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Today, we are witnessing an increasing adoption of mobile devices for messaging, telecommunications, video conferencing, online shopping, entertainment, and remote operations, all becoming possible through applications and data on devices. According to Statista, there are around 3. To tap this potential, businesses across the globe have started drifting to mobile applications to increase their brand visibility, build customer loyalty, provide better customer experience, and to form a direct marketing channel with their prospects and customers. While a website is the most common channel that businesses use to display their products and services to their customers, mobile applications are on a rise to capture an audience on the move. Generally, commuters prefer to find the smartest and quickest way to work to fulfill their requirements. A mobile application steers it exactly right with a custom dashboard, RSS feeds, personalized ads, features, and accessibility thus giving it an edge to a mobile website. While there are so many technologies that businesses are using in their mobile apps such as location-based tracking, Augmented Reality, Connected Sensors, payment integrations, however, Artificial intelligence has penetrated our mobility and application world in a great sense. AI-based mobile applications are trying to solve problems and perform tasks that we could not have even imagined a couple of decades ago.

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The Development of AI in The AI.

Overview : Western AI institutions have started to involve more diverse groups in the development and application of AI systems as a response to calls to level out the current global imbalances in the field. In this paper, the authors argue that increased representation can only go so far in redressing the global inequities in AI development and outline how to achieve broader inclusion and active participation in the field. When it comes to AI development, the scales are generally tipped in favour of countries in the Global North.

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For Chan, Okolo, Terner and Wang, greater representation can only go so far while structural inequalities remain unchallenged. To enact far-reaching change requires a redistribution of power. If we fail to provide a level playing field, Chan et al. The invisibility of countries in the Global South in conference publications is generally reflective of the broader inequality in AI development; it signals where innovation, hence funding, is happening.

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In terms of the top ten institutions, the US dominated the list with 8, including familiar names such as Google, Facebook, and Microsoft. Calls for inclusion may lead to what Sloan et al. With its abundance of capital, well-funded research institutes and technical infrastructure, the Global North is well-positioned to lead AI innovation.

However, its advantageous position is in large part due to riches accumulated through colonial exploitation. While calls to diversify the data-gathering pipeline are steps in the right direction, the authors delineate how the process is much more complicated — it involves dismantling long-standing global inequities.

For Chan et al. As for data labelling, there are limited possibilities for individuals in the Global South to participate or achieve any form of upward mobility. Given the tedious and repetitive nature of the task, data labelling companies often seek out a low-wage workforce from the Global South, contracted via crowdsourcing platforms such as MTurk and Samasource. Global power dynamics fuel the uneven growth of tech, leading Chan et al.

Fears over political and economic instability and a misguided view of local talent have led to limited investments in other areas. Chan et al. It follows that opportunities must be provided for local residents to acquire the skills and training needed for management roles and guide critical decisions.

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True global inclusion in AI, and the potential to bring genuine change, cannot be done without a redistribution of power. Original paper by Alan Chan, Chinasa T. Subscribe to the AI Ethics Brief. We write every week.]