Podcast
Questions and Answers
What distinguishes prescriptive analytics from other analytical approaches?
What distinguishes prescriptive analytics from other analytical approaches?
Which of the following statements about Mean Absolute Error (MAE) is true?
Which of the following statements about Mean Absolute Error (MAE) is true?
What is the primary focus of prescriptive analytics?
What is the primary focus of prescriptive analytics?
What is a key limitation of accurate predictions in prescriptive analytics?
What is a key limitation of accurate predictions in prescriptive analytics?
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Which of the following is a method of Explainable AI (XAI)?
Which of the following is a method of Explainable AI (XAI)?
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Who are the primary stakeholders concerned with Explainable AI?
Who are the primary stakeholders concerned with Explainable AI?
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What role does Explainable AI (XAI) play in predictive modeling?
What role does Explainable AI (XAI) play in predictive modeling?
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What does algorithmic transparency refer to in the context of AI?
What does algorithmic transparency refer to in the context of AI?
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Why is algorithmic transparency important in prescriptive analytics?
Why is algorithmic transparency important in prescriptive analytics?
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What is one of the primary uses of simulations in predictive modeling?
What is one of the primary uses of simulations in predictive modeling?
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What role does simulation play in predictive modeling?
What role does simulation play in predictive modeling?
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Which of the following best describes the goal of conscientious commerce?
Which of the following best describes the goal of conscientious commerce?
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How does prescriptive analytics typically guide managerial action?
How does prescriptive analytics typically guide managerial action?
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How does prescriptive analytics differ from descriptive analytics?
How does prescriptive analytics differ from descriptive analytics?
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What is a common challenge when managers are presented with accurate models without explanations?
What is a common challenge when managers are presented with accurate models without explanations?
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Which element is NOT a feature of effective data visualization?
Which element is NOT a feature of effective data visualization?
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What is the primary goal of predictive analytics?
What is the primary goal of predictive analytics?
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In which scenario is predictive analytics especially useful?
In which scenario is predictive analytics especially useful?
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How does supervised machine learning relate to predictive analytics?
How does supervised machine learning relate to predictive analytics?
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Which option best describes the concept of algorithmic transparency?
Which option best describes the concept of algorithmic transparency?
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What is a legitimate use of predictive analytics in a business context?
What is a legitimate use of predictive analytics in a business context?
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Which method is associated with Explainable AI (XAI)?
Which method is associated with Explainable AI (XAI)?
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What does simulation in predictive modeling allow organizations to do?
What does simulation in predictive modeling allow organizations to do?
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Which is not a primary focus of predictive analytics?
Which is not a primary focus of predictive analytics?
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Study Notes
Prescriptive Analytics and Explainable AI
- The presentation focuses on AI in business contexts, specifically prescriptive analytics and explainable AI.
- The presenter, Chitu Okoli, is a Professor of Digitalization at SKEMA Business School, Paris.
- The content covers various aspects of data analytics, including the three stages: descriptive, predictive, and prescriptive.
Conscientious Commerce
- Conscientious commerce emphasizes value creation for people in every transaction.
- Pure money commerce prioritizes buying low and selling high, focusing on profit maximization.
- Conscientious commerce prioritizes fairness, good deals, and avoiding cheating.
Artificial Intelligence and Data Analytics
- The presentation highlights the importance of artificial intelligence and data analytics in business contexts.
Three Stages of Data Analytics
- Descriptive analytics involves analyzing past data to identify trends and patterns.
- Predictive analytics uses past data to predict future outcomes.
- Prescriptive analytics suggests actions to improve future outcomes based on the analysis of past and predicted data.
Descriptive Analytics and Data Visualization
- Descriptive analytics is the process of examining past data to extract useful insights.
- Data visualization presents these insights through graphs, charts, and other visual aids, making patterns and trends readily apparent.
- Visualizations should give intuitive understanding and not mislead, correctly reflecting the truth using appropriate statistical methods.
Role-Playing Exercise: Private Health Insurance
- The exercise simulates the management of a private health insurance provider in the US.
- Health insurance plans in the US typically cover all medical costs.
- The simulated plan involves members aged 66 and older.
- The exercise addresses how managers manage healthcare costs and optimize profits while caring for members.
US National Medical Expenditure Survey (NMES) Dataset
- The dataset comprises variables related to hospital stays, health status, chronic illnesses, and other individual characteristics.
- It focuses on the prediction of hospital stays.
- Data points on age, health conditions, and demographic characteristics are included
Al-powered Descriptive Analytics with Microsoft Excel
- Tools like Al-powered Excel are available to facilitate easier and more efficient data analysis.
