CrowdCO-OP : sharing risks and rewards in crowdsourcing

Paid micro-task crowdsourcing has gained in popularity partly due to the increasing need for large-scale manually labelled datasets which are often used to train and evaluate Artificial Intelligence systems. Modern paid crowdsourcing platforms use a piecework approach to rewards, meaning that workers are paid for each task they complete, given that their work quality is considered sufficient by the requester or the platform. Such an approach creates risks for workers; their work may be rejected without being rewarded, and they may be working on poorly rewarded tasks, in light of the disproportionate time required to complete them. As a result, recent research has shown that crowd workers may tend to choose specific, simple, and familiar tasks and avoid new requesters to manage these risks. In this paper, we propose a novel crowdsourcing reward mechanism that allows workers to share these risks and achieve a standardized hourly wage equal for all participating workers. Reward-focused workers can thereby take up challenging and complex HITs without bearing the financial risk of not being rewarded for completed work. We experimentally compare different crowd reward schemes and observe their impact on worker performance and satisfaction. Our results show that 1) workers clearly perceive the benefits of the proposed reward scheme, 2) work effectiveness and efficiency are not impacted as compared to those of the piecework scheme, and 3) the presence of slow workers is limited and does not disrupt the proposed cooperation-based approaches

Understanding Malicious Behavior in Crowdsourcing Platforms: The Case of Online Surveys.

Crowdsourcing is increasingly being used as a means to tackle problems requiring human intelligence. With the ever-growing worker base that aims to complete microtasks on crowdsourcing platforms in exchange for financial gains, there is a need for stringent mechanisms to prevent exploitation of deployed tasks. Quality control mechanisms need to accommodate a diverse pool of workers, exhibiting a wide range of behavior. A pivotal step towards fraud-proof task design is understanding the behavioral patterns of microtask workers. In this paper, we analyze the prevalent malicious activity on crowdsourcing platforms and study the behavior exhibited by trustworthy and untrustworthy workers, particularly on crowdsourced surveys. Based on our analysis of the typical malicious activity, we define and identify different types of workers in the crowd, propose a method to measure malicious activity, and finally present guidelines for the efficient design of crowdsourced surveys

A neonatal presentation of factor V deficiency: A case report

BACKGROUND: Factor V deficiency is a rare autosomal recessive coagulation disorder. Awareness of presenting features and management is important to avoid bleeding complications associated with mortality and neurodisability. CASE PRESENTATION: A 6-day-old Pakistani boy was admitted with bleeding from the left nipple. His parents were first cousins. A coagulation screen showed a prothrombin time of 41 s (control 14 s), a partial thromboplastin time of 132 s (control 33 s) and a normal thrombin time of 15 s (control 14 s). Factor V activity was <0.01 IU/ml. Oral tranexamic acid was started. At 5 weeks of age the child presented with irritability, lethargy and reduced feeding and a drop of hemoglobin to 5.6 g/dl. A cranial computed tomography scan showed a right intra-cerebral bleed extending from the frontal lobe to the parieto-occipital region with shift of the midline to the left. A regime of 20 ml/kg of fresh frozen plasma four times a week was instituted and has prevented further bleeds up to the present age of 21 months. Neurodevelopment remained normal. CONCLUSION: This case illustrates that in an unusually bleeding newborn of consanguineous parents rare severe homozygous bleeding disorders need to be considered. Nipple bleeding may be the first presentation of a congenital bleeding disorder. In cases of factor V deficiency where factor concentrates are not available long term use of fresh frozen plasma can prevent potentially life threatening bleeding

Bias in data-driven artificial intelligence systems - An introductory survey

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues

Bias in data-driven artificial intelligence systems—An introductory survey

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues &gt; Fairness in Data Mining Commercial, Legal, and Ethical Issues &gt; Ethical Considerations Commercial, Legal, and Ethical Issues &gt; Legal Issues

A Study on Direct Reduction of Harmonics of Ultra High Power AC Arc Furnaces in Uneasy Mode of Operation

An arc furnace (AF) transformer is extensively subjected to harmonics generated by furnace. To mitigate this, filters are installed usually at AF primary terminals. In this scenario, the harmful current components, namely, the furnace-generated harmonics, the unbalanced, and reactive current, contribute to the transformer energy loss. This loss can be reduced if the filter is installed at the secondary terminals of the AF transformer. This article investigates and validates the furnace performance when the reactive current compensation and harmonic mitigation is implemented on the secondary side of AF transformer. The studies were performed using a real-time simulator OPAL-RT. Results from these studies can significantly enhance our understanding of the impact of filter installed at the furnace terminals

The Effectiveness of Resonant Harmonic Filters of Ac Arc Furnaces in Uneasy Mode

Resonant harmonic filters are commonly installed on the primary side of the arc furnace transformer, and conse-quently, the transformer is exposed to the furnace generated current harmonics, the furnace reactive and unbalanced currents. The effectiveness of resonant harmonic filters installed directly at the furnace terminals, i.e., on the secondary side of the furnace transformer, is investigated in this paper. Such filters operate in conditions of fast varying parameters of the furnace. This can cause a decline in the filter effectiveness of harmonics reduction and a decline in the effectiveness of the power factor improvement. This paper investigates the effectiveness of resonant harmonic filters in an uneasy mode of an arc furnace operation. This study should enable us to draw some quantitative conclusions on the usefulness of resonant harmonic filters installed directly on the furnace

Why the Electric Arc Nonlinearity Improves the Power Factor of Ac Arc Furnaces?

The arcs in ac arc furnaces are stabilized by line inductors, at the cost of the power factor decline. It seems that in common opinions also the arc nonlinearity contributes to this decline. It occurs, however, that at the same parameters, the furnace, regarded as a linear device, has a lower power factor as compared to its value, when the arc nonlinearity is taken into account. The value of ac arc furnaces power factor (PF) is estimated in this paper at the assumption that the dc voltage on the arc has a constant value and a furnace is in a steady state. The PF of ac arc furnaces was evaluated for three states of the furnace operation, namely, for a balanced operation; for a two-arcs operation and for a furnace with a unidirectional arc. The analysis presented in the paper and modeling demonstrate that the arc nonlinearity improves the furnace power factor