AI Strategy and Governance

Go to Course: https://www.coursera.org/learn/wharton-ai-strategy-governance

Introduction

**Course Review: AI Strategy and Governance on Coursera** In an era where artificial intelligence (AI) is increasingly shaping business practices and strategies, a course that provides a comprehensive understanding of AI’s role in governance and strategy is not just beneficial, but essential. "AI Strategy and Governance" offered on Coursera dives deep into this very subject, covering crucial themes that can empower individuals and organizations to harness AI effectively while addressing the ethical complexities that arise with its use. ### Overview The "AI Strategy and Governance" course is designed for professionals seeking to understand the multifaceted impact of AI on enterprise settings. It equips learners with insights on how AI can be leveraged to gain a competitive edge while emphasizing responsible usage and governance. Throughout the course, participants will explore various AI use cases, the significance of explainable AI, and the frameworks necessary for developing ethical AI practices. ### Module Breakdown **Module 1 – Economics of AI** This module sets the foundation by examining the operational inputs of AI. Learners will delve into the economic landscape shaped by AI advancements, including aspects of competition fostered by increased cloud adoption and data utilization. The insights gained about AutoML and its implications on industry dynamics are particularly valuable for understanding how machine learning is evolving. By the end of this module, you’ll have a solid grasp of AI’s economic implications and the intricate relationship between computational hardware and privacy demands. **Module 2 – AI Innovation** In the second module, the course explores tactical applications of AI and big data analytics. By reviewing current industry use cases, students will gain a practical understanding of how AI can revolutionize sectors like BioPharma, enhancing both productivity and transformation. The insights into practical deployments of AI across different industries make this module quite impactful for anyone looking to implement innovative AI solutions in their own organizations. **Module 3 – Algorithmic Bias and Fairness** One of the most pressing issues in the AI landscape today is the challenge of bias in algorithms. This module addresses potential biases in data stemming from human behavior and the ethical responses organizations must adopt to mitigate these challenges. You will learn about data manipulation practices and the importance of data protection laws, gaining the ability to formulate algorithms that prioritize fairness and inclusivity. **Module 4 – AI Governance and Explainable AI** The final module focuses on the crucial concept of explainability in AI systems. This is a must-know for anyone involved in AI strategy, as it discusses how transparency and fairness can impact decision-making processes. It examines why explainable AI is necessary and the ethical principles guiding the creation of AI governance policies. Participants will walk away equipped with the knowledge to foster trust through ethical AI practices in their organizations. ### Final Thoughts and Recommendation Overall, "AI Strategy and Governance" is a thorough and engaging course that balances theoretical insights with practical applications, making it suitable for both newcomers to AI and seasoned professionals. The diverse modules provide a comprehensive understanding of AI’s economic relevance, innovation potential, ethical dilemmas, and governance strategies, preparing participants to make informed, responsible decisions in their organizational roles. I highly recommend this course for business leaders, data scientists, policymakers, and anyone aspiring to integrate AI into their strategic frameworks responsibly. By completing this course, you will not only enhance your knowledge but also become an advocate for ethical AI practices in your professional community. Whether you aim to innovate within your field, manage AI-related risks, or simply grasp the strategic implications of AI technology, this course could be a pivotal step in your career development. Sign up on Coursera today and start your journey towards mastering AI strategy and governance!

Syllabus

Module 1 – Economics of AI

In this module, you will begin by examining the key inputs to AI and what tools are currently used to lower the barriers of entry for AI use. Next, you will learn the economics of AI and the competition that has emerged as AI becomes more crucial to support industry needs and we see more cloud adoption. You will learn about the value of data as it is tied to Deep Learning, and how AutoML is changing the landscape of Machine Learning, and the growing competition and implications of data harvesting. By the end of this module, you will have gained knowledge about the economic implications of AI and Machine Learning and how they impact our lives in unseen ways. You will also understand the complex nature of computational hardware and how that affects consumer demand, but also the demand for privacy.

In this module, you will examine AI and data analytics to show the economical use-cases of Big Data. You will also learn about the methods and tools that are being used to lower the barriers of entry for AI use. You will review current examples of Big Data and how those firms are using their analytical tools to enhance productivity and transformation. Lastly, you will get an in-depth look at how AI can be used in BioPharma and how the payoff of their AI investment is revitalizing their industry. By the end of this module, you will have a firm grasp on the practical deployment of AI across different industries, their use-cases, and how you can best implement them to drive innovation and transformation within business.

In this module, you will examine the inherent bias that can exist within data based on human behaviors. Building on these foundations, you will explore different responses within algorithmic bias and how organizations should respond and overcome these challenges. You will then review the manipulation of data, the different kinds of manipulation, and ways to ethically approach these issues. Lastly, you will examine data protection and the legal frameworks that exist to protect the consumer and individual data, and the stages of the privacy lifecycle. By the end of this module, you will have a thorough understanding of data biases, manipulation, and ethical questions of how data is handled and stored. You will be able to implement fairer algorithms and understand the legal ramifications of improperly managing data you collect.

In this module, you will learn about explainable AI and its relationship to Deep Learning. You will also review why it is important to have explainable AI and the different approaches to creating fair algorithms and AI policies. You will also examine Explainable AI and review the necessity of equitable algorithms. You will also learn why we do not always use Explainable AI for every model, and the impacts that it can have on performance. By the end of this module, you will have gained insight into decision-making with AI and the importance of fairness and transparency in creating explainable AI systems, as well as the ethical principles and governance policies that build trust in using AI and Machine Learning.