An algorithm is considered fair if its results and performance are independent of sensitive variables like gender, ethnicity, etc. Fairness can be introduced at different stages of model development, such as in data collection, preparation, and model selection. Techniques for identifying and mitigating bias include causal reasoning, explainability, fairness metrics, and counterfactuals. Counterfactual fairness evaluates predictions across different protected attribute values while holding other variables constant. Explainability helps ensure models make decisions for the right reasons. Overall fairness aims to achieve equal outcomes or opportunities across groups.
How do we protect privacy of users when building large-scale AI based systems? How do we develop machine learned models and systems taking fairness, accountability, and transparency into account? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical, legal, and technical challenges encountered by researchers and practitioners alike. In this talk, we will first motivate the need for adopting a "fairness and privacy by design" approach when developing AI/ML models and systems for different consumer and enterprise applications. We will then focus on the application of fairness-aware machine learning and privacy-preserving data mining techniques in practice, by presenting case studies spanning different LinkedIn applications (such as fairness-aware talent search ranking, privacy-preserving analytics, and LinkedIn Salary privacy & security design), and conclude with the key takeaways and open challenges.
Storytelling with Data - See | Show | Tell | EngageAmit Kapoor
Stories have been recognized for their power of communication & persuasion for centuries and we need to operate at that intersection of data, visual and stories to fully harness the power of data.
I take your through a short tour of the science and the art of visualization and storytelling. Then give you an introduction through examples and exemplar on the four different layers in a data-story: See - Show - Tell - Engage.
Used in the session on Business Analytics and Intelligence at IIM Bangalore in July 2014.
Machine Learning Model Validation (Aijun Zhang 2024).pdfAijun Zhang
Developing an effective AI/ML model risk management program, with topics covering
- Understanding machine learning lifecycle in banking
- Understanding key elements of machine learning model validation
- Testing modules for conceptual soundness
- Testing modules for outcome analysis
- Developing inherently interpretable benchmark models
- Developing the automated pipeline for streamlined validation
- Enabling automated validation and monitoring for dynamically updating models
Heuristics are simple rules or mental shortcuts that allow humans to make decisions quickly and with limited information. The document discusses several types of heuristics including: the gaze heuristic, recognition heuristic, social heuristics like "do what the majority do", and heuristics based on reasons like take the best and tallying. It also covers cognitive biases like hindsight bias. Overall, the document examines how heuristics demonstrate bounded rationality and how humans use fast and frugal mental shortcuts to make decisions in an efficient manner.
Moonshot thinking aims for a 10x improvement over what currently exists, instead of a mere 10% gain. It address a huge problem, proposes a radical solution, and uses breakthrough technology to make it happen.
How to convince your boss to use insights and strategies from Behavioral Econ...beworks
Behavioral Economics has revolutionized our understanding of decision making.
We now know that humans are far from perfectly rational. Instead, there are psychological biases that strongly influence people’s choices.
The result is a more accurate prediction of human behavior, which can facilitate desirable business outcomes.
Once you understand the drivers of behavior, you can change behavior.
An algorithm is considered fair if its results and performance are independent of sensitive variables like gender, ethnicity, etc. Fairness can be introduced at different stages of model development, such as in data collection, preparation, and model selection. Techniques for identifying and mitigating bias include causal reasoning, explainability, fairness metrics, and counterfactuals. Counterfactual fairness evaluates predictions across different protected attribute values while holding other variables constant. Explainability helps ensure models make decisions for the right reasons. Overall fairness aims to achieve equal outcomes or opportunities across groups.
How do we protect privacy of users when building large-scale AI based systems? How do we develop machine learned models and systems taking fairness, accountability, and transparency into account? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical, legal, and technical challenges encountered by researchers and practitioners alike. In this talk, we will first motivate the need for adopting a "fairness and privacy by design" approach when developing AI/ML models and systems for different consumer and enterprise applications. We will then focus on the application of fairness-aware machine learning and privacy-preserving data mining techniques in practice, by presenting case studies spanning different LinkedIn applications (such as fairness-aware talent search ranking, privacy-preserving analytics, and LinkedIn Salary privacy & security design), and conclude with the key takeaways and open challenges.
Storytelling with Data - See | Show | Tell | EngageAmit Kapoor
Stories have been recognized for their power of communication & persuasion for centuries and we need to operate at that intersection of data, visual and stories to fully harness the power of data.
