Archive · 2018-12-01
AI ethics on Saturday, 1 December 2018
7 items published this day, across 1 categories.
Research (7)
Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science
In this paper, we propose data statements as a design solution and professional practice for natural language processing technologists, in both research and development. Through the adoption and widespread use of data statements, the field can begin to address critical scientific and ethical issues that result from the use of data from certain populations in the development of technology for other populations. We present a form that data statements can take and explore the implications of adopti
Big data hurdles in precision medicine and precision public health
BACKGROUND: Nowadays, trendy research in biomedical sciences juxtaposes the term 'precision' to medicine and public health with companion words like big data, data science, and deep learning. Technological advancements permit the collection and merging of large heterogeneous datasets from different sources, from genome sequences to social media posts or from electronic health records to wearables. Additionally, complex algorithms supported by high-performance computing allow one to transform the
Examining patterns of adversity in Chinese young adults using the Adverse Childhood Experiences—International Questionnaire (ACE-IQ)
Predicting adverse drug reactions through interpretable deep learning framework
BACKGROUND: Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs. METHODS: In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori
Search and rescue with autonomous flying robots through behavior-based cooperative intelligence
A swarm of autonomous flying robots is implemented in simulation to cooperatively gather situational awareness data during the first few hours after a major natural disaster. In computer simulations, the swarm is successful in locating over 90% of survivors in less than an hour. The swarm is controlled by new sets of reactive behaviors which are presented and evaluated. The reactive behaviors integrate collision avoidance, battery recharge, formation control, altitude maintenance, and a variety
Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems
Intelligent machines have reached capabilities that go beyond a level that a human being can fully comprehend without sufficiently detailed understanding of the underlying mechanisms. The choice of moves in the game Go (generated by Deep Mind?s Alpha Go Zero [1]) are an impressive example of an artificial intelligence system calculating results that even a human expert for the game can hardly retrace [2]. But this is, quite literally, a toy example. In reality, intelligent algorithms are encroac
PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data
BACKGROUND: Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data. RESULTS: In this study, we pr