Responsible AI

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Data Con LA 2020 Description More and more organizations are embracing AI technology by infusing it in their products and services to to differentiate themselves against their competitors. AI is being utilized in some sensitive areas of human life. In this session let's look at some of principles governing adoption of AI in a responsible manner. Why companies are accelerating adoption of AI? Increasingly organization are accelerating adoption of AI to differentiate their product and services in the market. Outcomes of this digital transformation that we have seen in the areas of optimizing operations, engaging customers, empowering employees and transforming their products and services. *List some of the sensitive use cases where AI is being applied *Why governing AI is important and what are those principles? *How Microsoft is approaching it? Speaker Suresh Paulraj, Microsoft, Principal Cloud Solution Architect Data & AI

Daten & Analysen

Responsible AI
Opportunity and Responsibility in the Era of Artificial Intelligence
Suresh Paulraj – Principal Cloud Solution Architect

Why responsible AI?
“The more powerful the tool, the greater the benefit
or damage it can cause…Technology innovation is
not going to slow down. The work to manage it
needs to speed up.”
Brad Smith
President and Chief Legal Officer, Microsoft

Why responsible AI?
Advancements in AI are different than other technologies because of the pace of innovation, and
its proximity to human intelligence – impacting us at a personal and societal level.
Vision
2016
Object
recognition
human parity
Speech
Recognition
2017
Speech
recognition
human parity
Reading
2018
Reading
comprehension
human parity
Translation
2018
Machine
translation
human parity
Speech
Synthesis
2018
Speech synthesis
near-human
parity
Language
Understanding
2019
General
Language
Understanding
human parity

The Opportunities with AI
Healthcare Retail Financial Services Manufacturing

Today’s Debate
 Facial Recognition
 Fairness
 Corporate responsibility
 Deepfakes
 Human rights
 Meaningful human control
 Contact tracing
 Consent
 Unintended consequences
 Disproportionate impact
 Model Fragility
 Socio-technical issues
 Algorithmic auditing
 Platform accountability
 Regulation

Putting responsible AI into practice
Principles
Fairness
Accountability
Transparency
Inclusiveness
Reliability & Safety
Privacy & Security

Putting responsible AI into practice
Practices
Principles
Fairness
Accountability
Transparency
Inclusiveness
Reliability & Safety
Privacy & Security
Human-AI Guidelines
Conversational AI Guidelines
Inclusive Design Guidelines
AI Fairness Checklist
Datasheets for Datasets

Putting responsible AI into practice
Tools
Practices
Principles
Fairness
Accountability
Transparency
Inclusiveness
Reliability & Safety
Privacy & Security
Understand
Protect
Control

Responsible AI Tooling
UNDERSTAND
Interpret ML Fairlearn
PROTECT
Homomorphic
Encryption
Differential
Privacy
Presidio Confidential ML
CONTROL
MLOPs

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Jaysen Gillespie, Head of Analytics and Data Science at RTB House 1. Shopkick has over 30M downloads, but the userbase is very heterogeneous. Anecdotal evidence indicated a wide variety of users for whom the app holds long-term appeal. 2. Marketing and other teams challenged Analytics to get beyond basic summary statistics and develop a holistic segmentation of the userbase. 3. Shopkick's data science team used SQL and python to gather data, clean data, and then perform a data-driven segmentation using a k-means algorithm. 4. Interpreting the results is more work -- and more fun -- than running the algo itself. We'll discuss how we transform from ""segment 1"", ""segment 2"", etc. to something that non-analytics users (Marketing, Operations, etc.) could actually benefit from. 5. So what? How did team across Shopkick change their approach given what Analytics had discovered.

Data Con LA 2022 - Real world consumer segmentation

Data Con LA

Ravi Pillala, Chief Data Architect & Distinguished Engineer at Intuit TurboTax is one of the well known consumer software brand which at its peak serves 385K+ concurrent users. In this session, We start with looking at how user behavioral data & tax domain events are captured in real time using the event bus and analyzed to drive real time personalization with various TurboTax data pipelines. We will also look at solutions performing analytics which make use of these events, with the help of Kafka, Apache Flink, Apache Beam, Spark, Amazon S3, Amazon EMR, Redshift, Athena and Amazon lambda functions. Finally, we look at how SageMaker is used to create the TurboTax model to predict if a customer is at risk or needs help.

Data Con LA 2022 - Modernizing Analytics & AI for today's needs: Intuit Turbo...

