Podcast
Questions and Answers
Who primarily benefits from algorithmic transparency in AI?
Who primarily benefits from algorithmic transparency in AI?
What is the focus of realistic representation in AI?
What is the focus of realistic representation in AI?
What does ethical responsibility in AI involve?
What does ethical responsibility in AI involve?
Which of the following is a primary goal of stakeholders in explainable AI (XAI)?
Which of the following is a primary goal of stakeholders in explainable AI (XAI)?
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Which professionals are mainly involved in developing AI models?
Which professionals are mainly involved in developing AI models?
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Why is the role of domain experts crucial in realistic representation of AI?
Why is the role of domain experts crucial in realistic representation of AI?
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What is necessary for a fair AI model according to ethical responsibility?
What is necessary for a fair AI model according to ethical responsibility?
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What does prescriptive actionability in XAI imply?
What does prescriptive actionability in XAI imply?
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What is the primary purpose of prescriptive actionability in XAI?
What is the primary purpose of prescriptive actionability in XAI?
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Which stakeholder is most likely to prioritize actionable insights from AI?
Which stakeholder is most likely to prioritize actionable insights from AI?
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What role does causality play in prescriptive analytics?
What role does causality play in prescriptive analytics?
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Which of the following best describes the importance of algorithmic transparency?
Which of the following best describes the importance of algorithmic transparency?
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What does interactive explanation in XAI allow users to do?
What does interactive explanation in XAI allow users to do?
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What is a critical ethical consideration in AI concerning privacy?
What is a critical ethical consideration in AI concerning privacy?
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Why might a less accurate AI model be preferred over a more accurate one?
Why might a less accurate AI model be preferred over a more accurate one?
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What do accumulated local effects (ALE) plots primarily indicate?
What do accumulated local effects (ALE) plots primarily indicate?
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What is the main intention behind regulatory compliance in AI?
What is the main intention behind regulatory compliance in AI?
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What does the median line in an ALE plot indicate?
What does the median line in an ALE plot indicate?
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What is the primary purpose of simulations in prescriptive analytics?
What is the primary purpose of simulations in prescriptive analytics?
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Which of the following is a key stakeholder in the application of explainable artificial intelligence (XAI)?
Which of the following is a key stakeholder in the application of explainable artificial intelligence (XAI)?
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What does algorithmic transparency primarily aim to achieve?
What does algorithmic transparency primarily aim to achieve?
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What is a major ethical consideration when applying AI technologies in sensitive areas such as healthcare?
What is a major ethical consideration when applying AI technologies in sensitive areas such as healthcare?
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What is meant by a realistic representation of AI models?
What is meant by a realistic representation of AI models?
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What is the primary focus when utilizing prescriptive analytics?
What is the primary focus when utilizing prescriptive analytics?
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What role do stakeholders, such as managers or product owners, play in the context of Explainable AI (XAI)?
What role do stakeholders, such as managers or product owners, play in the context of Explainable AI (XAI)?
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Why is having accurate predictions without explanations considered limited?
Why is having accurate predictions without explanations considered limited?
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In the context of simulations in prescriptive analytics, what is a significant benefit of using accurate predictive models?
In the context of simulations in prescriptive analytics, what is a significant benefit of using accurate predictive models?
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How can Explainable AI (XAI) contribute to algorithmic transparency?
How can Explainable AI (XAI) contribute to algorithmic transparency?
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What ethical consideration is crucial when querying the impact of AI decisions on users?
What ethical consideration is crucial when querying the impact of AI decisions on users?
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What can be inferred about the realistic representation of AI models?
What can be inferred about the realistic representation of AI models?
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What is the significance of simulations in prescriptive analytics?
What is the significance of simulations in prescriptive analytics?
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What is a potential outcome of relying completely on an accurate predictive model without understanding its basis?
What is a potential outcome of relying completely on an accurate predictive model without understanding its basis?
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What is a key factor for effective managerial action based on prescriptive analytics?
What is a key factor for effective managerial action based on prescriptive analytics?
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What role does simulation play in prescriptive analytics?
What role does simulation play in prescriptive analytics?
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Which stakeholder is primarily responsible for ensuring algorithmic transparency?
Which stakeholder is primarily responsible for ensuring algorithmic transparency?
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What is a key ethical consideration in the use of artificial intelligence?
