डॉ. बी. आर. अम्बेडकर विश्‍वविद्यालय दिल्ली
Dr. B. R. Ambedkar University Delhi
Established by The Government of NCT of Delhi

Course coordinator and team: Dr Ramesh C Sharma

 

Artificial Intelligence (AI) has emerged as a transformative force in our modern world, reshaping industries, augmenting human capabilities, and influencing societal dynamics. As AI continues to evolve and integrate into various aspects of our lives, it is imperative for students to develop a foundational understanding of its concepts, implications, and potential for Social Sciences. This course not only fosters conceptual clarity but also equips learners to navigate the power of AI's impact on industries, education, and society at large. With this knowledge, students gain a competitive edge, as they become adept at leveraging AI's capabilities to drive innovation in their careers. By mastering AI's ethical considerations, they emerge as responsible AI citizens, capable of guiding AI's trajectory toward a responsible and impactful future.

Objectives: The objectives of the course are to:

• Develop a clear understanding of the foundational concepts of Artificial Intelligence (AI), including its definition, scope, and various paradigms, to facilitate informed discussions about its implications.
• Trace the historical evolution of AI, identifying key milestones and breakthroughs that have shaped its development, and recognize the lessons learned from past successes and challenges.
• Gain familiarity with fundamental AI techniques such as machine learning, deep learning, natural language processing, and computer vision, enabling the identification of suitable AI applications in Social Sciences.
• Explore the ethical considerations inherent to AI, including bias mitigation, transparency, and accountability, and critically evaluate the potential societal impacts of AI technologies on privacy, job displacement, and fairness.

 

Brief description of modules: The course has five modules.

Module 1: Understanding Artificial Intelligence

This module explains Artificial Intelligence (AI) which encompasses a broad range of technologies and applications, shaping the landscape of modern innovation. It explores its scope and delves into the historical perspective by understanding the foundations of early AI, key milestones, and breakthroughs that have paved the way for its current capabilities. A crucial distinction in AI lies between Narrow (Weak) AI and General (Strong) AI. Narrow AI is designed for specific tasks, while General AI possesses the ability to understand, learn, and apply knowledge across diverse domains. This categorization influences the development and deployment of AI systems, showcasing the spectrum of their capabilities. Beyond technical aspects, AI's presence in popular culture has given rise to various myths and misconceptions. Dispelling these misconceptions is vital for fostering a more accurate understanding of AI's potential and limitations. Addressing ethical considerations is equally imperative in the realm of AI development. Issues such as bias, transparency, and accountability demand careful attention to ensure the responsible and fair implementation of AI technologies.

Readings

Huawei Technologies Co., L. (2022). Artificial Intelligence Technology. Singapore: Springer Nature Singapore. (Open Access BOOK). ISBN: 9789811928796, 9811928797. Publisher: Springer Nature Singapore. Author: Ltd Huawei Technologies Co.      

G. Harkut, D. (Ed.). (2019). Artificial Intelligence - Scope and Limitations. IntechOpen. doi: 10.5772/intechopen.77611 . https://www.intechopen.com/books/7795

AI on Campus: Its Impact and Implications. The Chronicle of Higher Education, 2020, https://connect.chronicle.com/rs/931-EKA-218/images/AIonCampusv6.pdf.

Aoun, Joseph E. Robot-Proof: Higher Education in the Age of Artificial Intelligence. MIT Press, 2017.

Bacalja, Alexander, Catherine Beavis, and Annemaree O’Brien. “Shifting landscapes of digital literacy.” Australian Journal of Language and Literacy, vol. 45, no. 2, June 2022, pp. 253+. Gale Academic OneFile, link.gale.com/apps/doc/A728086026/AONE?u=cuny_nytc&sid=bookmark-AONE&xid=fd6fef66. Accessed 10 Sept. 2023.

Bearman, Margaret, and Rola Ajjawi. “Learning to Work with the Black Box: Pedagogy for a World with Artificial Intelligence.” British Journal of Educational Technology, vol. 54, no. 5, Sept. 2023, pp. 1160–73. EBSCOhost, https://doi-org.citytech.ezproxy.cuny.edu/10.1111/bjet.13337.

Bierman, Dick, Joost Breuker, and Jacobijn Sandberg, editors. Artificial Intelligence and Education: Proceedings of the 4th International Conference on AI and Education, 24-26 May 1989, Amsterdam, Netherlands. IOS Press, 1989.

