Modern Technology has infiltrated every component of human civilization and advertising is not left out. Marketers and Artificial Intelligence researchers have since incorporated deep learning algorithms into the delivery of adverts to existing and potential consumers across various platforms, using existing data. However, an undeniable level of bias thrives under the algorithms, thereby, altering the goals and intentions of targeted ads. Also, the bias being perpetrated by existing designs of deep learning is somewhat discriminatory and doesn’t reflect the views of modern socio-political events.
when we speak of bias in advertising using deep learning, we are referring to a gigantic flaw in the system that reduces the effectiveness of an Ad. Ads on websites and social media platforms utilize cookies to deliver targeted ads but the existing Algorithms ruin the process by creating other determinants. For instance, cooking ads targeting Men would most likely be shown to women, regardless of the ad input. Examples like this one are repeated with numerous other ads and need to be corrected so advertisers can receive maximum ROI.
How to correct the bias in Advertising Using Deep Learning
Advertisers and organizations need to let go of their tight grip on existing data. The need for flexibility needs to be incorporated into the new algorithms to avoid the start of a vicious unending cycle. Currently, advertisers maintain trust in researches that were carried out decades ago, failing to grasp the dynamic and fast changes currently occurring in the world. This obstinacy leads to the dismissal of new data and makes it impossible for organizations to collaborate for the development of new data.
Deep learning functions on inputted data, so the first solution is to input fresh and relevant data. Organizations, advertising agencies, and applications should collaborate with researchers to develop new market questionnaires that answer old questions. These answers can then be placed side by side with existing data and be critically studied. The result of such a rigorous study will be a new and improved algorithm, free from structural and foundational bias induced by social and economic reasons, thereby interpreting new ad information with new data.
After research, the data should be thoroughly categorized to avoid a mismatch. The data derived shouldn’t be used holistically. Dividing the population into segments and assigning the derived data to each segment helps advertisers target appropriate audiences without the fear of overlapping. Also, in dividing your population for more accurate advertising, advertisers need to put into consideration the concept of ‘context’. Deep learning is notorious for seeing things as black and white, thus, would face difficulties in certain situations. Incorporating a more advanced algorithm that enables deep learning to understand the context of an Ad reduces unnecessary bias in the delivery of Ads.
Deep learning is improving advertising in all ways, however, the lack of new research in the sector has given way to severe rigidity which makes the human perception of Ads subjective, thus leading to the creation of ineffective Ads. To combat this, we need to sponsor new research in the field of adversting.
About the Author