Since the inception of Artificial Intelligence (AI), issues of bias have plagued its development. Early AI models, trained on limited datasets that often mirrored societal biases, resulted in algorithms that perpetuated stereotypes and generated unfair outcomes for certain demographics. A notable case from 1988 revealed bias in an AI admissions program used by a UK medical school, which disadvantaged women and applicants with non-European names. In a more recent incident in 2016, Microsoft's chatbot Tay was introduced to Twitter to learn from interactions but quickly began using offensive and racist language, reflecting the negative content it was exposed to online. This event underscored how AI can swiftly absorb detrimental biases from its environment.
Subsequently, companies became more cautious about deploying similar chatbot technologies. However, the landscape changed on November 30, 2022, with the emergence of ChatGPT. Today, we explore the lessons gleaned since Tay's episode regarding biases. Foundation models, such as large language models (LLMs), signify a notable progress in artificial intelligence systems that can process, create, and comprehend human-like language. These models have recently gained immense popularity due to their exceptional accuracy across a wide range of tasks. In addition to text classification, sentiment analysis, machine translation, and answer generation, foundation models also find utility in areas like image recognition, autonomous systems, and creative arts, demonstrating their versatility and significant impact.
At the core of these models lies a fundamental question: where do the billions of parameters, which shape their "comprehension" and outputs, acquire their knowledge? Foundation models undergo training on extensive datasets comprising text, images, and sometimes audio sourced from the internet, books, articles, and other media. This extensive and diverse collection of human knowledge allows them to discern patterns, connections, and contexts, laying the groundwork for their intelligence and functionalities.
Nevertheless, the vast scope of knowledge accessible to these models brings its own set of challenges. The datasets used to train these models reflect biases, inconsistencies, and varying quality inherent in the source materials. These biases can have severe consequences when integrated in a generalized manner into crucial decisions that shape the future of real individuals with their own distinct narratives.
When applied in domains like loan evaluation, job recruitment, law enforcement, healthcare, customer support, social media regulation, and as organizations increasingly turn to AI-generated content and employee selection processes, these biases, even at levels close to ~0.1%, can result in thousands or millions of individuals being unfairly deprived of opportunities.
The evolution of Artificial Intelligence has brought about significant advancements in various fields, thanks to the remarkable capabilities of foundation models like large language models (LLMs). These models have shown exceptional accuracy and versatility across tasks ranging from text classification to image recognition, revolutionizing industries and enhancing user experiences. However, the issue of bias in AI remains a critical challenge that must be addressed to ensure fair and equitable outcomes. As we continue to harness the power of AI in decision-making processes across crucial domains such as finance, healthcare, and law enforcement, it is imperative to prioritize mitigating biases in AI systems to prevent unjust consequences for individuals and uphold ethical standards in the deployment of artificial intelligence technologies.