Summary: Generative AI, equipped with ever-improving technology, mirrors dilemmas of old. Ground-breaking as this technology may seem, it bears striking resemblance to pitfalls social media platforms grappled with in years prior. As generative AI surges forward, it will need to tread carefully to avoid echoing disheartening past missteps.
Inherited Problems, Unchartered Territory
Generative AI firms, such as OpenAI with its well-known product, ChatGPT, face challenges akin to social media platforms of the past. Even as they leverage potent technology, issues like content moderation and deceptive ads pose significant hurdles. Although Meta (formerly Facebook) and similar platforms have attempted to tackle these challenges, solutions have often fallen short, repeating a cycle of insufficient response and loose oversight.
Labor Practices and Transparency
Issues related to labor practices adopted from social media platforms add to the strain. Outsourcing content moderation to underpaid workers, primarily in the Global South, shroud the development and governance of AI systems in obscurity. This lack of transparency hinders the ability of researchers and regulators to comprehend the inner workings of these systems.
False Promises and Fast Paces
As AI companies embrace a “move fast and break things” mantra reminiscent of early social media platforms, they struggle with failing promises of safeguard implementation. Google’s Bard chatbot, criticized for generating misinformation, reveals the gaps within these systems. Although there are commitments for change, the pacy cycle of development often stifles effective solutions.
Amplified Misinformation
Generative AI’s increasingly sophisticated nature raises the stakes. From creating deepfake videos of political figures to spreading disinformation, generative AI’s potential to amplify misinformation is concerning. Measures introduced, such as labeling AI-generated political ads, fall short of addressing all potential misuse.
The Struggle of Oversight
Complicating the situation further, reductions in resources for harmful content detection shift the responsibility to civil society organizations. As the rapid development of AI outstrips attempts at regulation, companies find little imperative to slow down or prioritize public interest. This imbalance emphasizes that the true problem lies not within the technology, but the socioeconomic forces driving its reckless implementation.
The challenges faced by generative AI are a stark reminder that the strategies and practices used in the past may not serve well in emerging battlegrounds of technology. It is crucial that lessons are gleaned from past failures to ensure that the applications of generative AI consider societal benefits, labor justice, and transparency.
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Featured Image courtesy of Unsplash and Luca Bravo (XJXWbfSo2f0)
