At ServiceNow, our technology makes the world work for everyone, and our people make it possible. We move fast because the world can't wait, and we innovate in ways no one else can for our customers and communities. By joining ServiceNow, you are part of an ambitious team of change makers who have a restless curiosity and a drive for ingenuity. We know that your best work happens when you live your best life and share your unique talents, so we do everything we can to make that possible. We dream big together, supporting each other to make our individual and collective dreams come true.

The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics. The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available.

Last week, Google announced and released a beta version of Simple ML for Sheets, a TensorFlow Decision Forests-produced add-on for Google Sheets. This release is one of the first of its kind, offering many simple and some complex machine learning functionalities directly to Google Sheets users. Although Simple ML has been touted as the machine learning solution for people with no prior knowledge of machine learning, the Advanced Tasks it offers promise value to data scientists, machine learning experts and anyone else working with bigger datasets. Read on to learn more about this release and how it may shape spreadsheet-based data and machine learning projects in the future. Simple ML for Sheets is currently available in beta.

Language data and models demonstrate various types of bias, be it ethnic, religious, gender, or socioeconomic. AI/NLP models, when trained on the racially biased dataset, AI/NLP models instigate poor model explainability, influence user experience during decision making and thus further magnifies societal biases, raising profound ethical implications for society. The motivation of the study is to investigate how AI systems imbibe bias from data and produce unexplainable discriminatory outcomes and influence an individual's articulateness of system outcome due to the presence of racial bias features in datasets. The design of the experiment involves studying the counterfactual impact of racial bias features present in language datasets and its associated effect on the model outcome. A mixed research methodology is adopted to investigate the cross implication of biased model outcome on user experience, effect on decision-making through controlled lab experimentation. The findings provide foundation support for correlating the implication of carry-over an artificial intelligence model solving NLP task due to biased concept presented in the dataset. Further, the research outcomes justify the negative influence on users' persuasiveness that leads to alter the decision-making quotient of an individual when trying to rely on the model outcome to act. The paper bridges the gap across the harm caused in establishing poor customer trustworthiness due to an inequitable system design and provides strong support for researchers, policymakers, and data scientists to build responsible AI frameworks within organizations.

Governments are paying growing attention to the potential of artificial intelligence – the simulation of human intelligence processes by machines – to enhance what they do. To explore how public authorities are approaching the use of AI for tasks related to public finances, Global Government Fintech – the sister title of Global Government Forum – convened an international panel on 4 October 2022 for a webinar titled'How can AI help public authorities save money and deliver better outcomes?'. The discussion, organised in partnership with SAS and Intel, highlighted how AI is already helping departments to deliver results. But also that AI remains very much an emerging and, to many, rather nebulous field with many hurdles to clear before widespread use. "Discussions of artificial intelligence often bring up connotations of an Orwellian nature, dystopian futures, Frankenstein…" said Peter Kerstens, advisor, technological innovation & cyber security at the European Commission's Financial Services Department.

Hotel Engine is the world's largest Lodging Performance Network, established to create a richer, more rewarding business travel experience by connecting a global network of businesses and lodging partners. Our innovative travel-tech company is on an incredible growth trajectory and will continue to build on our strong foundations by bringing our customer obsession, data-driven problem-solving, and bias for action into every decision we make. In December of 2021, we closed our series B funding round with a $1.3 billion valuation, and that's just the beginning. We expect 2022 to be our best year yet. Working hard behind the scenes building and supporting our platform are exceptional people–from our large engineering and product teams to our fast-growing sales, supplier, and member support orgs, to our data, marketing, and operations teams.

Demand for instant access to financial data from investors and traders around the world has shaken up the financial services industry, and Nasdaq, a pioneer in digitizing the trading process, continues to innovate for customers seeking mobile-first, real-time, mission-critical analytics. Its approach involves embracing the cloud, data and analytics, and an API-first mindset. The global exchange operator, which encourages greater market participation and innovation as vital to the health of the global economy, was one of the early innovators in providing its clients with a wide variety of financial data and analytics. In 2008, Nasdaq partnered with AWS to provide different Nasdaq programs and data access via the cloud, first with Nasdaq Market Replay, followed by Data-on-Demand. Over the years, the company has delivered additional data services through the cloud, including last year's NextGen Solutions package and Nasdaq Cloud Data Service, further extending its reach as a provider of technology and analytics services.

When businesspeople think of important corporate assets, they often start by picturing physical things such as office buildings, computers and printers. This is great news for you in your role as a chief financial officer (CFO). You have a creative opportunity to use all the income, expense, payment, invoice, cash flow, customer and other types of data to generate more valuable insights. These insights lead to more intelligent business decisions. Those include deeper understandings of your cash flows so you can plan where to make investments in the future.

Michelangelo can deploy multiple models in the same serving container, which allows for safe transitions from old to new model versions and side-by-side A/B testing of models. The original incarnation of Michelangelo did not support deep learning's need to train on GPUs, but that the team addressed that omission in the meantime. The current platform uses Spark's ML pipeline serialization but with an additional interface for online serving that adds a single-example (online) scoring method that is both lightweight and capable of handling tight SLAs, for instance, for fraud detection and prevention. It does so by bypassing the overhead of Spark SQL's Catalyst optimizer. Noteworthy is that both Google and Uber built in-house protocol buffer parsers and representations for serving, avoiding bottlenecks present in the default implementation. Airbnb established their own ML infrastructure team in 2016/2017 for similar reasons. First, they only had a few models in production, but building each model could take up to three months. Second, there was no consistency among models. And third, there were large differences between online and offline predictions.

The banks hopes to streamline processes and protect clients from fraud. Indonesia's Bank Central Asia (BCA) will utilise data cloud firm Cloudera to boost operational efficiency and customer engagement, according to an announcement. The bank hopes that Cloudera will help them aggregate structured and unstructured data from emails, social media and call centres, as well as shorten the time taken for queries. Cloudera's data platform has also enabled BCA to implement machine learning processes for automation. As a result, the bank's business units have gained a holistic view of their customers and are using near real-time insights to provide personalised offerings based on customer profiles.