Another important ethical issue to consider when using AI in UX design is algorithmic bias. AI algorithms are only as unbiased as the data they are trained on, and if this data is biased or biased in any way, the AI system will learn and perpetuate these biases. In addition, these AIs are trained on the Internet, which is undoubtedly full of biases. The use of AI to perpetuate misinformation is another major ethical issue.
Machine learning models can easily generate factually incorrect text, meaning that fake news articles or fake summaries can be created in seconds and distributed through the same channels as real news articles. The need to distribute responsibility is keenly felt when algorithms don't work properly. Unethical algorithms can be thought of as malfunctioning software artifacts that don't work as intended. There are useful distinctions between design errors (types) and operating errors (chips), and between not working as intended (dysfunction) and the presence of unwanted side effects (malfunction).
Malfunction is distinguished from simple negative side effects by “avoidability”, or the extent to which comparable types of systems or devices perform their intended function without the effects in question. These distinctions clarify the ethical aspects of AI systems that are strictly related to their operation, either abstractly (for example, if we analyze gross performance) or as part of a larger decision-making system, and reveal the multifaceted interaction between expected and actual behavior. Machine learning, in particular, poses unique challenges, because achieving the desired or “right” behavior does not imply the absence of errors or harmful actions or feedback loops. When we talk about ethics in AI, we focus more on the potential use cases and negative repercussions of AI, but in reality, AI is doing a lot of good. The United Nations (UN) has also developed a Framework for Ethical AI that analyzes how AI is a powerful tool that can be used forever, but risks being used in ways that are incompatible with and contrary to UN values.
A previous review of the ethical challenges faced by AI has identified six types of problems that can be attributed to the operational parameters of decision-making algorithms and AI systems. By determining what the main ethical concerns of AI are, looking at examples of ethical AI, and considering best practices for using AI ethically, you can ensure that your organization is on the right track to use AI.