ethics?

As Dr. David Leslie of the Alan Turing Institute explains, “AI ethics is a set of values, principles, and techniques that employ widely accepted standards of right and wrong to guide moral conduct in the development and use of AI technologies.”

Many organizations have created AI codes of ethics to help developers and other professionals use this technology responsibly. These guidelines outline AI ethical principles and may include industry-specific standards.

For example, the Market Research Society released an AI ethics guide urging practitioners to “prioritize and safeguard participants’ privacy and data rights” when using AI in research projects. Similarly, the Institute and Faculty of Actuaries and the Royal Statistical Society co-published a guide to ethical data science that requires members to “maintai[n] human oversight of automated solutions,” including AI systems.

While these principles might seem abstract at first, they can help professionals recognize and address ethical concerns. Here are a few scenarios that workers may encounter:

• A Human Resources Specialist notices that AI-powered resume screening software consistently filters out women.
• A Data Scientist is asked to analyze confidential customer data without obtaining consent.
• A marketer uses a text-to-image generator to create visual content for social media, but the images misrepresent the products.

An AI ethics code empowers professionals to make moral decisions that prioritize human interests and minimize risks.

Key principles of AI ethics

There’s no universal framework for AI ethics. But a few foundational tenets appear consistently in guidelines from professional organizations and government agencies.

Transparency and accountability are two central ethical principles for AI. According to the Ada Lovelace Institute, businesses should practice transparency by creating clear data-sharing agreements and publishing spending data. Impact assessments and audits also promote accountability.

Fairness is another key part of AI ethical guidelines. AI systems should be designed to avoid discrimination against any communities or individuals. For example, the Microsoft Responsible AI Standard requires AI developers to “minimize the potential for stereotyping, demeaning, or erasing identified demographic groups, including marginalized groups.” This process involves ongoing bias checks and collaborating with members of diverse demographic groups to understand how AI tools impact them.

Additionally, respect for human dignity and autonomy are core AI moral principles. For instance, the Council of Europe requires member states to “ensure that activities within the lifecycle of artificial intelligence systems are fully consistent with human rights, democracy, and the rule of law.”

Privacy and data protection are two more guiding principles for AI development and usage. The UK General Data Protection Regulation requires businesses to only collect personal data for specified and legitimate purposes. Companies must also keep this information secure and delete it when they no longer need it.

Ethical challenges in AI

While artificial intelligence offers many benefits, it also has several troubling ethical implications. You may encounter these common challenges while designing or using AI tools.

Algorithmic bias

Without careful oversight, algorithmic systems can unintentionally reinforce unconscious biases. This often occurs when businesses train AI models with incomplete or unrepresentative data sets, leading to selection bias. Additionally, AI systems can cause harm by treating certain groups differently.

For example, a 2024 lawsuit against SafeRent alleged that the platform used a biased algorithm to score rental applicants. The algorithm didn’t factor in housing benefits and weighed credit information too heavily, leading to discrimination against low-income applicants and people of color.

Lack of transparency

Many companies have developed “black box” AI systems that don’t explain how they operate. For example, when you use ChatGPT, you see your inputs and outputs, but what happens in between is a mystery.

This secrecy raises troubling questions about the possibility of incorrect answers and hidden bias. After all, you can’t say for certain that a result is correct and fair if you can’t check the math yourself. As a result, many users mistrust AI technologies.

Major AI platforms also frequently withhold information about how they train their AI models. In 2024, book authors filed a lawsuit against NVIDIA, claiming the company’s AI training data “comes from copyrighted works” that were copied “without consent, without credit, and without compensation.” By refusing to disclose their source materials, AI companies risk violating intellectual property rights and angering human creators.

Tech dominance

When you think about companies developing AI systems, you probably picture tech giants like Amazon and Meta. These mega-corporations have significantly influenced the AI industry, raising concerns about monopolies. Sarah Cardwell, the CEO of the UK’s Competition and Markets Authority, observes that the dominance of a few companies “could shape [AI] markets in a way that harms competition and reduces choice and quality for businesses and consumers.”

Practical steps for professionals to adhere to AI ethical frameworks

Practicing AI ethics involves more than basic steps like not uploading customer data to ChatGPT. It requires a deeper understanding of how these systems work and their ethical considerations.

Stay educated on AI principles

Artificial technologies are evolving at lightning speed, with new applications and tools emerging monthly. Following industry-recognized AI ethics guidelines allows you to stay up-to-date and adapt to new challenges.

Online courses allow you to learn AI guiding principles at your own pace. For example, Multiverse’s AI Jumpstart module provides a foundation for core AI concepts like prompt engineering and machine learning. You’ll also learn to analyze AI outputs for ethical considerations like implicit bias. This flexible training will help you future-proof your career while expanding your technical skills.

Trustworthy resources from industry leaders are another invaluable source of AI training. Professional associations often create AI ethics codes and host educational workshops about new tools. Additionally, a mentor can offer one-on-one guidance when you face ethical issues, such as over-reliance on AI within your organization or using AI tools to create manipulative advertising.

Promote transparency in your work

When it comes to the ethical use of AI, transparency is non-negotiable. Always document your AI workflows clearly so others can review and understand your processes. This might involve making your AI code open-source or explaining how you used Midjourney to design a magazine ad.

This transparency should also extend to your data. Make sure to use diverse data sets that are ethically sourced and labeled. The data should also be free from undisclosed biases, such as the underrepresentation of people from certain age groups or geographic areas.

Invest in continuous skill development

As more businesses embrace artificial intelligence tools, upskilling is key to advancing your career while gaining a deeper understanding of AI ethics.

According to the Multiverse Skills Intelligence Report, 90% of employees want to improve their data skills. Multiverse’s AI for Business Value apprenticeship is an excellent opportunity to strengthen these skills. You'll learn how to use structured data to drive business value while mitigating AI risks.

There are also many informal opportunities to expand your knowledge of AI ethics. Participating in discussions and industry events can empower you to share your perspectives and learn from peers with similar ethical values. For instance, you could join the r/AIethics subreddit or attend events organized by the Institute for AI in Ethics.

Become an ethical AI leader

For centuries, science fiction stories like Frankenstein and Jurassic Park have explored the ethics of technology. With the emergence of AI, these moral dilemmas have become much more pressing and real.

Staying informed and continuously upskilling will enable you to navigate these changes responsibly. Multiverse’s free apprenticeship programs can help you gain practical experience while learning ethical AI practices. Take the next step by applying today.