Learning VAA: A new method for matching users to parties in voting advice applications

Artificial intelligence and voting advice applications

“…Directly relevant to AI is the concept of the "learning VAA" introduced by Romero Moreno et al ( 2022) which uses machine learning to estimate both the distance matrix and the weights in the matching algorithm. To do so, (Romero Moreno et al, 2022) employ user response data from two supplementary questions that feature prominently in all VAAs designed by PreferenceMatcher: voting intention and the reasoning behind the voting intention. They, therefore, use self-identified issue voters' voting intention to train their model by estimating the distance and weight parameters for the algorithm so the parameters predict issue-voters' voting intentions with the highest accuracy.…”

“…They, therefore, use self-identified issue voters' voting intention to train their model by estimating the distance and weight parameters for the algorithm so the parameters predict issue-voters' voting intentions with the highest accuracy. The advantage of the "learning VAA" approach is that it allows the spatial voting logic to be derived directly from the data Romero Moreno et al (2022) and to reveal how VAA statements are perceived by users, which could be useful for VAA designers and researchers in the field of spatial voting. The downside of the "learning VAA" approach is that it does not guarantee the creation of meaningful distance matrices when the input VAA user response data are of low quality, which is often the case when VAA statements are poorly formulated (see Gemenis, 2013a;van Camp et al, 2014;Bruinsma, 2021).…”

“…Pre-processing VAA data AI methods have been also used in (pre-)processing the data generated by VAA users, an area which is important inasmuch VAA user response data is used in VAA design and the evaluation of the impact of VAAs. Indeed, user response data has been used extensively to assess ex-post the effectiveness of the design (e.g., Mendez, 2012;Alvarez et al, 2014;Louwerse and Rosema, 2014) and the electoral effects (e.g., Kamoen et al, 2015;Gemenis, 2018;Germann et al, 2023) of VAAs, to train machine learning models in "social VAAs" (Katakis et al, 2013;Tsapatsoulis et al, 2015), and to dynamically calibrate VAA matching algorithms (Germann and Mendez, 2016;Romero Moreno et al, 2022).…”