According to the chapter, what are the main threats that algorithmic bias poses to democracy, and how does it affect different demographic groups?

Algorithmic bias threatens democracy by tilting elections, spreading misinformation and polarization, reinforcing ideological echo chambers, and creating unequal representation in political discourse. It affects demographic groups unevenly: younger urban voters are over-targeted with emotional and polarizing ads, while rural and older voters receive less tailored or more neutral messaging, and people from marginalized backgrounds are more likely to feel excluded or misrepresented by algorithmic content curation.

The chapter identifies several democracy-threatening effects of algorithmic bias in social media political campaigns. Biased algorithms can shape what voters see, often subconsciously directing them toward sensational or emotionally charged content, which cements existing biases and can influence election outcomes. Algorithms built to maximize engagement tend to amplify divisive or polarizing posts, creating filter bubbles and feedback loops where misinformation spreads faster than it can be detected or corrected, eroding public trust and democratic discourse. Network analysis in the chapter confirmed that users in ideological echo chambers had their prior beliefs reinforced and opposing views marginalized, while communities with similar political content became more insular and polarized. In terms of demographic effects, studies of targeted political ads showed that younger urban voters were disproportionately targeted, whereas rural and older demographics received less tailored political messaging. The tone of ads also differed: younger voters were more likely to see emotional and polarizing content, while older, more conservative populations received more neutral messaging, further stoking division. Additionally, survey respondents from marginalized backgrounds were much more likely to feel excluded or misrepresented by algorithmic content curation, highlighting unequal representation and the need for greater inclusivity in AI-led political campaigns.