I take your through a short tour of the science and the art of visualization and storytelling. Then give you an introduction through examples and exemplar on the four different layers in a data-story: See - Show - Tell - Engage.
Used in the session on Business Analytics and Intelligence at IIM Bangalore in July 2014.
Machine Learning Model Validation (Aijun Zhang 2024).pdfAijun Zhang
Developing an effective AI/ML model risk management program, with topics covering
- Understanding machine learning lifecycle in banking
- Understanding key elements of machine learning model validation
- Testing modules for conceptual soundness
- Testing modules for outcome analysis
- Developing inherently interpretable benchmark models
- Developing the automated pipeline for streamlined validation
- Enabling automated validation and monitoring for dynamically updating models
Heuristics are simple rules or mental shortcuts that allow humans to make decisions quickly and with limited information. The document discusses several types of heuristics including: the gaze heuristic, recognition heuristic, social heuristics like "do what the majority do", and heuristics based on reasons like take the best and tallying. It also covers cognitive biases like hindsight bias. Overall, the document examines how heuristics demonstrate bounded rationality and how humans use fast and frugal mental shortcuts to make decisions in an efficient manner.
Moonshot thinking aims for a 10x improvement over what currently exists, instead of a mere 10% gain. It address a huge problem, proposes a radical solution, and uses breakthrough technology to make it happen.
How to convince your boss to use insights and strategies from Behavioral Econ...beworks
Behavioral Economics has revolutionized our understanding of decision making.
We now know that humans are far from perfectly rational. Instead, there are psychological biases that strongly influence people’s choices.
The result is a more accurate prediction of human behavior, which can facilitate desirable business outcomes.
Once you understand the drivers of behavior, you can change behavior.
Can we use data to train Machine Learning models, perform statistical analysis, yet without putting private data on risk? There are tools and techniques such as Federated Learning, Differential Privacy or Homomorphic Encryption enabling safer work on the data.
Data Scientist Roles and Responsibilities | Data Scientist Career | Data Scie...Edureka!
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka "Data Scientist Roles and Responsibilities" PPT talks about the various Job Descriptions and specific skill sets for the different kinds of Data Scientists that are there. It explains why Data Science is the best career move, right now. Learn about various job roles and what they actually mean and the learning path to make a career in Data Science. Below are the topics covered in this module:
What is Data Science?
Who is a Data Scientist?
Types of Data Scientists
Skills Required to Become a Data Scientist
Data Science Masters Program @Edureka
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Instagram: https://www.instagram.com/edureka_lea...
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Influencing change through presentationsTravis Isaacs
The ability to craft (and deliver) a good presentation should be in the quiver of every designer, right along side their Moleskine and Micron pens.
I use presentations to unravel a vague idea or requirement to be sure I completely understand all of the facets and details. If I can’t clearly explain a topic or idea then I need to go back to the project stakeholder and regroup. In some cases this will uncover holes that need to be address even before I start sketching out a wire frame.
Semantic Web Ontology Powerpoint Presentation SlidesSlideTeam
You can download this product from -
https://www.slideteam.net/semantic-web-ontology-powerpoint-presentation-slides.html
slideteam.net has the world's largest collection of Powerpoint Templates. Browse and Download now!
Description of this above product -
This PowerPoint presentation briefly overviews the semantic web by covering its introduction, foundation, entities and ontologies, impact, and benefits. In this Semantic Web Ontology PowerPoint Presentation, we have covered the semantic web architecture, its working, standards of the semantic web, knowledge graph, and semantic metadata. In addition, this Semantic Web Ontology PPT contains the Markups and standards that help create semantic meta-statements, standards, and rules. Also, the Semantic Web Principles PPT presentation includes the principles and layers, everything that can be identified by URIs, resources, related links and their types, partial information, and so on. Moreover, the Semantic Web Standards deck comprises business benefits of the semantic web, the relationship of the semantic web with Machine Learning, Artificial Intelligence, and other technologies. Furthermore, this Semantic Search template caters to an overview of the semantic search mechanism and importance, the growth of semantic search, steps to obtain semantic searchs benefits, and its advantages to digital marketers. It also includes a timeline and a roadmap for semantic web development. Download our 100 percent editable and customizable template, which is also compatible with Google Slides.
9 Ways to Be More Productive - Backed by ScienceD B
Everyone wants to be more productive. Officevibe created a presentation to help explain science-based ways to be more productive. All of them are simple to do and free.
