Dr. Ricardo Baeza-Yates

In the first part, the talk sets the stage by covering irresponsible AI: (1) discrimination (e.g., facial recognition, justice); (2) phrenology (e.g., biometric-based predictions); (3) limitations (e.g., human incompetence, minimal adversarial AI), (4) indiscriminate use of computing resources (e.g., large language models) and (5) the impact of generative AI (disinformation, mental health and copyright issues). These examples do have a personal bias but set the context for the second part where the speaker will address three challenges: (1) principles & governance, (2) regulation and (3) our cognitive biases. The speaker will conclude by discussing their responsible AI initiatives and the near future.

Biography:

https://en.wikipedia.org/wiki/Ricardo_Baeza-Yates

Ricardo Baeza-Yates is a Director of Research at the Institute for Experiential AI of Northeastern University. Before, he was the VP of Research at Yahoo Labs, based in Barcelona, Spain, and later in Sunnyvale, California, from 2006 to 2016. He is the co-author of the best-seller Modern Information Retrieval textbook published by Addison-Wesley in 1999 and 2011 (2nd ed), which won the ASIST 2012 Book of the Year award. From 2002 to 2004 he was elected to the Board of Governors of the IEEE Computer Society and between 2012 and 2016 was elected to the ACM Council. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow, among other awards and distinctions. He obtained a Ph.D. in CS from the University of Waterloo, Canada, in 1989, and his areas of expertise are web search and data mining, information retrieval, bias in AI, data science, and algorithms in general.

Dr. Vishnu S. Pendyala of San Jose State University

Machine Learning and its branch, deep learning has been the workhorse of powerful Artificial Intelligence applications. Due to the intrinsic nature of deep learning, AI applications have been mired in misinformation, fairness issues, and lack of explainability to name a few causes of concern, leading to the question if machines can be good at all. Incorrect outcomes are not new to big data algorithms. When it comes to big data, accuracy is traded off for scalability and performance. The algorithms construct an approximate representation or a "sketch" of the data they work with. Stochasticity and complexity seem inevitable for AI applications to navigate the uncertain charter of predictions. Expanding on these ideas, the talk will introduce the theme of the seminar. 

Biography:

Vishnu S. Pendyala, Ph.D. is an Academic Senator at San Jose State University, chair of the IEEE Computer Society Santa Clara Valley chapter, and IEEE Computer Society Distinguished Contributor. During his 3-year term as an Association for Computing Machinery (ACM) Distinguished Speaker and before as an industry expert and now as a faculty member and researcher, he was invited to speak at 60+ engagements including conferences, faculty development programs, and other forums some of which are available on YouTube and IEEE.tv. He is a senior member of the IEEE and has over two decades of experience in the software industry in Silicon Valley. A couple of recent papers he authored with his students won best paper awards in reputed conferences. His book, “Veracity of Big Data,” and some of his other edited books on machine learning and software development are available in major libraries in USA including those of the US Congress, MIT, Stanford University, and internationally. Dr. Pendyala taught a one-week course sponsored by the Government of India’s Ministry of Human Resource Development (MHRD), under the GIAN program in 2017, to Computer Science faculty from across the country and delivered the keynote in a similar program sponsored by All India Council for Technical Education (AICTE), Government of India in 2022. Dr. Pendyala recently served on a National Science Foundation (NSF) proposal review panel. He received the Ramanujan Memorial gold medal and a shield for his college at the State Math Olympiad in Andhra Pradesh, India as an undergraduate student.

Address:San Jose, United States

Dr. Ruksana Azhu Valappil

This talk highlights the state of AI in healthcare, the transformative impact and grave challenges of AI-focused healthcare solutions and the ethical considerations they raise. The speaker will explore the value and necessity of responsible AI in healthcare, and strategies and best practices to mitigate biases and reduce harm. The talk concludes with the business case for responsible AI while helping create a more sustainable and equitable healthcare ecosystem.

Biography:

Dr. Ruksana Azhu Valappil is an accomplished digital healthcare expert, neuroscientist and social entrepreneur. She is the founder of NEEV Health, providing advisory services to the health and hospital industry on responsible AI with strategic consulting, ethical protocols, educational content development and training. A firm believer in public service, she serves as a Commissioner on the Hospital Supplier Diversity Commission at California Department of Health Care Access and Information. Dr. Valappil earned her PhD in Biomedical Engineering and holds a Bachelor’s degree in Electrical & Electronics Engineering.