- The presentation shows how Excel Al can streamline analysis tasks.
Predictive Analytics
- Predictive analytics analyzes past data to forecast future outcomes.
- It assumes the future will reflect past trends, and focuses on high-accuracy estimations.
Altair Al Studio (RapidMiner)
- Altair Al Studio (RapidMiner) is a tool used for creating and evaluating predictive models.
- It provides visualizations to interpret model outputs.
Which is the best-performing model to predict the number of hospital stays?
- Techniques involved compare models (and their accuracy) to predict hospital stays.
- The presentation also features measures to help assess the accuracy of different models.
Gradient Boosted Tree Model
- The model prioritizes data like income, health conditions, and other variables for predictions.
- Weights are assigned to different factors to show their significant role in the prediction outcome.
Prescriptive Analytics
- Prescriptive analytics uses past and predicted data to provide actionable suggestions for optimizing future outcomes.
- This involves understanding input features (the independent variables) and how they lead to the target outcomes.
- This is aimed at enhancing future outcomes by intervening to improve the target rather than just replicating past patterns.
Explainable AI (XAI)
- Explainable Al (XAI) helps make the workings of Al models understandable to humans.
- XAI is a way to ensure Al models' decisions are understandable and consistent to build trust.
Stakeholders of XAI
- Managers, regulatory bodies, and Al developers are stakeholders in explaining how and why Al models make particular decisions.
- Their specific concerns include the need for transparency, representation of the real world, and ethical implications of the model.
Primary Goals of Stakeholders of XAI
- Algorithmic transparency, showing how Al models arrive at their results to build trust.
- Realistic representation, the model's correspondence to real-world scenarios, and trustworthiness.
- Ethical responsibility, how an Al system handles private information and respects ethical human values
- Prescriptive actionability; implications for decisions, cause-and-effect relationships between variables, and actionable steps to shape outcomes based on the results.
XAI for Algorithmic Transparency
- Understanding how Al arrives at its decisions in human-understandable terms.
- Focusing on high-level explanations without extensive technical details.
- Primarily beneficial to Developers.
XAI for Realistic Representation
- Explaining how Al reflects real-world scenarios faithfully.
- The role of domain experts in validating real-world correspondences and use cases.
- Relevant for managers and developers focused on real-world applications in various fields.
XAI for Ethical Responsibility
- Ensuring Al respects human values like fairness, privacy, and liberty.
- Addressing potential bias in data or results and trade-offs between accuracy and explainability.
XAI for Prescriptive Actionability
- Explaining the implications of Al results for recommended human decisions.
- Enabling users to understand causality in the relationships between variables.
- Promoting use of realistic simulations of alternate scenarios.
- Helps manage actionable insights and persuade managers to adopt AI solutions.
Relationships among Stakeholders' Goals for XAI
- Dependencies and relationships among stakeholder goals of XAI are visualized through different relationships for ethical, realistic, algorithmic, and prescriptive actionability factors.
Accumulated Local Effects (ALE) Plots for XAI
- Plots used to determine the significance of features like income, medicaid status, and others based on their impact on the average predictions for hospital stays.
- Plots also display the distributions of different aspects of the dataset.
Actionable Explanation
- The process of extracting and conveying actionable insights from analysis results of AI models.
- Actionable explanation involves the steps needed to develop actionable strategies and outcomes from analysis results.
Key Attributes of Concepts in Actionable Explanation
- Explains relevance, ultimate concepts, and the controllability of observed values.
Relevance of Concepts: Ultimate
- The concept's significance is a key factor in analysis and is particularly important in finding the optimal solutions.
Relevance of Concepts: Relevant
- Relevant concepts are those proven to affect the ultimate concept.
Relevance of Concepts: Not Relevant
- Not relevant concepts are those that don't demonstrably affect the ultimate concept.
Controllability of Concepts
- High, low, or no control by managers over the influences or effects of various variables.
Managerial Implications of Controllability of Concepts
- High control helps in shaping concepts according to desired outcomes.
- Low or no control highlights the need to measure, understand, and anticipate values.
Summary
- Data analytics stages improve insights exponentially.
- Descriptive, predictive, and prescriptive approaches give a complete picture.
- Explainable Al helps managers understand the models' logic, creating more actionable insights.
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Description
This quiz explores the role of artificial intelligence in business through data analytics, focusing on prescriptive analytics and explainable AI. Discover how these concepts create value and fairness in commerce. Understand the three stages of data analytics: descriptive, predictive, and prescriptive.
