Promoting Diverse News Consumption Through User Control Mechanisms

News recommender systems (NRSs) are an essential component of online news portals. To avoid the emergence of “filter bubbles” where users display an overly selective perception of the news situation, NRSs must not only display a diverse range of news, but also motivate users to engage with the diversified content. Many existing approaches attempt to achieve this by modifying the recommendation strategy or by applying selection control techniques such as digital nudging. Based on insights from self- determination theory, we present an alternative approach that relies on user control mechanisms to promote self-determined motivation for exploratory use and thus diverse news consumption behavior. We also outline a methodological design to empirically confirm the viability of our approach. As such, we not only contribute to the theoretical understanding of the role of user control in diverse news consumption behavior, but also provide guidance on validating the practical feasibility of our approach

Unraveling Information-Limiting Environments: An Empirical Review of Individual, Social, and Technological Filters in Social Media

Social media platforms offer a convenient way for people to interact and exchange information. However, there are sustained concerns that filter bubbles and echo chambers create information-limiting environments (ILEs) for their users. Despite a well-developed conceptual understanding, the empirical evidence regarding the causes and supporting conditions of these ILEs remains inconclusive. This paper addresses this gap by applying the triple-filter-bubble model developed by Geschke et al. (2019) to analyze empirical literature on the individual, social, and technological causes of ILEs. While we identify some factors that increase the probability of ILEs under certain conditions, our findings do not suffice to thoroughly validate conceptual models that explain why ILEs emerge. Therefore, we call for future research to investigate the causes of ILEs with higher external validity to develop a more comprehensive understanding of this phenomenon

Power Imbalances in Society and AI: On the Need to Expand the Feminist Approach

Recent advances in Artificial Intelligence have brought novel opportunities for businesses, societies, and individuals alike, yet they also raise complex questions on inequitable power distribution. We see contemporary AI systems, that reinforce power imbalances and disadvantage marginalized, underrepresented, and underprivileged people. Current approaches to advancing AI, such as Ethical, Fair, or Trustworthy AI, have not included the effects of power in their considerations. As feminism has a long history of doing so, we introduce an intersectional and inclusive feminist approach to shape AI in a more equitable way. We approach this by building on recent Information Systems and interdisciplinary research as well as on evidence from expert interviews in focus groups, which we conducted in 2022 and 2023. Our study reveals that utilizing the feminist approach could be effective firstly, to shape AI systems and secondly, to change prevailing power structures in societal systems to become more equitable

Prédiction innovante des émissions de méthane tenant compte du stade de lactation

peer reviewedPrevious research has shown that CH4 emissions of dairy cows are linked to milk composition and particularly to fatty acids (FA). We showed that mid-infrared (MIR) prediction equations can be used to obtain individual enteric CH4 emissions from the milk MIR spectra. However body tissue mobilisation alters milk FA and potentially links between CH4 and MIR spectra. Therefore to reflect the expected metabolic status during lactation, a method was developed to consider days in milk (DIM) in the MIR based prediction equation. A total of 446 CH4 reference data were obtained using the SF6 method on 146 Jersey, Holstein and Holstein-Jersey cows. Linear (P1) and quadratic (P2) Legendre polynomials were computed from DIM of CH4 measurements. A first derivative was applied to the MIR spectra. The calibration model was developed using as independent variables first derivative, first derivative × P1, first derivative × P2 and a modified PLS regression. The CH4 emission prediction (g CH4/day) showed a calibration coefficient of determination (R2c) of 0.75, a cross-validation coefficient of determination (R2cv) of 0.67 and the standard error of calibration (SEC) was 63 g/day. In order to check if this new equation showed an expected and biological meaningful behavior, it was applied to the milk MIR spectra database of the Walloon Region of Belgium (1,804,476 records). The resulting trend across lactation was similar to what was expected, with increasing averaged CH4 up to DIM 83 and a slight decrease after. This pattern was a clear improvement when compared to predictions from previous equations. Results indicate that this innovative approach with integration of DIM information could be a good strategy to improve the equation by taking better account of the metabolism of the cows

Prediction innovante des émissions de méthane tenant compte du stade de lactation

peer reviewedPrevious research has shown that CH4 emissions of dairy cows are linked to milk composition and particularly to fatty acids (FA). We showed that mid-infrared (MIR) prediction equations can be used to obtain individual enteric CH4 emissions from the milk MIR spectra. However body tissue mobilisation alters milk FA and potentially links between CH4 and MIR spectra. Therefore to reflect the expected metabolic status during lactation, a method was developed to consider days in milk (DIM) in the MIR based prediction equation. A total of 446 CH4 reference data were obtained using the SF6 method on 146 Jersey, Holstein and Holstein-Jersey cows. Linear (P1) and quadratic (P2) Legendre polynomials were computed from DIM of CH4 measurements. A first derivative was applied to the MIR spectra. The calibration model was developed using as independent variables first derivative, first derivative × P1, first derivative × P2 and a modified PLS regression. The CH4 emission prediction (g CH4/day) showed a calibration coefficient of determination (R2c) of 0.75, a cross-validation coefficient of determination (R2cv) of 0.67 and the standard error of calibration (SEC) was 63 g/day. In order to check if this new equation showed an expected and biological meaningful behavior, it was applied to the milk MIR spectra database of the Walloon Region of Belgium (1,804,476 records). The resulting trend across lactation was similar to what was expected, with increasing averaged CH4 up to DIM 83 and a slight decrease after. This pattern was a clear improvement when compared to predictions from previous equations. Results indicate that this innovative approach with integration of DIM information could be a good strategy to improve the equation by taking better account of the metabolism of the cows

Use of the milk MIR spectra with a lactation stage specific model to predict CH4 emitted by dairy cows

peer reviewe

Milk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes

Mitigating the proportion of energy intake lost as methane could improve the sustainability and profitability of dairy production. As widespread measurement of methane emissions is precluded by current in vivo methods, the development of an easily measured proxy is desirable. An equation has been developed to predict methane from the mid-infrared (MIR) spectra of milk within routine milk-recording programs. The main goals of this study were to improve the prediction equation for methane emissions from milk MIR spectra and to illustrate its already available usefulness as a high throughput phenotypic screening tool. A total of 532 methane measurements considered as reference data (430 ± 129 g of methane/day) linked with milk MIR spectra were obtained from 165 cows using the SF6 technique. A first derivative was applied to the MIR spectra. Constant (P0), linear (P1) and quadratic (P2) modified Legendre polynomials were computed from each cows stage of lactation (days in milk), at the day of SF6 methane measurement. The calibration model was developed using a modified partial least-squares regression on first derivative MIR data points × P0, first derivative MIR data points × P1, and first derivative MIR data points × P2 as variables. The MIR-predicted methane emissions (g/day) showed a calibration coefficient of determination of 0.74, a cross-validation coefficient of determination of 0.70 and a standard error of calibration of 66 g/day. When applied to milk MIR spectra recorded in the Walloon Region of Belgium (≈2 000 000 records), this equation was useful to study lactational, annual, seasonal, and regional methane emissions. We conclude that milk MIR spectra has potential to be used to conduct high throughput screening of lactating dairy cattle for methane emissions. The data generated enable monitoring of methane emissions and production characteristics across and within herds. Milk MIR spectra could now be used for widespread screening of dairy herds in order to develop management and genetic selection tools to reduce methane emissions