What is a key ethical consideration in the use of artificial intelligence?
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How should realistic representation of AI models be approached?
How should realistic representation of AI models be approached?
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What best defines the role of stakeholders in explainable AI (XAI)?
What best defines the role of stakeholders in explainable AI (XAI)?
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When does algorithmic transparency become vital?
When does algorithmic transparency become vital?
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What main benefit does descriptive analytics provide in organizational decision-making?
What main benefit does descriptive analytics provide in organizational decision-making?
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Which statement about ethical AI use is correct?
Which statement about ethical AI use is correct?
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What does prescriptive analytics primarily focus on?
What does prescriptive analytics primarily focus on?
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In the context of data visualization, what is a critical characteristic?
In the context of data visualization, what is a critical characteristic?
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Study Notes
Prescriptive Analytics and Explainable Al
- Presented by Chitu Okoli, Professor of Digitalization at SKEMA Business School, Paris.
- This session is part of a PGE M1 course.
Outline for This Session
- Conscientious commerce
- Artificial intelligence and data analytics
- Descriptive analytics
- Predictive analytics
- Prescriptive analytics
- Explainable AI (XAI)
- Actionable explanation
- Evaluation of the teacher
Pure Money Commerce vs. Conscientious Commerce
- Pure money commerce: Buy low, sell high, leaving the other party "dry".
- Conscientious commerce: Creates value for people, every transaction is a good deal for both parties, being cheated is worse than cheating.
Conscientious Commerce: People Are More Valuable Than Money
- Treat others as you want to be treated.
- Helping people is valuable. Money is a tool, not a replacement for intrinsic worth.
Conscientious Commerce: Employees Are More Valuable Than Customers
- Employees are the real people, long-term relationships are key.
- Organizational profitability should support employee livelihood, not just increase wealth.
- Sacrificing employees for more profit, is driven by the love of money, it replaces happy employees.
A Few Like-Minded References
- "Be Happy. Be Audacious" (Harvard Business Review)
- "Five Reasons Employees Are More Important Than Customers" (Harvard Business Review)
- Forbes articles on the topic.
- "Why value my employees before my customers?" on Monster.com
Artificial Intelligence and Data Analytics
- Includes physical sensing, knowledge representation, and reasoning.
- Machine learning includes: supervised learning, unsupervised learning, deep learning, and natural language processing.
- Data mining discovers insights from large datasets, including data preparation, data analytics, and data visualization.
- Business intelligence uses data-guided organizational decisions, such as data gathering, extraction, transformation, loading(ETL), data warehousing, dashboards, reports, and OLAP analysis.
Three Stages of Data Analytics
- Descriptive analytics (visualizing past data to understand patterns).
- Predictive analytics (estimating future outcomes based on past data).
- Prescriptive analytics (suggesting action to improve future outcomes).
Descriptive Analytics and Data Visualization
- Analyzing historical data to pinpoint patterns.
- Creating data visualizations to present informative and insightful narratives.
- Data visualization should create accurate, insightful stories and not use misleading statistical tricks.
Role-Playing During Exercises: We Are Managers at a Private Health Insurer Provider
- Health insurance lets people contribute to future healthcare costs.
- Private health insurance in the US generally covers all medical costs.
- Some people have Medicaid for more basic coverage.
- Managers consider how to balance senior citizen healthcare needs with reasonable costs and potential profits.
US National Medical Expenditure Survey
- Demographics of the sample include hospital stays, health conditions, activities of daily living, region, age, race, gender, marital status, education, income, employment, private insurance, and Medicaid status.
- A significant sample involving 66+ year olds will showcase crucial insights into the health insurance sector.
Al-powered Descriptive Analytics with Microsoft Excel
- A YouTube video showcasing how to use Excel for Al-driven descriptive analysis.
Predictive Analytics
- Analyzing past data to predict the future while assuming the future will resemble the past.
- Identifying input factors that lead to desired outcomes (important).
- Prioritizing and simulating alternative scenarios for various outcomes.
Altair Al Studio (RapidMiner)
- A data analysis tool for predictive analytics.
- Features multiple visualizations like charts and diagrams related to modeling to understand the results of modeling.
Which is the Best-Performing Model to Predict Hospital Stays?
