Resolving Knowledge Limitations for Improved Collective Intelligence: A novel online machine learning approach

One of the reasons human groups struggle to make the best decisions is that they are inherently biased in their beliefs. In essence, our perception of what is true is often distorted by individual and social biases, including stereotypes. When individuals deliberate about a decision, they tend to transmit these beliefs to others, thereby steering the entire group away from the best decision. For example, a senior doctor could spread a misinterpretation of symptoms to junior doctors, resulting in inappropriate treatments. The primary objective of this thesis is to mitigate the impact of such biases on group decision-making in domains such as medical diagnostics, policy-making, and crowdsourced fact-checking. We propose to achieve this by having humans interact through a collective decision-making platform in charge of handling the aggregation of group knowledge. The key hypothesis here is that by carefully managing the collectivization of knowledge through this platform, it will be substantially harder for humans to impose their biases on the final decision. The core of our work involves the development and analysis of algorithms for decision-making systems. These algorithms are designed to effectively aggregate diverse expertise while addressing biases. We thus focus on aggregation methods that use online learning to foster collective intelligence more effectively. In doing so, we take into account the nuances of individual expertise and the impact of biases, aiming to filter out noise and enhance the reliability of collective decisions. Our theoretical analysis of the proposed algorithms is complemented by rigorous testing in both simulated and online experimental environments to validate the system’s effectiveness. Our results demonstrate a significant improvement in performance and reduction in bias influence. These findings not only highlight the potential of technology-assisted decision-making but also underscore the value of addressing human biases in collabor
Doctorat en Sciences
info:eu-repo/semantics/nonPublished