Abstract: Digital advertising is a key source of financing for online publicists. By attracting readers to their sites, they can make an income by displaying advertisements on behalf of others. The advertising space is sold via fully automated auctions that run their course within the blink of an eye. The bids are based on a range of factors, such as the popular standing of the digital publication, the size and positioning of the ad, and even the time of day. At present, bids are primarily based on information about the reader who will be seeing the ad. This is known as personalised advertising and has problems related to privacy and fairness. An emergent alternative to personalised advertising is to base bids on the surrounding media context. This practice is known as contextual advertising and avoids the use of personal data. On the downside, contextual advertising can blur the line between publisher content and advertisements in a negative way. It can also recreate problems linked to personalised advertising by making assumptions about the reader based on the content that he or she is looking at. The combination of personalised and contextual advertisement is especially loaded, as the advertiser will then know both who is requesting a webpage, but also what the webpage is about. Not only does this open for the most severe privacy issues, but it can also be used to manipulate democratic process such as elections. In this talk, we survey how AI is currently being used in online advertising, and then look closer at personalised and contextual advertising. We discuss how advertisers reason when they set up their campaigns, what changes when the AI systems that execute the campaigns become more capable, and what could happen when these techniques are applied to political opinion formation. On a more positive note, we will also talk about how the negative effects can mitigated, and how consumers can sometimes use the workings of the advertising AIs to their own advantage.