Ethical Issues with AI for Digital Product Development

The Ethical Issues of AI in Product Development

The ethical implications of AI are as varied as its uses. Consequently, a comprehensive list is difficult to draw up. In this article, we focus on the main ethical considerations in AI product development, explaining what they are and why they raise concerns.

Bias and Discrimination Issues

One of the main ethical concerns regarding products that incorporate AI-driven decision-making is possible bias. While artificial intelligence itself is designed to be objective, we need to remember how it is trained – based on already existing data. And this is exactly what can lead to discrimination.

Imagine introducing an automatic recruitment system which scans resumes and runs the whole hiring process, with human input introduced only for the last step, the interview. The rest of the decisions are based on data analysis — what could go wrong?

A lot of things. For instance, women might be eliminated at the start when inputting their expected salaries. The ongoing gender pay gap is visible in historical data; data that the AI system would have been trained on. According to the AI’s information, these applicant’s salary demands would appear too high. Similarly, historical data may have discrepancies based on race, disability, sexual orientation, or any other area of difference — as training data, an AI will accept such data at face value.

Such problems could occur in any field – banking, digital marketing, sales, warehouse management… As long as the data itself isn’t fully objective, the AI-driven decision-making risks being flawed and discriminatory.

Data Security and Privacy Issues

AI and data management have become an inseparable combination. While artificial intelligence may help protect stored data, it requires vast amounts of it in the first place. This means that the potential for breaches is generally higher.

The main ethical concern here regarding AI is whether it will indeed prove to protect data effectively and whether it will be safe from breaches and cyberattacks. Especially since the use of AI for data protection creates an additional potential access point to the data – the AI model itself. This is not the only issue.

Another security consequence of using AI is the limited privacy of the users. Businesses require as much data as possible to create an effective AI-driven system. This leads to them requesting and collecting more and more user data, making them less and less anonymous in the digital world.

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Transparency and Accountability Issues

Among the most critical ethical concerns regarding AI development are transparency and accountability.

Imagine you use a GenAI-driven product to create content. That content then turns out to be too similar to the content and data used to train the AI, which was copyrighted. Your ‘new’ content is now in breach of copyright and may be flagged as plagiarized. Who should be held responsible? How can you prevent that from happening in the future?

Such situations may also occur in AI-driven decision-making, with the generative AI drawing too narrowly or closely on its training dataset. This can be especially dangerous. After all, it’s often impossible to point out who is at fault when a problem occurs, and the users themselves are usually not knowledgeable about how particular AI models work.

Sustainability Issues

Sustainability is one of the most overlooked aspects of AI. While entirely digital, AI requires physical infrastructure, creating a major ethical issue.

The more complex the model is, the more data it is based on. The more data it is based on, the bigger the data center capacity it requires. This means increased consumption of electricity, which is still all too often produced from non-eco-friendly sources, such as coal power plants.

This particular concern will probably lose significance over time as ‘clean’ energy sources are increasingly adopted, but currently, it is a critical issue and difficult to avoid.

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How do Institutions Address the Ethical Implications of AI?

With so many ethical implications regarding AI, it is unsurprising that many institutions wish to regulate it. Governing and legislating bodies, such as the EU, aim to protect their citizens from these potential problems.

• EU – Many regulations affect AI in the EU, from the newest AI Act698792_EN.pdf), to the GDPR and EDPB guidelines on automated individual decision-making and profiling for the purposes of regulation. One of the most important ethical concerns tackled is bias in AI decision-making — it is forbidden to make legally binding decisions based purely on automated processing.
• US – The United States is still working on its AI legislation, but as The New York Times reports, there are intensive efforts to put such legislation in place.

How to Tackle AI Ethical Issues in Digital Product Development?

To ensure that your digital products address the ethical implications of artificial intelligence, you must embrace ethical AI product design and development. This requires undertaking a few steps:

• Clear data – To avoid bias and discrimination, you need to ensure that the training data is unbiased. Consider the socio-economic and historical impact of the existing information and clear any data that might not be objective due to the circumstances in which it was gathered. You can also consider eliminating categories from AI analysis that could cause such bias (e.g. exclude gender from the model).
• Prepare information on how the AI model works – This way, your product will be transparent, providing users with information on how and why the AI makes certain decisions.
• Agree on the ethics and monitor the model – Finally, you need to create your own in-house set of ethics to be followed by the AI and observe whether the model follows them. With machine learning, it’s possible that bias may emerge later in development. You need to constantly evaluate your AI model, identifying ethical issues whenever they occur, and solving them.

The Takeaway

There are many ethical considerations, issues, and concerns when it comes to AI itself, and so it is with the development of AI-driven products. Citizens currently receive limited legal protection, with the US government still working on proper legislation, so this responsibility lies with product owners and developers. However, with the right approach to choosing training data and then monitoring AI performance, it is possible to create digital products that use AI ethically.