We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.

The spread of true and false news online

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

The paper “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification” by Joy Buolamwini and Timnit Gebru, that will be presented at the Conference on Fairness, Accountability, and Transparency (FAT*) in February 2018, evaluates three commercial API-based classifiers of gender from facial images, including IBM Watson Visual Recognition. The study finds these services to have recognition capabilities that are not balanced over genders and skin tones [1]. In particular, the authors show that the highest error involves images of dark-skinned women, while the most accurate result is for light-skinned men.

/pdf/gender-shades-intersectional-accuracy-disparities-in-4qgeu0c1i3.pdf

Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification

With the widespread use of AI systems and applications in our everyday lives, it is important to take fairness issues into consideration while designing and engineering these types of systems. Such systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that the decisions do not reflect discriminatory behavior toward certain groups or populations. We have recently seen work in machine learning, natural language processing, and deep learning that addresses such challenges in different subdomains. With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain and have attempted to address them. In this survey we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined in order to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and how they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

A Survey on Bias and Fairness in Machine Learning

Text is the easiest means to record information but need not always be the best means for understanding a concept. In psychological theories, it is argued that when information is presented visually, it provides a better means to understand a concept. While techniques exist for generating text from a given image, the inverse problem that is to automatically fetch coherent images to represent a given set of instructions (sequence of text), is a hard one. In this paper, we present a novel multistage framework to convert textual instructions into coherent visual descriptions (text instructions annotated with images). The key components in the proposed approach are: (i) novel framework, which combines the text as well as image analysis to generate visual descriptions; (ii) ensure coherency across visual descriptions, using a combination of deep learning and graph based approach. Effectiveness of our proposed approach is shown through a user study on a dataset of instructions and corresponding images collected from WikiHow website.

Coherent Visual Description of Textual Instructions

Data analytics and artificial intelligence are extensively used by enterprises today and they increasingly span organization boundaries. Such collaboration between organizations today happens in an ad hoc manner, with very little visibility and systemic control on who is accessing the data, how, and for what purpose. When sharing data and AI models with other organizations, the owners desire the ability to control access, have visibility into the entire data pipeline and lineage, and ensure integrity. In this work, we present a decentralized trusted data and model platform for collaborative AI, that leverages blockchain as an immutable metadata store of data and model resources and operations performed on them, to support and enforce ownership, authenticity, integrity, lineage and auditability properties. Smart contracts enforce policies specified on data, including hierarchical and composite policies that are uniquely enabled by the use of blockchain. We demonstrate that our system is light-weight and can support over 1000 transactions per second with sub-second latency, significantly lower than the time taken to execute data pipelines.

Blockchain-Based Platform for Trusted Collaborations on Data and AI Models

In the era of big data where every individual is a target of intensive data collection, there is a need to create technological tools that empower individuals to track what happens to their data. Provenance has been studied extensively in both database and workflow management systems, so far with little focus on text-retrieval based workflows with user defined operators. Such kind of workflow provenance aims to capture a complete description of evaluation (or enactment) of a workflow, and this is crucial to this problem of personal data use. As an initial step to solving this problem, the work presented in this report aims at developing our own tamper proof temporal provenance storage platform and query based model that can track, store and analyze data transformations.

Project saya : tamper proof temporal provenance storage platform

Mode of non-operative management of thoracolumbar spine fracture continues to remain controversial with the most common modality hinging on bracing. TLSO is the device with a relative extension locked position, and many authors suggest they may have a role in the healing process, diminishing the load transferred via the anterior column, limiting segmental motion, and helping in pain control. However, several studies have shown prolonged use of brace may lead to skin breakdown, diminished pulmonary capacity, weakness of paraspinal musculature with no difference in pain and functional outcomes between patients treated with or without brace.To identify number of spinal braces used for spinal injury and cost implications (in a DGH), to identify the impact on length of stay, to ascertain patient compliance and quality of patient information provided for brace usage, reflect whether we need to change our practice on TLSO brace use.Data collected over 18-month period (from Jan.2020 to July 2021). Patients were identified from the TLSO brace issue list of the orthotic department, imaging (X-rays, CT, MRI scans) reviewed to confirm fracture and records reviewed to confirm neurology and non-operative management. Patient feedback was obtained via post or telephone consultation. Inclusion criteria- patients with single or multi -level thoracolumbar osteoporotic or traumatic fractures with no neurological involvement treated in a TLSO brace. Exclusion criteria- neurological involvement, cervical spine injuries, decision to treat surgically, concomitant bony injuries.72 braces were issued in the time frame with 42 patients remaining in the study based on the inclusion/exclusion criteria. Patient feedback reflected that 62% patients did not receive adequate advice for brace usage, 73% came off the brace earlier than advised, and 60% would prefer to be treated without a brace if given a choice. The average increase in length of stay was 3 days awaiting brace fitting and delivery. The average total cost burden on the NHS was £127,500 (lower estimate) due to brace usage.If there is equivalence between treatment with/without a brace, there is a need to rethink the practice of prescribing brace for all non-operatively treated fractures and a case-by-case approach may prove more beneficial.

Thoracolumbar sacral orthosis (tlso) brace for spine fractures: what's the evidence and do patients use them?

In this paper, we look at the problem of retrieving business need specific l ineage i nformation f rom t he provenance graphs. A provenance graph models the events happening on various assets on a data platform. The output of a lineage query may contain large number of nodes. However, a user depending on her business role may only want a small subset of these nodes and the lineage relationships among these nodes. We formally define the notion of a class of business lineage queries wherein a business user specifies the lineage events relevant to her business need, in terms of event labels. The lineage output then consists of these events of interest, assets associated with these events and the lineage relationships among these events and assets of interest. We propose a novel framework for efficiently executing such business lineage queries and experimentally illustrate the effectiveness of the same.

Sameep Mehta

Papers

Coherent Visual Description of Textual Instructions

Blockchain-Based Platform for Trusted Collaborations on Data and AI Models

Project saya : tamper proof temporal provenance storage platform

Thoracolumbar sacral orthosis (tlso) brace for spine fractures: what's the evidence and do patients use them?

On Efficiently Processing Business Lineage Queries