Abstract:  In the first part we cover five current specific problems that motivate the needs of responsible AI: (1) discrimination (e.g., facial recognition, justice, sharing economy, language models); (2) phrenology (e.g., biometric based predictions); (3) unfair digital commerce (e.g., exposure and popularity bias); (4) stupid models (e.g., minimal adversarial AI) and (5) indiscriminate use of computing resources (e.g., large language models). These examples do have a personal bias but set the context for the second part where we address four challenges: (1) too many principles (e.g., principles vs. techniques), (2) cultural differences; (3) regulation and (4) our cognitive biases. We finish discussing what we can do to address these challenges in the near future to be able to develop responsible AI, particularly under the umbrella of the incoming regulation (European Union’s AI Act & White House’s Blueprint for an AI Bill of Rights).

Speaker Bio: Dr. Ricardo Baeza-Yates areas of expertise are information retrieval, web search and data mining, data
science and algorithms. He is currently a Professor at Northeastern University, Silicon Valley campus, since August 2017. He is also CTO of NTENT, a semantic search technology company based in California since June 2016. Before he was VP of Research at Yahoo Labs, based in Sunnyvale, California, from August 2014 to February 2016. Before he founded and led from 2006 to 2015 the Yahoo labs in Barcelona and Santiago de Chile. Between 2008 and 2012 he also oversaw Yahoo Labs in Haifa, Israel, and started the London lab in 2012. He is part time Professor at the Dept. of Information and Communication Technologies (DTIC) of the Universitat Pompeu Fabra (UPF), in Barcelona, Spain, as well as at the Dept. of Computing Science (DCC) of Universidad de Chile in Santiago. During 2005, he was an
ICREA research professor at UPF.

He obtained a Ph.D. in CS from the University of Waterloo, Canada, in 1989. Before he obtained two masters (M.Sc. CS & M.Eng. EE) and the electronics engineer degree from the University of Chile in Santiago. He is co-author of the best-seller Modern Information Retrieval textbook, published in 1999 by Addison-Wesley with a second enlarged edition in 2011, which won the ASIST 2012 Book of the Year award. He is also co-author of the 2nd edition of the Handbook of Algorithms and Data Structures, Addison-Wesley, 1991; and co-editor of Information Retrieval: Algorithms and Data Structures, Prentice-Hall, 1992, among more than 600 other publications. Within ACM he was the Chilean site director of the regional ACM Programming Contest and member of the South American steering committee from 1998 to 2005. Later he was member of the ACM Publications Board from 2007 to 2009 and of the ACM European Council from 2010 to 2014. Finally, he was elected to the ACM Council from July 2012 to June 2016. From 2002 to 2004 he was elected to the board of governors of the IEEE Computer Society. He has received the Organization of American States award for young researchers in exact sciences (1993), the Graham Medal for innovation in computing given by the University of Waterloo to distinguished ex-alumni (2007), the CLEI Latin American distinction for contributions to CS in the region (2009), and the National Award of the Chilean Association of Engineers (2010), among other distinctions. In 2003, he was the first computer scientist to be elected to the Chilean Academy of Sciences and since 2010 is a founding member of the Chilean Academy of Engineering. In 2009, he was named ACM Fellow and in 2011 IEEE Fellow.

Abstract: Recent advances in the democratization of AI have been enabling the widespread use of generative models, causing the exponential rise of fake content. Nudification of over 680,000 women by a social bot, impersonation scams worth millions of dollars, or spreading political misinformation through synthetic politicians are just the footfall of the deep fake dystopia.

As every technology is simultaneously built with its counterpart to neutralize it, this is the perfect time to fortify our eyes with deep fake detectors. Deep fakes depend on photorealism to disable our natural detectors: we cannot simply look at a video to decide if it is real. On the other hand, this realism is not preserved in physiological, biological, and physical signals of deep fakes, yet. In this talk, I will begin with presenting our renowned FakeCatcher, which detects synthetic content in portrait videos using heart beats, as a preventive solution for the emerging threat of deep fakes. Detectors blindly utilizing deep learning are not as effective in catching fake content, as generative models keep producing formidably realistic results. My key assertion follows that such signals hidden in portrait videos can be used as an implicit descriptor of authenticity, like a generalizable watermark of humans, because they are neither spatially nor temporally preserved in deep fakes. Building robust and accurate deep detectors by exhaustively analyzing heartbeats, PPG signals, eye vergence, and gaze movements of deep fake actors reinforce our perception of reality.

Moreover, we also innovate novel models to detect the source generator of any deep fake by exploiting its heart beats to unveil residuals of different generative models. Achieving leading results over both existing datasets and our recently introduced in-the-wild dataset justifies our approaches and pioneers a new dimension in deep fake research.

Speaker Bio:  Dr. Ilke Demir’s research focuses on generative models for digitizing the real world, deep fake detection and generation techniques, analysis and synthesis approaches in geospatial machine learning, and computational geometry for synthesis and fabrication. Dr. Demir has contributed to several animated feature and VR/AR short films in Pixar Animation Studios and Intel Studios, respectively. She established the research foundations of the world’s largest volumetric capture studio at Intel, bridging the gap between the creative process and AI approaches. In addition to her publications in top-tier venues, she has organized workshops, competitions, and courses in deep learning, computer vision, and graphics such as DeepGlobe, SkelNetOn, WiCV, SUMO, DLGC, EarthVision, and OpenEDS, to name a few.

Dr. Demir has been actively involved in women in science organizations, always being an advocate for women and underrepresented minorities. Currently, she is a Senior Staff Research Scientist at Intel Corporation.