CBDMSAI 6000: Cognitive Bias and Applied Decision-Making Strategies in ARTIFICIAL INTELLIGENCE
This Course is offered by: The Center for Cognitive Bias, Heuristics, and Human Behavior in Decision Making
| Brief Description: This six week online continuing education course identifies, explains, analyzes, evaluates, and applies tools to manage cognitive bias, heuristics, and unethical behaviors associated with Generative (GenAI) and Machine Learning (MLAI) artificial intelligence in healthcare, business, finance, engineering, policy making, manufacturing, marketing, research, communications, and criminal justice. Registered students learn to recognize, eliminate, or control the prominent existing temptations that isolate GenAI and MLA data from science-based judgment in decision making. Catalogue Description: The course guides the student through scientific evidence in the fields of computer science, software engineering, mathematics, experimental psychology, neuroscience, neurophysiology, behavioral economics, business analytics, and psychosociology in achieving recognition, understanding, and control of the multiple cognitive biases that impact decision-making strategies in artificial intelligence. The student will learn that while massive quantities of data and powerful analytic tools seek to improve the information available for effective decision-making, sophisticated tools frequently fail to eliminate uncertainty and inaccuracy in data interpretation and application. The student will also learn that private, public, and government markets are increasingly volatile due to the variations of artificial intelligence, which often lack nuance and complex contextual understanding of real-world situations, especially those involving human emotions and ethical considerations. Recognition that the certainties of decision-making outcomes are significantly impacted by rapidly changing competition, customer migration, the appearance of new markets, evolving cultures and policies, modern technologies replacing old ones, and application and outcome expectations constantly shifting will be highlighted with solutions offered to the class. Students will learn Insights on how to manage raw data, no matter the amount or source, to minimize and/or eliminate potential flawed outcomes from a wide range of unrecognized cognitive biases. Examples and case studies will demonstrate that the intuition and judgement of competent executives, healthcare providers, sales managers, research scientists, financial administrators, educators, and market analysts often find themselves trapped into viewing copious data through frames of reference limited by cognitive bias and ethical licensing. Students will learn that decision makers unquestionably require practical tools presented in this course to combine the most informative understanding of applied cognitive bias recognition and control when interpreting and applying informative descriptive and predictive analytics involved in utilizing, applying, integrating, and interpreting GenAI and MLAI. From the foundational theories of generative adversarial network learning to transformative applications in healthcare, marketing, and education, this course offers a comprehensive exploration of AI’s multifaceted vulnerabilities, regardless of its source, to multiple cognitive biases and unethical applications. The goal of this course is to encourage individual self-reflection on the dynamic and rapidly evolving landscape of AI, providing students with both foundational knowledge and forward-looking insights in applications with best practice remedies against confounding cognitive bias and unethical applications. The student will learn tools to address the challenges of technological advances that demand the decision-maker’s accurate management of outcomes and evidence-based best practices against ethical dilemmas, privacy concerns, failed outcomes, and unwanted societal implications. The course thoughtfully engages students with these issues through case studies, real-life examples, theoretical and practical insights, group discussions, innovative instructor podcast multimedia material, and timely, applicable instructor material. Expected Outcomes: * Skilled in gathering artificial intelligence evidence-based information, identifying data alternatives, assessing evidence, selection thresholds, action triggers, implementation, consequence review, and continuous quality improvement. * Identification and control of seen and unseen obstacles to effective artificial intelligence decision-making * Learning and applying techniques and tools for artificial intelligence decision-making cost-benefit analysis * Understanding how the brain processes information and makes decisions. * Self-awareness and understanding of the impact of personal cognitive biases, heuristics, and emotional influences on artificial intelligence decision-making outcomes * Improved communication and interpersonal skills in facilitating effective group decision-making. * Learning skills to recognize and avoid the potential negative outcomes of poor decision-making and impulsive behavior. Certificate Awarded: At successful course completion, the student receives a certificate of continuing education in Cognitive Bias and Applied Decision Making Strategies in Artificial Intelligence issued by the Course Professor, the University of the Incarnate Word, School of Professional Studies Dean, and the Manager, UIW Professional and Continuing Education. Roles: This continuing education course is a must for senior executives, management professionals, and scientific researchers seeking to deepen their understanding of the impact of human behavior and cognitive bias on decision making in their areas of expertise; policymakers and regulators needing to develop informed guidelines for GenAI and MLAI usage; students and academics exploring GenAI and MLAI bias and ethics; information technology experts compelled to apply the latest advancements in cognitive bias remedies; business leaders looking to implement GenAI and MLAI responsibly, and healthcare providers dedicated to bringing the best outcomes to their patients. Registration Fee: The $1850 course fee includes registration, all course materials, a signed continuing education certificate upon successful course completion, and eligibility for higher continuing education certificate levels in cognitive bias and decision-making. |