As societies grow older, a growing percentage of workers over the traditional retirement age are choosing to remain in the workforce. However, age discrimination against older workers seeking new job opportunities is prevalent. We conducted a study that asked participants to rate resumes of job candidates from various backgrounds for an IT job position. We found age bias, or ageism, in hiring decisions is implicit and more prevalent than other well-reported forms of bias, such as race or gender biases, yet ageism is also far more difficult for job candidate search algorithms to ignore. In this paper, we examine the challenges of age biases in job hiring algorithms and discuss various steps that can be taken to mitigate them.

Cognitive biases, such as the bandwagon effect, occur when a participant places a disproportionate emphasis on external information when making decisions under uncertainty. These effects are challenging for humans to overcome - even when they are explicitly made aware of their own biases. One challenge for researchers is to detect if the information is used in decision making and to what degree. One can gain a better understanding of how this external information is used in decision making using an eye tracker. In this paper, we evaluate cognitive biases in the context of assessing the binary relevance of a set of documents in response to a given information need. We show that these cognitive biases can be observed by examining gaze time in Areas of Interest (AOI) that contain this pertinent external information.

The brown planthopper (BPH), Nilaparvata lugens (Stål), is a pest responsible for widespread damage to rice plants throughout South, Southeast, and East Asia. It is estimated that 10-30% of yield loss in rice crops is attributable to the BPH. In this paper, we develop a method to detect and classify the forms of BPH using CNNs and then model the infestation migration patterns of BPH in several rice-growing regions by using a CNN-LSTMs learning model. This prediction model considers inputs such as wind speed and direction, humidity, ambient temperature, the use of pesticides, the form of BPH, strain of rice, and spacing between rice seedlings to make predictions on the spread of BPH infestations over time. The detection and classification model outperformed other known BPH classification models, providing accuracy rates of 89.33%. Our prediction model accurately modeled the BPH-affected area 82.65% of the time (as determined by lamp trap counts). These models can help detect, classify, and model the infestations of other agricultural pests, improving food security for rice, the staple crop that 900 million of the world's poor depend on for most of their calorie intake.

As a foundational technology, blockchains have demonstrated their ability to remove middlemen and streamline ledger-based transactions in everything from cryptocurrencies (e.g. Bitcoin) to centralized voting. In many underdeveloped countries, blockchain-based technologies provide opportunities for transparent transactions between parties, reducing corruption and facilitating trust. Much research on blockchains to date has focused on its virtues, but far less attention has been paid to its inherent risks and dangers. In this paper, we examine the risks and dangers of relying on blockchains in underdeveloped countries. Despite its many promises, blockchain technologies face significant hurdles for adoption in underdeveloped countries; in this paper, we explore eight these risks and dangers.

Companies such as Zappos.com and Amazon.com provide financial incentives for newer employees to quit. The premise is that workers who will accept this offer are misaligned with their company culture, which will therefore negatively affect quality over time. Could this pay-to-quit incentive scheme align workers in online labor markets? We conduct five empirical experiments evaluating different pay-to-quit incentives with crowdworkers and evaluate their effects on mean task accuracy, retention rate, and improvement in mean task accuracy. We find that the number of times a user is prompted for the inducement, the type and frequency of performance feedback given to participants, the type of incentive, as well as the amount offered can help retain high-performing workers but encourage poor-performing workers to quit early. When we combine the best features from our experiments and examine their aggregate effectiveness, mean task accuracy is improved by 28.3%. Last, we also find that certain demographics contribute to the effectiveness of pay-to-quit incentives.

Crowdsourcing is a market of steadily-growing importance upon which both academia and industry increasingly rely. However, this market appears to be inherently infested with a significant share of malicious workers who try to maximise their profits through cheating or sloppiness. This serves to undermine the very merits crowdsourcing has come to represent. Based on previous experience as well as psychological insights, we propose the use of a game in order to attract and retain a larger share of reliable workers to frequently-requested crowdsourcing tasks such as relevance assessments and clustering. In a large-scale comparative study conducted using recent TREC data, we investigate the performance of traditional HIT designs and a game-based alternative that is able to achieve high quality at significantly lower pay rates, facing fewer malicious submissions.

The 3rd International Workshop on Patent Information Retrieval builds on the experiences of the first two workshops, to provide its participants an exciting, scientifically challenging and interactive event, where the specific issues of patent retrieval may be put into the general context of Information Retrieval and Knowledge Management, in order to explore innovative solutions to new and old problems, but also to evaluate and adapt traditional or classic approaches to new problems. Between the scientific presentations and posters, distinguished keynote speakers and a panel discussion, PaIR 2010 shapes itself into a significant landmark in the field of domain specific information retrieval.

Event tracking is the task of discovering temporal patterns of popular events from text streams. Existing approaches for event tracking have two limitations: scalability and inability to rule out non-relevant portions in text streams. In this study, we propose a novel approach to tackle these limitations. To demonstrate the approach, we track news events across a collection of weblogs spanning a two-month time period.