Bias in Technology: An In-depth Examination of AI and Broader Tech Culture
Luiza Castro Rey
Partner, Corporate & Crypto Specialist | Head of Legal & Web3 | Chief Legal Officer | Speaker
In our digital era, the accelerated growth of artificial intelligence often takes the limelight. But as professionals venturing into this world or as informed citizens, we must dive deeper into the subject.
Surface-level articles can introduce, but they seldom address the intricate
relationship between AI and societal constructs.
While it's admirable to simplify complex subjects for beginners, it's crucial we don't misconstrue them as deep dives into the intricacies of the AI domain.
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Representational Harms and AI’s Mirroring Effect
Recently, a thought-provoking video from the London Interdisciplinary School delved into the ethical quagmire surrounding a polarizing BuzzFeed post on AI-generated Barbies. The piece underlined the disturbing biases embedded within AI image generators. While the BuzzFeed article was taken down due to its contentious nature, the larger concern remains:
How do AI image generators amplify existing societal biases?
The phenomenon of "representational harms" is both disturbing and enlightening. It highlights the risk of AI models painting certain societal groups with a broad, often prejudiced brush, perpetuating existing stereotypes or even exacerbating them. A striking illustration of this problem emerged in the mentioned BuzzFeed article, which showcased AI-generated Barbies from various countries. The depictions were rife with bias: Barbies from Latin America were unfairly light-skinned, reinforcing colorism. Germany's Barbie was insensitively reminiscent of a Nazi uniform, and the South Sudan representation was alarmingly militaristic. These outputs aren't mere innocuous errors but signal deeper underlying issues. Such portrayals beg the question: How can AI, a pinnacle of human innovation, produce content that seems regressive?
As well put in LIS video, a lot of those problems and discussions arise when AI models produce outputs that, either by upholding the status quo or by accentuating stereotypes, demean certain societal groups.
Take, for example, MidJourney, an AI image generator that designs unique images based on word prompts. If you prompted it with words like "lawyer" or "CEO," would the images produced reflect diverse and unbiased representations, or would they inadvertently echo societal stereotypes?
?Peeling Back the Layers: Why Does AI Go Awry?
At its core, AI is a product of data—data that originates from human sources. It learns from vast amounts of information, often pulled from the internet, which is an aggregation of our collective history, opinions, biases, and beliefs. When we query AI with prompts, it provides outputs based on this vast knowledge reservoir. The issue arises when these data sources are dominated by biased or one-sided perspectives. Thus, the result is a reflection, often amplified, of societal undercurrents. In essence, when AI errs, it's often because it's mirroring the imbalances, biases, and prejudices already present in the data it was trained on.
It's tempting to castigate AI for such transgressions, but we must remember that machines only replicate what they're taught. AI models, especially in machine learning, are trained on vast datasets. If these datasets are riddled with biases, the AI will, in turn, mirror these biases. Hence, AI is inadvertently a mirror to our society, reflecting both our virtues and vices.
As lawyers and professionals, one might wonder about our role in this. For starters, we must understand that a machine-learning model is akin to a student. If students are taught from biased textbooks, their worldview becomes skewed. Similarly, if AI models are trained on biased data, their outputs will be tainted.
?Historical Bias: It's Not Just About AI
It's essential to understand that the issue of bias in technology isn't a new or exclusive problem to AI. Dive back in time, let's consider the origins of the iconic character Lara Croft. Behind her design were not AI algorithms but predominantly young white men, inadvertently (or advertently) sculpting her based on their own
cultural and gendered perspectives. Japanese anime, renowned worldwide, is
crafted by humans with their unique set of biases, not machines. The characters, narratives, and themes are reflective of the creators' conscious and unconscious perspectives, which can be shaped by prevalent social norms and biases.
The Persistent Shadow of Gender Imbalances in STEM
The roots of gender bias stretch deep into the tech and STEM sectors. These historically male-dominated fields have inadvertently (or sometimes overtly) prioritized male perspectives, leaving an indelible mark on their outputs. Sexism and patriarchy have left imprints on the products and services birthed in these fields. While the conversation around gender parity in STEM has gained momentum, we're still grappling with the legacy of a predominantly male-driven tech world. This historical imbalance undoubtedly influences the tech products we interact with daily.
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Technology: Society’s Mirror or Molder?
Technology isn't merely a neutral tool; it's a reflection of societal values, norms, and structures. When biases manifest in AI outputs, it's crucial to probe deeper than the technology itself. The human hand behind it, with its editorial choices, prompt crafting, and image selection, is equally responsible. In the BuzzFeed Barbie debacle, one might argue that human biases in decision-making were even more glaring than the machine’s outputs.
BuzzFeed, AI, and Human Accountability
The BuzzFeed debacle with AI-generated Barbies is emblematic of a larger issue. While AI does exhibit representational bias, it's essential to scrutinize human involvement too. Editorial decisions, prompt crafting, image selection, and more were all human-controlled aspects. Arguably, the biases displayed in the BuzzFeed campaign overshadow those inherent in AI. The question arises: Can AI combat representational bias?
Potentially, yes. AI offers us tools that can help visualize a more inclusive future. However, to solely rely on technology for our cultural redemption would be naive. We're not too far from the days when human designers, in the 90s, manually excluded non-white individuals from Eastern European ad campaigns. While times have changed, such incidents serve as a stark reminder that biases aren't just machine-generated; they're deeply rooted in human history.
?AI: An Instrument of Change or Reinforcement?
AI tools, like any other, are as effective or flawed as their human handlers. The Algorithmic Justice League, among others, emphasizes the ethical nuances in AI. It's naive to think of AI as just a source of problems; it can also be part of the solution. If well-directed, AI could combat biases, offering a more inclusive vision for the future. But technology alone can't rectify cultural issues. Recalling the 90s, human designers manually edited out non-white individuals from advertising campaigns. It’s clear that machine bias often stems from deeply entrenched human prejudices.However, with the right approach, AI can be a significant ally.
Using diverse teams, like that of MidJourney, and crafting well-thought-out prompts, such as "Confident Woman CEO in a conference room," can generate more representative results.
Responsibility and Accountability in the Age of AI
A pivotal question arises: What can we do about it?
1.???Curate Balanced Datasets: The genesis of AI bias often lies in the data. By ensuring diverse and representative datasets, we can curb the inception of biases.
2.???Develop Robust Filters: Before deploying AI models, especially image generators, we need to institute filters that identify and rectify problematic outputs.
3.???Continuous Monitoring: It's not enough to just train an AI model. Regular audits are necessary to ensure that the AI functions as desired, without unintentional biases.
4.???Public Awareness and Engagement: Encourage a more informed discourse around the potential pitfalls of AI. This ensures accountability and pushes developers to prioritize ethical AI development.
5.???Legislation and Regulation: As legal professionals, we play a pivotal role in shaping AI's ethical framework through regulations that safeguard against biases.
The digital activist, Joy Boulamwini, encapsulates this sentiment succinctly: "Whether AI will help us reach our aspirations or reinforce unjust inequalities is ultimately up to us." Indeed, the onus is on us - developers, professionals, and society at large - to ensure AI's promise isn't overshadowed by its pitfalls.
In Conclusion
Bias in AI is not just a tech issue—it's an echo of broader societal prejudices. The challenge ahead is not merely technological but profoundly human. As we innovate and integrate AI further into our lives, we must continually reflect, learn, and strive for a more equitable and inclusive digital landscape. Only then can we hope to utilize technology in a way that truly betters society.