IMPACT OF DATA COLLECTION ON ML MODELS: ANALYZING DIFFERENCES OF BIASES BETWEEN LOW- VS. HIGH-SKILLED ANNOTATORS

Labeled data is crucial for the success of machine learning-based artificial intelligence. However, companies often face a choice between collecting few annotations from high- or low-skilled annotators, possibly exhibiting different biases. This study investigates differences in biases between datasets labeled by said annotator groups and their impact on machine learning models. Therefore, we created high- and low-skilled annotated datasets measured the contained biases through entropy and trained different machine learning models to examine bias inheritance effects. Our findings on text sentiment annotations show both groups exhibit a considerable amount of bias in their annotations, although there is a significant difference regarding the error types commonly encountered. Models trained on biased annotations produce significantly different predictions, indicating bias propagation and tend to make more extreme errors than humans. As partial mitigation, we propose and show the efficiency of a hybrid approach where data is labeled by low-skilled and high-skilled workers

Balancing fisheries and coastal management across the triple bottom line: objectives and outcomes co-designed with stakeholders

Fisheries and coastal assets are both common pool resources. The management of natural resources has a special focus in sustainability science because of the need to avoid the ‘tragedy of the commons’. Not only do common resources need special attention to ensure future sustainability, there is also a need to ensure management decisions are not made on short timelines, so as to prevent the ‘tragedy of the horizon’. This tragedy occurs when the time to replenish those resources is much longer than the timeframe over which impacts of resource decisions are managed, and often imposes costs on future generations. This thesis focuses on how the management of fisheries and coastal resources can be implemented through a triple bottom line lens to avoid both tragedies. Foremost, the thesis examines how appropriate social objectives can be developed, particularly through stakeholder engagement, and how management options can be assessed to identify options that maximise triple bottom line outcomes. These aspects are demonstrated through a series of case studies. The purpose of the research presented in this thesis is to explore stakeholder participation in fisheries and coastal management decision-making with the triple bottom line approach. The triple bottom line as defined John Elkington (1997) encompasses seven paradigm shifts for sustainability: (i) using markets to improve environmental management and create triple-win situations; (ii) incorporating lifecycle technologies and approaches; (iii) engaging and co-designing with stakeholders so that they become process partners; (iv) transparency throughout assessment and management processes; (v) adopting long time horizons; (vi) uncovering and appreciating social ‘soft’ values and other externalities which will see the need for evolving ways and means to quantify qualitative outcomes; and (vii) establishing governance embedded with corporate social responsibility (CSR)