The advent of digital platforms (e.g. prediction markets, participatory budgeting, and the `metaverse') and social media has empowered individuals to voice their opinions and concerns over societal and policy-related issues. Artificial intelligent (AI) systems are increasingly being utilized to make societal decisions to reflect people's opinions. Voting is a democratic approach for expressing opinions that dates back to the ancient Greece. Voting rules are systematic ways to aggregate opinions of the crowd and form the foundation of democratic decision-making within AI and multi-agent systems. Traditional voting rules start with the promise that the opinion of the crowd (the wisdom of crowds) is always correct. However, in many situations the de facto wisdom of the crowd is inaccurate. This problem is more prevalent on online platforms where a large fraction of voters may be adversaries or malicious bots with the goal of changing the outcome or simply spreading misinformation. This project aims at addressing these shortcomings by developing novel algorithmic solutions for aggregating the opinion of the crowd when information about preference data is incomplete, noisy, or contain misinformation.