# Is AI Undermining Equality? Examining Inclusion and Fairness
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Chapter 1: Introduction to AI and Equality
The digital age has witnessed two significant failures: the absence of inclusion and a lack of impartiality. This backdrop makes it unsurprising that Microsoft’s AI bot Tay quickly began spewing racist and inflammatory remarks, while Google’s deep learning system misidentified gorillas and individuals with darker skin tones. Given this context, it seems unreasonable to allow corporations to unilaterally dictate the ethical principles that guide AI technologies.
Considering that more than half of the global population still lacks internet access, it’s clear that this gap will only widen in the realm of AI unless addressed. Research indicates that the absence of inclusion and fairness disproportionately impacts marginalized communities, ultimately benefiting a privileged few. Although various AI ethics committees and organizations aim to promote inclusive applications by establishing guidelines for developers, funders, and regulators, differing interpretations of intelligence, morality, and ethics often favor existing dominant perspectives. Additionally, there are growing concerns that scientists may lose control over AI applications, particularly as many of those creating these systems may not fully grasp their functionalities.
Section 1.1: The Call for Inclusion
According to Sandhya Venkatachalam, the issues of exclusion and bias signify that society has not sufficiently ensured access to diverse populations nor guaranteed the neutrality of internet-based technologies. This aim has largely been overlooked during the digitization process, yet solutions must be developed for AI to navigate these challenges effectively. The implications of AI technologies are profound, necessitating a strategy that incorporates varied voices and viewpoints into the AI development process.
Subsection 1.1.1: The Internet Boom Analogy
Photo by Sharon McCutcheon from Pexels.
Venkatachalam likens this challenge to the internet boom, questioning who stands to gain from advancements in AI. She posits, “When technology revolutions occur, something that was once prohibitively expensive becomes widely accessible. Just as ‘connecting and communicating’ became affordable with the internet, I would argue that ‘analyzing and predicting’ will become similarly inexpensive with AI. The beneficiaries will be those individuals and organizations with exclusive access to data.”
Section 1.2: Data Bias and Its Consequences
Another critical yet often overlooked issue is the presence of biased data. For AI to accurately generate predictions from extensive datasets, it is essential to evaluate and interpret this data correctly. This complex task is elaborated upon in my article, Unchecked AI Can Mirror Human Behavior. For instance, since the majority of health data collected pertains to white males, many AI systems are currently skewed to identify the most effective experimental treatments for this demographic.
Chapter 2: Addressing Ethical AI
Implementing ethical AI principles in real-world settings requires a significant commitment of time and resources from legal, regulatory, and data science teams. Although there is no universal solution for creating responsible and inclusive AI applications, this should not deter organizations from striving for ethical AI through a thoughtful combination of cutting-edge research, legal frameworks, and professional best practices.
Ethical AI should become the norm rather than the exception. If we are genuinely committed to this cause, we must prioritize the perspectives of those who currently lack power and influence. Failing to do so risks transforming AI from being humanity's most effective problem solver into its greatest enabler of injustice.