Doroudi, Shayan. “The Intertwined Histories of Artificial Intelligence and Education.” International Journal of Artificial Intelligence in Education, 4 Oct. 2022. https://doi-org.citytech.ezproxy.cuny.edu/10.1007/s40593-022-00313-2

Module 2: AI Techniques and Problem-Solving

Module 2 deals with a comprehensive exploration of AI Techniques and Problem-Solving. It encompasses various facets, beginning with an in-depth look at Machine Learning, which includes supervised, unsupervised, and reinforcement learning methodologies. The module further delves into the intricate realm of Deep Learning, shedding light on neural networks, their architecture, and diverse applications. Natural Language Processing (NLP) is another crucial aspect covered, focusing on language understanding and generation, revealing the pivotal role AI plays in processing and interpreting human language.

In addition, Module 2 addresses the field of Computer Vision, elucidating concepts such as image recognition, object detection, and image generation powered by AI. The module extends its reach to Robotics and Automation, exploring how AI contributes to the realms of robotics and process automation, shaping the future of industries and workflows. To provide a practical perspective, the module includes Case Studies, presenting real-world examples that showcase the application of AI techniques across different domains.

Readings

Automated Machine Learning: Methods, Systems, Challenges (Open Access Book) https://link.springer.com/book/10.1007/978-3-030-05318-5

Understanding Machine Learning: From Theory to Algorithms (2014) by Shai Shalev-Shwartz and Shai Ben-David. Cambridge University Press.

http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning

https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf

https://www.elsevier.com/journals/machine-learning-with-applications/2666-8270/open-access-journal

Machine Learning with Applications is a peer reviewed, open access journal.

http://neuralnetworksanddeeplearning.com/

https://www.deeplearningbook.org/

Module 3: AI Tools and Emerging Trends

Module 3 deals with a comprehensive exploration of AI Tools and Emerging Trends, providing a robust foundation for understanding the practical applications and evolving landscape of artificial intelligence. The module initiates by delving into AI Development Frameworks such as TensorFlow, PyTorch, and scikit-learn, elucidating their role in empowering developers to create sophisticated AI models. It then transitions to the critical aspects of data preprocessing and feature engineering, underscoring their significance in optimizing data for effective utilization in AI applications. A pivotal focus of Module 3 is on Cloud-based AI services and platforms, highlighting the integration of AI with cloud computing to enhance scalability and accessibility. The concept of Explainable AI is also explored, emphasizing the importance of interpretable models and model transparency in ensuring trust and understanding in AI systems. Additionally, the module addresses Edge AI and IoT, examining the deployment of AI at the edge of the network, showcasing its relevance in scenarios where real-time processing is essential.

To provide a forward-looking perspective, Module 3 explores Current Trends in AI, ranging from its applications in public healthcare, finance, agriculture, and smart cities. This section underscores the dynamic nature of AI and its transformative impact across diverse sectors.

Readings

High-Level Expert Group on Artificial Intelligence. (2019). A definition of AI: Main capabilities and scientific disciplines. European Commission. Available at: https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=56341

Antonio Aceves Fernandez, M., & M. Travieso-Gonzalez, C. (Eds.). (2022). Artificial Intelligence Annual Volume 2022. IntechOpen. doi: 10.5772/intechopen.109246

Soofastaei, A. (Ed.). (2019). Advanced Analytics and Artificial Intelligence Applications. IntechOpen. doi: 10.5772/intechopen.78899

https://www.intechopen.com/books/8523

http://www.iro.umontreal.ca/~bengioy/talks/lisbon-mlss-19juillet2015.pdf

 

Module 4: AI's transformative role in Social Sciences

Module 4 is dedicated to the exploration of AI's transformative role in Social Sciences, casting a comprehensive gaze on its impact within this discipline and its influence on the workforce dynamics. The module begins by scrutinizing how artificial intelligence reshapes the landscape of social sciences professions, encompassing both its disruptive potential and augmentation capabilities. It then specifically zooms in on how AI contributes to upskilling and reskilling in social sciences, dynamically adapting skill sets to meet the evolving demands of this intricate field.