- Provides a table summarizing models for predicting hospital stays, measured by mean absolute error (lower is better), runtimes, total time, and training and scoring time.
Gradient Boosted Tree Model
- The most important factors (based on visualizations) impacting the number of hospital stays.
Prescriptive Analytics
- Analyzing past data to guide interventions improving future outcomes.
- Focusing on input factors influencing the target outcome.
- Determining priorities for interventions and simulating different scenarios based on those factors.
Explainable Al (XAI)
- Explaining AI predictions in human terms.
- High level and understandable explanation of how and why a model works in general terms.
- Helps managers in understanding how to appropriately respond to an AI analysis.
Stakeholders of XAI
- Managers/Product Owners
- Users (Consumers)
- Developers (Programmers, data scientists, etc.)
- External Regulators
Primary Goals of Stakeholders of XAI
- Algorithmic Transparency
- Realistic Representation
- Ethical Responsibility
- Prescriptive Actionability
XAI for Algorithmic Transparency
- Transparency in terms humans can understand the AI processes and resulting outputs.
- High-level explanations of models with no complex technical details required.
- Focus on the inner workings of the model, without needing technical details.
XAI for Realistic Representation
- Explains the degree of accuracy and applicability to real-world scenarios that the model accurately represents.
- Using domain experts to verify if a model is relevant and appropriate for the intended real-world application.
XAI for Ethical Responsibility
- Providing information to ensure that algorithms respect human values like fairness, free will, privacy, and more.
- Determining if the algorithm is impartial and free from biases in both the data and its use.
- Trade-off between a model's accuracy and its usability/transparency for ethical reasons.
XAI for Prescriptive Actionability
- Describing the implications of results leading to informed human decisions.
- Identifying cause-and-effect relationships to support intelligent decision making.
- Allow users to interactively simulate different scenarios and compare their results.
Relationships among Stakeholders' Goals
- A diagram illustrates the interactions among the goals of different stakeholders, including relationships between Algorithmic Transparency and Realistic Representation.
Accumulated Local Effects (ALE) Plots for XAI
- Plots used for understanding how specific variables affect predictions and outcomes for the given model.
- The relationship between numeric and categorical features with predictions are illustrated visually in the analysis.
Actionable Explanation
- Practical steps to understand AI model predictions and take action.
- Methods to decide which concepts are meaningful and which ones are not relevant to the project.
Relevance of Concepts (Ultimate, Relevant, Not Relevant)
- The different concepts impact the ultimate outcome, which ones are relevant for further research, and how to identify if a concept needs further analysis.
- Differentiating concepts based on their degree of influence and direct or indirect impact on the outcome, also how the outcome would change if there are changes in the concept.
Controllability of Concepts
- The extent to which managers can influence the concept in a particular outcome's direction.
- Degree of control over factors that could impact the outcome, and further steps to determine if the manager can manipulate the factors that influence the outcomes.
- Providing details on the degree or lack of impact a manager could influence an outcome, especially for concepts with indirect factors on an outcome
Managerial Implications of Controllability of Concepts
- For high control concepts, it's crucial managers take action to shape those concepts.
- For low/no control concepts, managers must observe and monitor their values to understand their impact on the outcomes, and anticipate how those changes will impact the overall outcomes.
Evaluation of the Teacher
- Comments should be provided to the instructor focusing on specific class sessions.
- Comments should focus on the session topics, not the homework assignments completed, and not on other instructor's sessions.
- Comments should focus on the relevance and worth of the topics covered, effectiveness of in-class activities, and classroom management.
Conclusion
- Three stages of data analytics increasingly provide insights.
- Descriptive analytics reveals past data patterns.
- Predictive analytics forecasts future outcomes.
- Prescriptive analytics directs actions to improve future outcomes.
- Explainable Al helps understand models.
- Actionable explanations help take appropriate actions based on data and models.
- Data analytics are important and useful in understanding various business scenarios.
What's Coming Up Next
- More lectures based on the schedule.
- Group project details.
- DataCamp exercises.
- Perusall social readings.
Sources
- Research papers and datasets used in the presentation.
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Description
Explore the critical concepts of prescriptive analytics and explainable AI in the context of conscientious commerce. This quiz provides insights on how artificial intelligence can create value in transactions while emphasizing the importance of ethics in data analytics. Engage with these pivotal topics presented by a leading expert in the field.
