Artificial Intelligence (AI)

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Artificial intelligence (AI) refers to the capacity of computers or other machines to perform tasks that typically require human intelligence such as reasoning, problem-solving, and decision-making. AI systems use algorithms and computational techniques to process large volumes of data, extract patterns, and make predictions or decisions based on those patterns.

Generative artificial intelligence is a specific subset of AI focused on creating content such as text, images, video, music, and other outputs in response to user input (or prompts). Generative AI models are designed to learn the patterns and structure of their input training data and generate new data with similar characteristics. Because generative AI tools can quickly and easily generate a wide variety of human-like outputs, they have the potential to radically transform the way we approach content creation across a wide range of domains and industries. However, because AI outputs are derived from undocumented data sources, infringe on intellectual property, and are prone to error, they are also subject to a number of important limitations. (see section below)

Prompt refers to the text commands users communicate to AI tools in order to generate a response.  Adjusting how your prompt is phrased can provide a different response from the AI.

Despite their broad potential, generative AI models also have several important limitations. Understanding these limitations is critical for using these technologies ethically and effectively.

Ethical Concerns

• Bias and Fairness: Generative AI models can learn biases present in the training data, producing outputs that reflect, reinforce, or amplify social prejudices and stereotypes.
• Misinformation and Manipulation: AI-generated content can be used to create convincing fake news, deepfakes, and other forms of misinformation, leading to potential manipulation and harm.
• Plagiarism and Copyright: The use of AI-generated content raises significant questions about authorship, intellectual property, and attribution, potentially leading to issues with plagiarism and copyright infringement.
• Attribution and Accountability: Determining responsibility for AI-generated content can be challenging, raising questions about who is accountable for errors, biases, or malicious outputs.
• Inequality: As AI providers move from free to fee-based service models unequal access to these tools could exacerbate existing global inequalities.

Related reading: Artificial intelligence (AI) bias impacts: classification framework for effective mitigation.

Quality and Reliability

• Quality: AI outputs may contain false, misleading, or inaccurate information.
• Consistency: Generative AI models can produce irrelevant or inconsistent results, even in response to the same prompt.
• Superficiality: While AI can generate content, it might lack true creativity, originality, and deep understanding of complex concepts.
• Degeneration: As AI-generated content fills the internet and becomes the source data on which future generations of AI are trained, the quality of AI output may degrade over time leading to "model collapse".

Related reading: In AI We Trust: Ethics, Artificial Intelligence, and Reliability.

Data Privacy and Security

• Data Exposure: The training of generative AI models requires large datasets, which could contain sensitive or private information that might be inadvertently revealed in generated outputs.
• User Privacy: AI platforms may collect and retain personal data that could be used for purposes other than what was originally intended or disclosed to the user.

Related reading: When AI Meets Information Privacy: The Adversarial Role of AI in Data Sharing Scenario

Energy Consumption and Environmental Impact

• High Computational Demands: Training and running large-scale generative AI models can require significant computational resources, leading to high energy consumption and environmental impact.

Related reading: Towards the Sustainability of AI; Multi-Disciplinary Approaches to Investigate the Hidden Costs of AI

Human Dependency, De-skilling, and Displacement

• Dependency: Overreliance on AI-generated content might lead to reduced human skills and expertise, impacting critical thinking and creativity.
• Loss of Traditional Skills: Traditional skills like research, writing, and content creation might diminish as these tasks are taken over by AI.
• Job Displacement: AI could potentially displace large segments of the workforce by automating tasks once performed by humans.
• Labor Exploitation: AI systems rely on millions of low-paid workers around the world (particularly the Global South) who review, evaluate, and annotate AI outputs for quality assurance.

Related reading: Automation, AI and Work