You can read the entire article on our blog:
https://www.officevibe.com/blog/how-to-be-more-productive-at-work-infographic
Download our free resources about engagement and happiness:
https://www.officevibe.com/resources
Follow us on Facebook:
www.facebook.com/officevibe
Share your thoughts on Twitter !
https://twitter.com/Officevibe
Webinar: Using Behavioral Economics to Identify What Motivates Shopper BehaviorRevTrax
The presentation introduces behavioral economics and its applications for business. It discusses key concepts like mental accounting, anchoring, scarcity, and bundle framing. Experimental results are shared that tested different messaging related to these concepts. For example, adding phrases like "for your family" increased coupon savings and redemption rates. The presentation also discusses how RevTrax uses first-party purchase data and multivariate testing to better understand consumer behavior and optimize marketing efforts based on behavioral economics principles.
Data Science - Part III - EDA & Model SelectionDerek Kane
This lecture introduces the concept of EDA, understanding, and working with data for machine learning and predictive analysis. The lecture is designed for anyone who wants to understand how to work with data and does not get into the mathematics. We will discuss how to utilize summary statistics, diagnostic plots, data transformations, variable selection techniques including principal component analysis, and finally get into the concept of model selection.
This document contains a reading list and notes from a psychology for startups lecture. The key points are:
1) Mental models define how we think the world works and influence all of our decisions, but they are not always accurate. We must actively seek to improve our models.
2) Our minds are prone to predictable errors and biases like availability bias, representativeness bias, and anchoring. We must be aware of these to avoid irrational decisions.
3) When seeking advice, focus on those with broad but not too deep expertise ("foxes"), rather than experts in a single area ("hedgehogs"). Foxes are more likely to give good long-term advice.
4)
Analysis of "You may not need big data after all - Jeanne W. Ross, Cynthia M....Dheepika Chokkalingam
The document discusses how companies can improve decision making through better use of existing data resources rather than relying on big data. It argues that companies first need to learn how to effectively analyze and use the data already in their core systems to support operational decisions before pursuing big data. It provides four key practices of companies with strong evidence-based decision making cultures: 1) establishing a single source of performance data, 2) providing real-time feedback to decision makers, 3) regularly updating business rules based on facts, and 4) coaching employees to make data-driven decisions.
Analysis of "A leader's guide to data analytics - Florian Zettelmeyer"Dheepika Chokkalingam
Florian Zettelmeyer discusses the importance of analytical thinking skills over technical skills. He provides four guidelines for effective analytics: 1) Start with understanding the business problem; 2) Understand how the data was generated; 3) Leverage domain expertise to interpret results; and 4) Have a culture where established ideas can be questioned based on data, not just assumptions. The document also discusses the relevance of these concepts for Indian managers, noting analytics can help decision-making if managers have some data science knowledge to ensure quality and prevent faulty assumptions.
Can we use data to train Machine Learning models, perform statistical analysis, yet without putting private data on risk? There are tools and techniques such as Federated Learning, Differential Privacy or Homomorphic Encryption enabling safer work on the data.
Data Scientist Roles and Responsibilities | Data Scientist Career | Data Scie...Edureka!
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka "Data Scientist Roles and Responsibilities" PPT talks about the various Job Descriptions and specific skill sets for the different kinds of Data Scientists that are there. It explains why Data Science is the best career move, right now. Learn about various job roles and what they actually mean and the learning path to make a career in Data Science. Below are the topics covered in this module:
What is Data Science?
Who is a Data Scientist?
Types of Data Scientists
Skills Required to Become a Data Scientist
Data Science Masters Program @Edureka
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Instagram: https://www.instagram.com/edureka_lea...
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Influencing change through presentationsTravis Isaacs
The ability to craft (and deliver) a good presentation should be in the quiver of every designer, right along side their Moleskine and Micron pens.
I use presentations to unravel a vague idea or requirement to be sure I completely understand all of the facets and details. If I can’t clearly explain a topic or idea then I need to go back to the project stakeholder and regroup. In some cases this will uncover holes that need to be address even before I start sketching out a wire frame.
Semantic Web Ontology Powerpoint Presentation SlidesSlideTeam
You can download this product from -
https://www.slideteam.net/semantic-web-ontology-powerpoint-presentation-slides.html
slideteam.net has the world's largest collection of Powerpoint Templates. Browse and Download now!
