In the wake of modern advancements, Artificial Intelligence (AI) has taken the world by storm. ChatGPT, an AI chatbot, has emerged as a prime example of the wonders and perils of AI. While AI tools promise efficiency and advanced capabilities, they come with a risk of amplifying existing biases.
The gender bias in AI is an eye-opener
Lensa AI, a popular avatar creation app, has shown how gender biases can be inadvertently baked into software. The app hypersexualizes female avatars, while male avatars are designed as warriors or astronauts. However, the issue is not confined to visual representation. ChatGPT-4, despite OpenAI’s risk assessment, has been observed to produce text that perpetuates gender stereotypes. In a hypothetical scenario where the chatbot was asked to craft a story about a boy and girl choosing university subjects, it depicted the boy choosing science and technology, whereas the girl pursued fine arts, emphasizing traditional gender roles.
How AI learns and decides
AI systems rely on supervised or unsupervised machine learning models. While supervised models allow for human oversight during the learning process, unsupervised models learn autonomously. These models can be unpredictable as developers mainly spot errors during the output stage.
AI’s interpretation of gender
Data used in training AI systems goes through a labelling process, wherein data annotators categorize data based on given guidelines. Biases can be inadvertently introduced if annotators have personal biases or if the guidelines are inherently biased. Consequently, AI systems trained on such data may produce outputs that are skewed or biased.
Beyond Lensa AI, other AI gender bias examples
It’s not just avatar apps that manifest gender biases. A significant instance was when Apple’s credit card algorithm was revealed to favor male clients by offering higher credit lines, even when some had comparable or worse credit histories than female clients.
Reflection of societal biases in AI
The inherent biases in AI systems mirror the societal biases of their creators. AI isn’t purely objective; it can inadvertently perpetuate pre-existing stereotypes and biases if not designed with a critical, unbiased approach.
The importance of diversity in AI development
Diversity is paramount. The software development industry, with a mere 8% of women, is lacking in diversity. To reduce biases in AI, it’s crucial to have diverse development teams that can provide a wide range of perspectives, minimizing the scope of unconscious biases creeping into the AI systems.
Organizations’ awareness and steps towards mitigation
Many tech companies are cognizant of AI biases, thanks to documented research and analyses by civil society. Some organizations have taken preliminary steps to address the issue, though the measures and their efficacy remain ambiguous. A well-rounded approach would be to integrate gender and tech experts within teams, ensuring that AI tools are built within an ethically sound framework.
As AI continues to play a pivotal role in modern society, addressing inherent biases becomes paramount. The challenge lies not just in the development of AI tools but in their ethical and unbiased application. Ensuring that AI mirrors the best of humanity, rather than its prejudices, is a responsibility that the tech industry and society at large must shoulder.