Dr. Sindhu Joseph

The talk will discuss the potential implications of generative AI on employment and the workforce and explore strategies for mitigating job displacement and fostering a smooth transition to a future augmented by AI technologies.

Biography:

Dr.Sindhu Joseph is the Founder and CEO of CogniCor, the leading intelligent business automation platform that delivers revolutionary operational efficiency and conversational experience for wealth management firms. She holds a PhD in AI, co-authored 6 patents, and is a speaker on topics around Enterprise AI and ethical AI.

Wealthmanagement.com top 10 to watch in 2023 and the recipient of the Wealth Solutions Report’s 2022 Top WSR Women Leader in Wealth award, she envisions to create an experience shift and scalable efficiencies in the wealth management industry using Artificial Intelligence and automation.

Dr. Yang Liu

Ensuring alignment, which refers to making models behave in accordance with human intentions, has become a critical task before deploying large language models (LLMs) in real-world applications. For instance, OpenAI devoted six months to iteratively aligning GPT-4 before its release. However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations. This obstacle hinders the systematic iteration and deployment of LLMs. This talk discusses dimensions that we believe are important to consider and hopefully cover the majority of current concerns about LLM trustworthiness. We will also discuss the difficulties in making a large language model (LLM) more trustworthy and introduce a new approach to alignment, termed "Large Language Model Unlearning."

Biography:

Yang Liu is currently an Assistant Professor of Computer Science and Engineering at UC Santa Cruz (2019 - present). He was previously a postdoctoral fellow at Harvard University (2016 - 2018) hosted by Yiling Chen. He obtained his Ph.D. degree from the Department of EECS, University of Michigan, Ann Arbor in 2015. He is interested in crowdsourcing and algorithmic fairness in machine learning. He is a recipient of the NSF CAREER Award and the NSF Fairness in AI award (lead PI). He has been selected to participate in several high-profile projects, including DARPA SCORE and IARPA HFC. His research has been covered by media including WIRED and WSJ. His work on using machine learning to forecast future security incidents has been successfully commercialized and acquired by FICO. His recent works have won four best paper awards at relevant workshops.

http://www.yliuu.com/

https://scholar.google.com/citations?user=jKrIVCIAAAAJ&hl=en

https://www.linkedin.com/in/yang-liu-5862451a 

Dr. Jesmin Jahan

As generative AI (Gen-AI) continues to redefine the landscape of numerous industries, from drug discovery to creative arts, its integration into our socio-technical fabric necessitates a rigorous assessment of trustworthiness. Z-Inspection® emerges as a pivotal methodology in this context, offering a comprehensive framework to scrutinize AI systems across their lifecycle. This process is grounded in the European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI, yet exhibits the flexibility to adapt to diverse regulatory environments.

In this talk, we will delve into the intricacies of Z-Inspection® and its application to Gen-AI. We will explore how this holistic approach facilitates the identification and examination of ethical dilemmas, leveraging socio-technical scenarios crafted by interdisciplinary experts attuned to the specific use case. The discussion will extend to the ethical quandaries inherent in Gen-AI, including bias amplification, the generation of deceptive content, privacy erosion, opacity, human skill diminution, employment disruption, and environmental impact.

Our focus will be on preemptive analysis and the proactive cultivation of mitigation strategies through Z-Inspection®, aiming to balance the immense promise of Gen-AI with the imperative of safeguarding ethical standards. Attendees will gain insights into operationalizing trustworthiness in AI development, ensuring that the advancements in Gen-AI are not only innovative but also aligned with societal values and ethical principles.

Biography:

Dr. Jesmin Jahan Tithi is a Research Scientist at Intel Corporation, where she focuses on high-performance computing (HPC) and hardware-software codesign. She received her Ph.D. in Computer Science from Stony Brook University, New York, and her B.Sc. in Computer Science and Engineering from Bangladesh University of Engineering and Technology. She has also worked as an intern at Google, Intel, and the Pacific Northwest National Laboratory. Dr. Tithi is a leading expert in HPC and has made significant contributions to the field. She is the author of over 30 peer-reviewed publications, and her work has been featured in top academic conferences and journals.