Data Con LA

Data Con LA 2022 - Moving Data at Scale to AWS

Data Con LA

Anand Ranganathan, Chief AI Officer at Unscrambl Conversational AI is getting more and more widely used for customer support and employee support use-cases. In this session, I'm going to talk about how it can be extended for data analysis and data science use-cases ... i.e., how users can interact with a bot to ask analytical questions on data in relational databases. This allows users to explore complex datasets using a combination of text and voice questions, in natural language, and then get back results in a combination of natural language and visualizations. Furthermore, it allows collaborative exploration of data by a group of users in a channel in platforms like Microsoft Teams, Slack or Google Chat. For example, a group of users in a channel can ask questions to a bot in plain English like ""How many cases of Covid were there in the last 2 months by state and gender"" or ""Why did the number of deaths from Covid increase in May 2022"", and jointly look at the results that come back. This facilitates data awareness, data-driven collaboration and joint decision making among teams in enterprises and outside. In this talk, I'll describe how we can bring together various features including natural-language understanding, NL-to-SQL translation, dialog management, data story-telling, semantic modeling of data and augmented analytics to facilitate collaborate exploration of data using conversational AI.

Data Con LA 2022 - Collaborative Data Exploration using Conversational AI

Data Con LA

Anil Inamdar, VP & Head of Data Solutions at Instaclustr The most modernized enterprises utilize polyglot architecture, applying the best-suited database technologies to each of their organization's particular use cases. To successfully implement such an architecture, though, you need a thorough knowledge of the expansive NoSQL data technologies now available. Attendees of this Data Con LA presentation will come away with: -- A solid understanding of the decision-making process that should go into vetting NoSQL technologies and how to plan out their data modernization initiatives and migrations. -- They will learn the types of functionality that best match the strengths of NoSQL key-value stores, graph databases, columnar databases, document-type databases, time-series databases, and more. -- Attendees will also understand how to navigate database technology licensing concerns, and to recognize the types of vendors they'll encounter across the NoSQL ecosystem. This includes sniffing out open-core vendors that may advertise as â€œopen source,"" but are driven by a business model that hinges on achieving proprietary lock-in. -- Attendees will also learn to determine if vendors offer open-code solutions that apply restrictive licensing, or if they support true open source technologies like Hadoop, Cassandra, Kafka, OpenSearch, Redis, Spark, and many more that offer total portability and true freedom of use.

Data Con LA 2022 - Why Database Modernization Makes Your Data Decisions More ...

Data Con LA

Zia Khan, Computer Systems Analyst and Data Scientist at LearningFuze Data Science tutorial is designed for people who are new to Data Science. This is a beginner level session so no prior coding or technical knowledge is required. Just bring your laptop with WiFi capability. The session starts with a review of what is data science, the amount of data we generate and how companies are using that data to get insight. We will pick a business use case, define the data science process, followed by hands-on lab using python and Jupyter notebook. During the hands-on portion we will work with pandas, numpy, matplotlib and sklearn modules and use a machine learning algorithm to approach the business use case.

Data Con LA 2022 - Intro to Data Science

Data Con LA

Data Con LA 2022 - How are NFTs and DeFi Changing Entertainment

Data Con LA

Curtis ODell, Global Director Data Integrity at Tricentis Join me to learn about a new end-to-end data testing approach designed for modern data pipelines that fills dangerous gaps left by traditional data management toolsâ€”one designed to handle structured and unstructured data from any source. You'll hear how you can use unique automation technology to reach up to 90 percent test coverage rates and deliver trustworthy analytical and operational data at scale. Several real world use cases from major banks/finance, insurance, health analytics, and Snowflake examples will be presented. Key Learning Objective 1. Data journeys are complex and you have to ensure integrity of the data end to end across this journey from source to end reporting for compliance 2. Data Management tools do not test data, they profile and monitor at best, and leave serious gaps in your data testing coverage 3. Automation with integration to DevOps and DataOps' CI/CD processes are key to solving this. 4. How this approach has impact in your vertical

Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...

Data Con LA

Arif Ansari, Professor at University of Southern California Super Bowl Ad cost $7 million and each year a few Super Bowl ads go viral. The traditional A/B testing does not predict virality. Some highly shared ones reach over 60 million organic views, which can be more valuable than views on TV. Not only are these voluntary, but they are typically without distraction, and win viewer engagement in the form of likes, comments, or shares. A Super Bowl ad that wins 69 million views on YouTube (e.g., Alexa Mind Reader) costs less than 10 cents per quality view! However, the challenge is triggering virality. We developed a method to predict virality and engineer virality into Ads. 1. Prof. Gerard J. Tellis and co-authors recommended that advertisers use YouTube to tease, test, and tweak (TTT) their ads to maximize sharing and viewing. 2022 saw that maxim put into practice. 2. We developed viral Ads prediction using two scientific models: a. Prof. Gerard Tellis et al.'s model for viral prediction b. Deep Learning viral prediction using social media effect 3. The model was able to identify all the top 15 Viral Ads it performed better than the traditional agencies. 4. New proposed method is Tease, Test, Tweak, Target and Spots Ad.

