Language Modeling, Ethical Considerations of Generative AI, and Responsible AI Language Modeling, Ethical Considerations of Generative AI, and Responsible AI
Editor’s note: Madiha is a speaker for ODSC East this April 23-25. Be sure to check out her talk, “Language Modeling,... Language Modeling, Ethical Considerations of Generative AI, and Responsible AI

Editor’s note: Madiha is a speaker for ODSC East this April 23-25. Be sure to check out her talk, “Language Modeling, Ethical Considerations of Generative AI, and Responsible AI,” there!

Decades of technological innovation have shaped Artificial Intelligence (AI) as we know it today, but there has never been a moment for AI quite like the present one. 

In its early stages, Artificial Intelligence primarily consisted of Machine Learning models trained to make predictions based on data. For instance, two major Machine Learning tasks are Classification, where the goal is to predict a label, and Regression, where the goal is to predict continuous values. However, breakthroughs in Artificial Intelligence over the years have led to the development of advanced forms of Machine Learning, such as Deep Learning and Reinforcement Learning, which have transformed every industry. 

Artificial Intelligence has made significant strides since its inception, evolving from simple algorithms to highly advanced Neural Networks capable of performing sophisticated tasks such as generating completely new content, including images, audio, and video. This capability is known as Generative Artificial Intelligence, which can produce content on demand. Rather than merely analyzing or classifying existing data, Generative AI can create something entirely new, whether it is text, image, audio, music, video, art, or code. 

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Building upon the exponential advancements in Deep Learning, Generative AI has attained mastery in Natural Language Processing. The driving force behind Generative AI and Large Language Models (LLMs) is Language Modeling, a Natural Language Processing technique that predicts the next word in a sequence of words. Large Language Models are revolutionary and have the power to transform all industries because human activity is predominantly described through language. Traditionally, Language Models were trained using statistical techniques like N-grams. With the advent of Deep Learning, Language Models trained using Neural Networks emerged as an alternative to N-gram Language Models. 

The emergence of Large Language Models and Text-to-Image Generators marks the onset of a new phase in the capabilities of language-based AI applications. We are currently amidst the AI-first era, where AI has drastically transformed our way of life and work. A significant advantage of this transformation is the democratization of access to knowledge and skills facilitated by Generative AI. However, this democratization also presents both benefits and challenges.

Despite the widespread popularity of Generative AI tools, concerns about the potential risks associated with Generative AI have been raised, particularly regarding ethical, legal, and environmental issues. These ethical implications have profound implications for our future. Failure to address these ethical concerns could erode trust in AI systems, limiting their potential use and adoption. If Artificial Intelligence systems are not built and designed with ethical considerations at their core, they can expand historical discrimination, perpetuate social injustice, breach privacy rights, and amplify existing biases. 

The transformative impact of AI technologies on societies has sparked ongoing and vigorous debates. Therefore, it is imperative to view AI through an ethical lens. There is a critical need to develop, assess, and deploy AI systems in a manner that is safe, trustworthy, and inclusive as well as address potential harms before they become irreversible. 

Promising efforts are underway to define, measure, and mitigate the challenges and concerns surrounding Generative AI. These challenges are being addressed through Responsible Artificial Intelligence, an approach focused on developing, assessing, and deploying AI systems in a manner that prioritizes safety, trustworthiness, fairness, and transparency. At the same time, it’s equally crucial for users to ensure that they utilize Generative AI technology ethically and responsibly, contributing to a safe present and a promising future for all. 

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Join my talk on “Language Modeling, Ethical Considerations of Generative AI, and Responsible AI” at ODSC East 2024 Women in Data Science Ignite Session to learn more about the field of Language Modeling, Ethical Considerations of Generative AI, and Responsible Artificial Intelligence to mitigate the risks associated with Generative AI and ensure the ethical and trustworthy implementation and usage of this amazing technology. I’m excited to see you at ODSC East 2024!


About the Author:

Madiha Shakil Mirza is an AI Engineer with a research background and consulting experience in Natural Language Processing (NLP), Generative AI, Machine Learning, and Deep Learning across various industries. She is currently part of the Artificial Intelligence Practice at Avanade.

Madiha graduated from the University of Minnesota with a degree in Master of Science in Computer Science. Her graduate thesis, titled “Language Models for Interpretation of English Puns,” focused on building Language Models for Computational Humor to create computers and conversational agents (voice assistants, chatbots) with humorous personalities capable of engaging in human-like conversations.

Madiha is also a published researcher in NLP, with her work on “A Feature Engineering Approach to Irony Detection in English Tweets” published in the Proceedings of The 12th International Workshop on Semantic Evaluation (June 2018), Association for Computational Linguistics, New Orleans, LA.

LinkedIn: https://www.linkedin.com/in/madihamirza/

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