A particular artificial intelligence system or a specific AI application can produce output that appears accurate or reliable but is not real or based on data. This is called hallucination and it is common in generative AI applications that use natural language processing or NLP and based on large language models. This phenomenon has also been observed in other AI concepts and technologies such as practical machine learning processes and computer vision.
Explaining Hallucinations in Artificial Intelligence: The Causes and Effects of AI Hallucination
Hallucinations in AI are a serious problem. It makes an AI system or a specific AI algorithm and AI model unreliable for practical applications. The phenomenon also creates trust issues and can affect the public acceptance of AI applications such as chatbots or their specific use cases and other generative AI applications. A hallucinating AI system also endangers the operations of organizations dependent on an artificial intelligence strategy.
Causes of Hallucinations in Artificial Intelligence
The causes of AI hallucination can be pinned down to either the quality of the AI model of a particular AI system or a mismatch between the AI system and its operator. This phenomenon also represents one of the challenges in artificial intelligence, the problem in developing and deploying large AI algorithms and AI models, and a notable drawback of generative AI applications. The following are the known causes of AI hallucination:
1. Outdated or Low-Quality Training Data
A particular AI model is trained on data. Part of the capabilities and performance of this model are dependent on the quality and quantity of its training dataset. However, if the entire dataset is outdated, incomplete, or inaccurate, the model can produce irrelevant outputs. Some of these outputs might even be inaccurate or provide incorrect information. This has been observed in the earlier versions of ChatGPT which were based on an older large language model that was trained on a dataset that contain information no older than 2022.
2. Incorrect Classification or Labeling of Data
Part of training an AI model is data classification or data labeling. This is important in organizing data and assigning meaning or context to the data. Labeling enabled machine learning or deep learning algorithms to learn from the entire dataset and make accurate predictions. However, if labels are incorrect, inconsistent, or ambiguous, the model may confuse or misinterpret the data and generate outputs that are inaccurate. An example would be an image recognition software that produces incorrect outputs because of mislabeled images.
3. Factual Errors or Biases in the Training Data
It is important to note that a specific AI model can also inherit the issues of its training dataset. Remember that its capabilities are determined by the data in which it was trained. A dataset with factual errors, inconsistencies, or biases would produce misleading outputs. The model can even reinforce or amplify these errors and biases in its outputs. The propagation of disinformation across the web poses a threat to AI modeling and other non-commercial AI projects based on open-source and publicly available training data.
4. Insufficient Programming or Algorithms
Another possible cause of AI hallucination is the quality of programming and algorithms used. Take note that AI models need proper programming and algorithms to process and understand the data. Flawed or error-riddled programming or an insufficient or faulty algorithm could result in a deployed model failing to analyze and learn from training data or interpreting user-provided inputs that would further result in generating outputs that are either nonsensical or not representative of the dataset it was trained.
5. Lack of Context or Failure to Understand Context
Generative AI applications depend on user-provided inputs or instructions called prompts. A user might provide inadequate prompts that are either too generalized or vague. This could result in a particular model generating undesired output. Other models might also struggle understanding context or colloquialisms, slang expressions, or various form figures of speech that are used in natural conversations due to the quality of their training. This could result in misinterpretation or literal interpretations that could generate undesired outputs.
Effects of Hallucinations in Artificial Intelligence
The main effect of AI hallucination is the generation of an outcome that appears reliable and confident. This means that an AI system makes things up to respond to a human-provided prompt. Companies such as OpenAI define hallucinations as the tendency to invent facts in moments of uncertainty or a flaw in the logical processes of an AI model. The following are the specific examples of the effects of AI hallucination:
1. Deception and Spread of Wrong Information
Usage of chatbots such as ChatGPT, Bing Chat or Copilot, and Google Gemini have exploded in popularity beginning in 2023. They tend to be faster and more convenient at answering questions or in providing specific information than traditional web search engines. However, because these generative AI applications are based on large language models that were trained on large datasets, they are still prone to hallucinations. This can lead to the dissemination of wrong information and can endanger individuals dependent on chatbots.
2. Quality Issues and Performance Degradation
The phenomenon affects the quality and performance of artificial intelligence systems. This also affects the practical applications of these systems. For example, if a system generates outputs that are either nonsensical or irrelevant despite appearing sensible or reliable, these outputs are rendered unusable. Producing inconsistent or contradictory outputs can undermine the reliability and credibility of the system. These can lower the satisfaction of users, impede widespread adoption, and negatively affect the reputation of artificial intelligence.
3. Emergence of Legal and Ethical Issues
Hallucinations in artificial intelligence systems can also raise legal and ethical issues. Remember that the phenomenon can impede the widespread adoption of an AI system or affect the overall reputation of artificial intelligence. Some organizations might ban the use of systems if instances of hallucinations become widespread or if developers fail to address the problem and keep their systems from hallucinating. The phenomenon also affects the credibility of organizations or professional fields that utilize AI systems in their operations.
4. Can Have Minor and Major Repercussions
The effects of AI hallucination can have minor and major impacts. Examples of minor repercussions include damage to the reputation of professionals who use generative AI applications in their line of work and loss of public confidence in AI. More serious repercussions can lead to financial losses in organizations dependent on AI systems and endanger the lives of individuals such as in the case of incoherent computer vision in self-driving vehicles or flawed medical diagnoses in AI-assisted healthcare services.