A critical focal point of Module 4 involves the understanding of how AI can be tailored to the social sciences, with an emphasis on personalized learning and adaptive curricula catering to the unique needs of learners in this domain. The module expands its scope to explore additional applications of AI in social sciences, such as sentiment analysis for understanding public opinion, predictive modeling for societal trends, and data-driven insights for policy formulation.

Furthermore, the module delves into the integration of AI-powered tools for career guidance and skill assessment, demonstrating their relevance in aiding decision-making related to social sciences career paths and skill development. While exploring opportunities, the module addresses the challenges associated with integrating AI into social sciences education, acknowledging the nuances involved in this context.

Looking ahead, Module 4 not only considers current applications but also anticipates future readiness for AI-driven professions in the social sciences. It aims to equip learners with insights and strategies to navigate the evolving landscape of social sciences education, workforce expectations, and emerging application areas of AI in this dynamic field.

Readings

Lee, Raymond S. T. Artificial Intelligence in Daily Life. Springer, 2020.

Aceves-Fernandez, M. A. (Ed.). (2018). Artificial Intelligence - Emerging Trends and Applications. InTech. doi: 10.5772/intechopen.71805

https://www.intechopen.com/books/6646

Boden, M. (2018). Artificial Intelligence: A very short introduction. Oxford University Press.

Bostrom, N. (2016). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

Broussard, M. (2019). Artificial Unintelligence: How computers misunderstand the world. MIT Press.

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton and Company.

 

Module 5: Ethical and Societal Implications of AI

Module 5 deals with an in-depth examination of the Ethical and Societal Implications of AI, unraveling the complex dynamics and considerations associated with the widespread integration of artificial intelligence. The module begins by addressing Bias and Fairness in AI, exploring the sources of bias, and implementing strategies for its mitigation, as the ethical deployment of AI systems is crucial for fostering fairness and inclusivity. Privacy and Security represent another critical facet, encompassing discussions on data protection and the ethical implications of AI-enabled surveillance, delving into the delicate balance between technological advancement and individual privacy rights.

Furthermore, Module 5 delves into the intricate relationship between AI and job displacement, acknowledging and addressing the social and economic concerns arising from the automation of various roles. It also explores the ethical dimensions of AI in the realm of creativity, examining issues such as copyright, ownership, and the ethical considerations surrounding AI-generated content. The module extends its purview to the role of governments and policies in AI regulation, emphasizing the need for ethical guidelines and legal frameworks to govern the development and deployment of AI technologies.

Ethical frameworks play a pivotal role in Module 5, with discussions encompassing established principles like Asimov's Laws and the conceptualization of an AI-specific Hippocratic Oath. These frameworks serve as guiding principles to ensure the responsible and ethical use of AI.

Readings

AI Ethics in Higher Education: Insights from Africa and Beyond. (2023). Germany: Springer International Publishing. (Open Access Book). ISBN: 9783031230356. Publisher: Springer International Publishing. Editors: Caitlin C. Corrigan, Christoph Luetge, Jerry John Kponyo, Simon Atuah Asakipaam        

Artificial Intelligence - Latest Advances, New Paradigms and Novel Applications. (2021). In E. Osaba, E. Villar, J. L. Lobo, & I. Laña (Eds.), Artificial Intelligence. IntechOpen. doi: 10.5772/intechopen.87770 https://www.intechopen.com/books/9958

Grose, Thomas K. “Disruptive Influence.” ASEE Prism, vol. 32, no. 3, 2023, pp. 14–17. JSTOR, https://www.jstor.org/stable/48734149. Accessed 11 Sept. 2023.

Holmes, Wayne and Kaska Porayska-Pomsta. The Ethics of Artificial Intelligence in Education: Practices, Challenges, and Debates. Routledge, 2023.

Hervieux, Sandy and Amanda Wheatley, editors. The Rise of AI: Implications and Applications of Artificial Intelligence in Academic Libraries. Association of College and Research Libraries, 2022.

Jamieson, Sandra. “The AI ‘Crisis’ and A (Re)turn to Pedagogy.” Composition Studies, vol. 50, no. 3, Fall 2022, pp. 153-157.

Dignum, V. (2019). Responsible Artificial Intelligence: How to develop and use AI in a responsible way. Springer.

Assessment structure (modes and frequency of assessments)

Sl. No	Name of Assessment	Weightage
1	Assignment 1 based on Modules 1 and 2	30%
2	Assignment 2 based on Module 3, 4 and 5	30%
3	End Semester Exam	40%