Description of this above product -
This PowerPoint presentation briefly overviews the semantic web by covering its introduction, foundation, entities and ontologies, impact, and benefits. In this Semantic Web Ontology PowerPoint Presentation, we have covered the semantic web architecture, its working, standards of the semantic web, knowledge graph, and semantic metadata. In addition, this Semantic Web Ontology PPT contains the Markups and standards that help create semantic meta-statements, standards, and rules. Also, the Semantic Web Principles PPT presentation includes the principles and layers, everything that can be identified by URIs, resources, related links and their types, partial information, and so on. Moreover, the Semantic Web Standards deck comprises business benefits of the semantic web, the relationship of the semantic web with Machine Learning, Artificial Intelligence, and other technologies. Furthermore, this Semantic Search template caters to an overview of the semantic search mechanism and importance, the growth of semantic search, steps to obtain semantic searchs benefits, and its advantages to digital marketers. It also includes a timeline and a roadmap for semantic web development. Download our 100 percent editable and customizable template, which is also compatible with Google Slides.
9 Ways to Be More Productive - Backed by ScienceD B
Everyone wants to be more productive. Officevibe created a presentation to help explain science-based ways to be more productive. All of them are simple to do and free.
You can read the entire article on our blog:
https://www.officevibe.com/blog/how-to-be-more-productive-at-work-infographic
Download our free resources about engagement and happiness:
https://www.officevibe.com/resources
Follow us on Facebook:
www.facebook.com/officevibe
Share your thoughts on Twitter !
https://twitter.com/Officevibe
Webinar: Using Behavioral Economics to Identify What Motivates Shopper BehaviorRevTrax
The presentation introduces behavioral economics and its applications for business. It discusses key concepts like mental accounting, anchoring, scarcity, and bundle framing. Experimental results are shared that tested different messaging related to these concepts. For example, adding phrases like "for your family" increased coupon savings and redemption rates. The presentation also discusses how RevTrax uses first-party purchase data and multivariate testing to better understand consumer behavior and optimize marketing efforts based on behavioral economics principles.
Data Science - Part III - EDA & Model SelectionDerek Kane
This lecture introduces the concept of EDA, understanding, and working with data for machine learning and predictive analysis. The lecture is designed for anyone who wants to understand how to work with data and does not get into the mathematics. We will discuss how to utilize summary statistics, diagnostic plots, data transformations, variable selection techniques including principal component analysis, and finally get into the concept of model selection.
This document contains a reading list and notes from a psychology for startups lecture. The key points are:
1) Mental models define how we think the world works and influence all of our decisions, but they are not always accurate. We must actively seek to improve our models.
2) Our minds are prone to predictable errors and biases like availability bias, representativeness bias, and anchoring. We must be aware of these to avoid irrational decisions.
3) When seeking advice, focus on those with broad but not too deep expertise ("foxes"), rather than experts in a single area ("hedgehogs"). Foxes are more likely to give good long-term advice.
4)
Analysis of "You may not need big data after all - Jeanne W. Ross, Cynthia M....Dheepika Chokkalingam
The document discusses how companies can improve decision making through better use of existing data resources rather than relying on big data. It argues that companies first need to learn how to effectively analyze and use the data already in their core systems to support operational decisions before pursuing big data. It provides four key practices of companies with strong evidence-based decision making cultures: 1) establishing a single source of performance data, 2) providing real-time feedback to decision makers, 3) regularly updating business rules based on facts, and 4) coaching employees to make data-driven decisions.
Analysis of "A leader's guide to data analytics - Florian Zettelmeyer"Dheepika Chokkalingam
Florian Zettelmeyer discusses the importance of analytical thinking skills over technical skills. He provides four guidelines for effective analytics: 1) Start with understanding the business problem; 2) Understand how the data was generated; 3) Leverage domain expertise to interpret results; and 4) Have a culture where established ideas can be questioned based on data, not just assumptions. The document also discusses the relevance of these concepts for Indian managers, noting analytics can help decision-making if managers have some data science knowledge to ensure quality and prevent faulty assumptions.
This presentation describes the marketing plan of Google Play Store App - VR 360 Relax. It is created during Marketing Internship under Prof. Sameer Mathur, iIM Lucknow
Natureview Farm manufactures and markets refrigerated yogurt cups. It aims to increase revenue from $13 million to $20 million in 2001. It considers three options: 1) Expand 6 SKU cup sizes into supermarkets, 2) Expand 4 SKU larger cup sizes nationally, or 3) Introduce 2 SKU children's multipacks in natural food stores. It chooses option 2 to expand larger cup sizes nationally in supermarkets as it will generate the needed $7 million revenue increase while maintaining relationships in natural food stores.
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge