![]() Similarly, AI’s use in healthcare for diagnosing and predicting diseases may falter if the training is limited to data from developed nations. The potential fallout from this can span various sectors, from security to consumer services, potentially leading to discrimination or unjust treatment. To illustrate, facial recognition technology in AI, often educated with data from developed nations where white individuals form the majority, may lack efficacy in identifying individuals with darker skin tones or unique facial characteristics from developing nations. Such bias in AI emerges when the data employed to instruct AI models fails to represent the realities or conditions of developing nations. This inadequacy may curb economic growth and innovation in these regions, subsequently amplifying the digital and economic chasm between them and their developed counterparts. Specifically, AI algorithms trained predominantly on data from developed economies could fall short in comprehending, let alone addressing, unique challenges endemic to developing nations. The adverse implications of AI data bias might render AI technology less effective, and even inaccurate, in markets of developing nations. ![]() This is primarily embodied in the form of AI data bias, an inequality in data used for training AI, which could precipitate significant economic disparities between advanced and developing countries. Yet beneath this veneer of progress, a latent economic imbalance, potentially harmful to developing nations, lurks. In the pulsating rhythm of the digital revolution, Artificial Intelligence (AI) has rooted itself deep within various sectors of the economy.
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