Data Con LA 2022-Perfect Viral Ad prediction of Superbowl 2022 using Tease, T...

Data Con LA

Jai Bansal, Senior Manager, Data Science at Aetna This talk describes an internal data product called Member Embeddings that facilitates modeling of member medical journeys with machine learning. Medical claims are the key data source we use to understand health journeys at Aetna. Claims are the data artifacts that result from our members' interactions with the healthcare system. Claims contain data like the amount the provider billed, the place of service, and provider specialty. The primary medical information in a claim is represented in codes that indicate the diagnoses, procedures, or drugs for which a member was billed. These codes give us a semi-structured view into the medical reason for each claim and so contain rich information about members' health journeys. However, since the codes themselves are categorical and high-dimensional (10K cardinality), it's challenging to extract insight or predictive power directly from the raw codes on a claim. To transform claim codes into a more useful format for machine learning, we turned to the concept of embeddings. Word embeddings are widely used in natural language processing to provide numeric vector representations of individual words. We use a similar approach with our claims data. We treat each claim code as a word or token and use embedding algorithms to learn lower-dimensional vector representations that preserve the original high-dimensional semantic meaning. This process converts the categorical features into dense numeric representations. In our case, we use sequences of anonymized member claim diagnosis, procedure, and drug codes as training data. We tested a variety of algorithms to learn embeddings for each type of claim code. We found that the trained embeddings showed relationships between codes that were reasonable from the point of view of subject matter experts. In addition, using the embeddings to predict future healthcare-related events outperformed other basic features, making this tool an easy way to improve predictive model performance and save data scientist time.

Data Con LA 2022- Embedding medical journeys with machine learning to improve...

Data Con LA

Jie Chen, Manager Advisory, KPMG Data is the new oil. However, many organizations have fragmented data in siloed line of businesses. In this topic, we will focus on identifying the legacy patterns and their limitations and introducing the new patterns packed by Kafka's core design ideas. The goal is to tirelessly pursue better solutions for organizations to overcome the bottleneck in data pipelines and modernize the digital assets for ready to scale their businesses. In summary, we will walk through three uses cases, recommend Dos and Donts, Take aways for Data Engineers, Data Scientist, Data architect in developing forefront data oriented skills.

Data Con LA 2022 - Data Streaming with Kafka

Data Con LA

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Data Con LA 2022 - What's new with MongoDB 6.0 and Atlas

Data Con LA 2022 - Real world consumer segmentation

Data Con LA 2022 - Modernizing Analytics & AI for today's needs: Intuit Turbo...

Data Con LA 2022 - Moving Data at Scale to AWS

Data Con LA 2022 - Collaborative Data Exploration using Conversational AI

Data Con LA 2022 - Why Database Modernization Makes Your Data Decisions More ...

Data Con LA 2022 - Intro to Data Science

Data Con LA 2022 - How are NFTs and DeFi Changing Entertainment

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Data Con LA 2022-Perfect Viral Ad prediction of Superbowl 2022 using Tease, T...

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Data Con LA 2022 - Data Streaming with Kafka

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Building Real-Time Pipelines With FLaNK Timothy Spann, Principal Developer Advocate, Streaming - Cloudera Future of Data meetup, startup grind, AI Camp The combination of Apache Flink, Apache NiFi, and Apache Kafka for building real-time data processing pipelines is extremely powerful, as demonstrated by this case study using the FLaNK-MTA project. The project leverages these technologies to process and analyze real-time data from the New York City Metropolitan Transportation Authority (MTA). FLaNK-MTA demonstrates how to efficiently collect, transform, and analyze high-volume data streams, enabling timely insights and decision-making. Apache NiFi Apache Kafka Apache Flink Apache Iceberg LLM Generative AI Slack Postgresql

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

Timothy Spann

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This project demonstrates a machine learning approach to detecting credit card fraud using advanced algorithms and techniques. The project utilizes a dataset containing various features such as transaction amount, merchant location, time of transaction, and others to build a predictive model. The presentation covers data preprocessing steps, feature engineering techniques, and the selection of machine learning algorithms such as logistic regression or random forest. It also discusses model evaluation metrics and the importance of fraud detection in financial institutions for safeguarding against fraudulent activities. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Detecting Credit Card Fraud: A Machine Learning Approach

Boston Institute of Analytics

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This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Predicting Loan Approval: A Data Science Project

Boston Institute of Analytics

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