These resources are part of the course materials and they can be included in Weekly quiz and Final quiz assessment. 

 

 

VIDEO  

 

AI Is Dangerous, but Not for the Reasons You Think | Sasha Luccioni 

The Future of Artificial Intelligence – with Prof. Tom Davenport (dal minuto 10.15 al minute 28) 

 

BOOK chapters  

 

Friedman & Nissenbaum, Bias in Computer Systems (Open Access) 

Porter, Beyond the promise: implementing ethical AI (Open Access) 

  

 

 

Articles & Papers  


Sagodi, André; van Giffen, Benjamin; Schniertshauer, Johannes; Niehues, Klemens; and vom Brocke, Jan (2024) "How Audi Scales Artificial Intelligence in Manufacturing," MIS Quarterly Executive: Vol. 23: Iss. 2, Article 5. 

For organizations to realize maximum value from artificial intelligence (AI), they need the capability to scale it and must consider scaling throughout all stages of an AI innovation project. But AI scaling presents significant challenges, especially for manufacturing companies. We describe how Audi, a leading automotive manufacturer, scaled its crack detection AI solution and unlocked long-term business value in manufacturing. Based on lessons learned at Audi, we provide recommendations and actions for CIOs and senior leaders who seek to capture value through scaling AI solutions. 

 

Van Giffen, B., Herhausen, D., & Fahse, T. (2022). Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods. Journal of Business Research, 144, 93-106. https://doi.org/10.1016/j.jbusres.2022.01.076 

Over the last decade, the importance of machine learning increased dramatically in business and marketing. However, when machine learning is used for decision-making, bias rooted in unrepresentative datasets, inadequate models, weak algorithm designs, or human stereotypes can lead to low performance and unfair decisions, resulting in financial, social, and reputational losses.  

 

Feuerriegel, S., Dolata, M. & Schwabe, G. Fair AI. Bus Inf Syst Eng 62, 379–384 (2020). https://doi.org/10.1007/s12599-020-00650-3 

Information systems (IS) are currently undergoing a fundamental shift: Until recently, decision support was developed upon rule-based and thus deterministic algorithms. However, with recent advances in artificial intelligence (AI), these decision rules have been replaced by probabilistic algorithms (e.g., deep learning; see Kraus et al. 2020). Probabilistic algorithms make inferences by learning existing patterns from data and, once deployed, provide predictions for unseen data under some uncertainty. Owing to this, they are prone to biases and systematic unfairness whereby individuals or whole groups are treated disparately. 

 

Davenport, T. H., & Patil, D. J. (2022, July 15). Is data scientist still the sexiest job of the 21st century? Harvard Business Review. Retrieved from https://hbr.org/2022/07/is-data-scientist-still-the-sexiest-job-of-the-21st-century 

Ten years ago, the authors posited that being a data scientist was the “sexiest job of the 21st century.” A decade later, does the claim stand up? The job has grown in popularity and is generally well-paid, and the field is projected to... 

 

Asadi Someh, Ida & Wixom, Barb & Beath, Cynthia & Zutavern, Angela. (2022). Building an Artificial Intelligence Explanation Capability. MIS Quarterly Executive. 21. 10.17705/2msqe.00063. 

Though companies are building artificial intelligence (AI) systems and integrating them into business operations, executives are concerned about AI’s distinctive challenges (e.g., opacity) and seeking to develop new capabilities in response. We describe a new AIX explanation capability that companies must establish before their AI initiatives can thrive. This capability has four dimensions: decision tracing, bias remediation, boundary setting and value formulation. Together, these dimensions help organizations to address the challenges of model opacity, model drift, acting mindlessly and the unproven nature of AI.