Ad tech, short for advertising technology is a growing industry with the evolution of Internet and online systems in the current world scenario. Ad-Tech revolutionized the way to reach the right audience digital advertising campaigns by collecting and processing massive amounts of online data to analyze, recommend, control, form & pitch for digital advertising campaigns. Advertising has been traditionally through TV. From the first ever TV advertisement in 1941, the evolution of ads on TV has come across many changes. But with recent changes in ad tech with digital advertising, and the increasing use of YouTube, there has been an enormous base of users who can be targeted, and hence the search for finding the right channels to target them is crucial. This paper mainly focuses on building an advertiser agnostic deep learning solution which would help recommend the right YouTube channels for the relevant audience based on their interests. This would help the advertisers to run their YouTube campaigns effectively with a better reach in the field of ad tech industry, and make the conversion easier by catering audiences based on the channels with similar interests.
In the rapidly developing “AI as a Service” (AIaaS) market, plug-and-play versions of artificial intelligence services and tool sets are offered for purchase. Customers (users) of AIaaS have the ability to quickly build and integrate AI capabilities into their applications without the need to create their own systems from the ground up. Despite this, it is well knowledge that AI systems are capable of developing their own forms of bias and inequity. According to the findings of this study, the “one-size-fits-all” nature of AIaaS results in challenges and disagreements due to the fact that fairness varies depending on the circumstances. In this work, we investigate and organize the AIaaS sector, as well as present a taxonomy of artificial intelligence services that is based on the degrees of autonomy that are provided to the client. In the next section, we present a critical examination of the various forms of AIaaS, pointing out how each type may encourage prejudices or create other harm in the context of applications that are intended for the general public. By taking these measures, we hope that academics will start to pay more attention to the challenges that come with this emerging subject.
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