Purpose – Information verification is an important factor in commercial valuation practice.Valuers use their professional autonomy to decide on the level of verification required, thereby creating an opportunity for client-related judgement bias in valuation. The purpose of this paper is to assess the manifestation of client attachment risks in information verification. Design/methodology/approach – A case-based questionnaire was used to retrieve data from 290 commercial valuation professionals in the Netherlands, providing a 15 per cent response rate of the Dutch commercial valuation population. Descriptive and inferential statistics have been used to test research hypotheses involving relations between information verification and professional features that may indicate client attachment such as an executive job level and brokerage experience. Findings – The results reveal that valuers acting at partner level within their organisation obtain lower scores on information verification compared to lower-ranked valuers. Also, brokerage experience correlates negatively to information verification of valuation professionals. Both findings have statistical significance. Research limitations/implications – The results reflect valuers’ reasoning behaviour rather than actual behaviour. Replication of findings through experimental design will contribute to research validity. Practical implications – Maintaining close client contact in a competitive environment is important for business continuity yet may foster client attachment.The associated downside risks in valuation practice call for higher awareness of (subconscious) client influence and the development of attitudinal scepticism in valuer training programmes. Originality/value – This paper is one of the few that explore possible sources of valuer judgement bias by relating client-friendly valuer features to a key area of valuation i.e. information verification.
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With the proliferation of misinformation on the web, automatic misinformation detection methods are becoming an increasingly important subject of study. Large language models have produced the best results among content-based methods, which rely on the text of the article rather than the metadata or network features. However, finetuning such a model requires significant training data, which has led to the automatic creation of large-scale misinformation detection datasets. In these datasets, articles are not labelled directly. Rather, each news site is labelled for reliability by an established fact-checking organisation and every article is subsequently assigned the corresponding label based on the reliability score of the news source in question. A recent paper has explored the biases present in one such dataset, NELA-GT-2018, and shown that the models are at least partly learning the stylistic and other features of different news sources rather than the features of unreliable news. We confirm a part of their findings. Apart from studying the characteristics and potential biases of the datasets, we also find it important to examine in what way the model architecture influences the results. We therefore explore which text features or combinations of features are learned by models based on contextual word embeddings as opposed to basic bag-of-words models. To elucidate this, we perform extensive error analysis aided by the SHAP post-hoc explanation technique on a debiased portion of the dataset. We validate the explanation technique on our inherently interpretable baseline model.
Previous research shows that automatic tendency to approach alcohol plays a causal role in problematic alcohol use and can be retrained by Approach Bias Modification (ApBM). ApBM has been shown to be effective for patients diagnosed with alcohol use disorder (AUD) in inpatient treatment. This study aimed to investigate the effectiveness of adding an online ApBM to treatment as usual (TAU) in an outpatient setting compared to receiving TAU with an online placebo training. 139 AUD patients receiving face-to-face or online treatment as usual (TAU) participated in the study. The patients were randomized to an active or placebo version of 8 sessions of online ApBM over a 5-week period. The weekly consumed standard units of alcohol (primary outcome) was measured at pre-and post-training, 3 and 6 months follow-up. Approach tendency was measured pre-and-post ApBM training. No additional effect of ApBM was found on alcohol intake, nor other outcomes such as craving, depression, anxiety, or stress. A significant reduction of the alcohol approach bias was found. This research showed that approach bias retraining in AUD patients in an outpatient treatment setting reduces the tendency to approach alcohol, but this training effect does not translate into a significant difference in alcohol reduction between groups. Explanations for the lack of effects of ApBM on alcohol consumption are treatment goal and severity of AUD. Future ApBM research should target outpatients with an abstinence goal and offer alternative, more user-friendly modes of delivering ApBM training.
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