BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Central BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260712T183358Z UID:64FF8DC9-7BFB-42D5-87A9-113AE5ADEB3E DTSTART;TZID=US/Central:20261001T090000 DTEND;TZID=US/Central:20261001T100000 DESCRIPTION:2026 Fall IEEE OKC Webinar Series | IEEE Computer Society Disti nguished Visitor\n\nIEEE Oklahoma City invites you to join the event below \, organized by Oklahoma International Publishing\, as part of its 2026 Fa ll OkIP Conferences on Thursday\, October 1\, 2026.\n\nThis virtual event is free of charge to IEEE members. Please register ahead of time to receiv e the proper instructions for remote participation:\n\n>> IEEE Computer So ciety Distinguished Visitor Speaker:\nProf. Saman Halgamuge\, Fellow of IE EE\, IET\, AAIA\, and NASSL\nFull Professor\nThe University of Melbourne\, Australia\n\n>> Talk:\nAI Social Responsibility Perspectives\n\n>>> Abstr act:\n\nThe rapid adoption of Artificial Intelligence (AI) since the break through paper on Transformers in 2017\, following breakthroughs in deep l earning and Large Language Models (LLMs)\, has transformed almost every di scipline and sector of society. In this talk\, I will first examine why th e unprecedented uptake of AI requires us to think seriously about the soci al responsibilities that accompany its development and use.\n\nFrom a tech nical perspective\, LLMs are trained on vast corpora of human-generated te xt and subsequently fine-tuned to produce user-friendly and task-specific outputs. Their capabilities arise from the collective knowledge\, creativi ty\, and experiences embedded in this human-curated data. However\, the sa me dependence on human-generated content also makes these systems vulnerab le to bias\, manipulation\, misinformation\, and misuse.\n\nThe societal i mplications of deploying LLMs trained on largely unseen datasets deserve c ritical examination. Questions of fairness\, accountability\, transparency \, and regulation become increasingly important when a small number of ins titutions control the development of systems that influence millions of pe ople. I argue that several concerning trends have emerged alongside the ra pid adoption of AI:\n\nOverreliance on AI-generated answers: Many users in creasingly assume that LLMs provide correct answers in the same way a calc ulator produces a correct numerical result. This misconception is particul arly concerning when young people seek advice from AI systems on personal\ , educational\, or emotional matters\, potentially replacing trusted frien ds\, educators\, or trained professionals.\nUnconscious influence on human behaviour: Commercial\, political\, or cultural biases embedded (e.g. mar keting information) within training data may subtly shape opinions\, prefe rences\, and consumption habits without users being fully aware of the inf luence.\nErosion of human expertise: As AI tools increasingly automate wri ting\, coding\, design\, and analytical tasks\, professionals may lose imp ortant skills if education and assessment systems do not continue to culti vate and evaluate independent human capability.\nMalicious adaptation of A I systems: AI technologies can be retrained or manipulated to spread misin formation\, deepen social divisions\, undermine cultures\, or destabilise communities and nations.\nAI privilege and geopolitical inequality: Access to advanced AI increasingly depends on computing infrastructure\, special ised chips\, data centres\, and energy resources\, which raises concerns t hat AI may reinforce existing global inequalities\, concentrating power an d influence among a small number of nations and corporations.\nThese conce rns highlight the need for a broader discussion of social responsibility\, extending beyond the technology itself to encompass the institutions\, go vernments\, and societies that develop and deploy it.\n\nThe second part o f the talk will explore the social responsibility associated with the futu re trajectory of AI. Technologies and their future trajectories are rarely neutral\; they are shaped by the priorities\, values\, and resources of t he societies that create them. AI is no exception. Around the world\, nati ons are competing to become regional or global AI powers\, investing heavi ly in infrastructure\, talent\, and innovation.\n\nYet the benefits of AI should not be limited to those with economic\, political\, or technologica l advantages. While AI will undoubtedly transform labour markets\, as prev ious technological revolutions have done\, social responsibility demands t hat we also consider the wellbeing of those whose livelihoods and communit ies may be disrupted. Human dignity\, fairness\, and inclusion must remain central considerations in the AI transition.\n\nSimilarly\, ethical claim s about AI cannot be accepted uncritically if the values embedded within t hese systems are not transparent. Responsible AI requires openness about d esign choices\, training processes\, governance structures\, and accountab ility mechanisms.\n\nFinally\, AI's environmental footprint must also be c onsidered. Training and operating large-scale AI systems require substanti al amounts of energy\, water\, computing hardware\, and critical materials . The pursuit of AI advancement should not come at the expense of environm ental sustainability or equitable access to resources.\n\nThe talk will co nclude with a brief overview of our ongoing research addressing these chal lenges and exploring pathways towards a more socially responsible and incl usive AI future.\n\n>>> About the speaker:\n\nProf Saman Halgamuge\, Fello w of IEEE\, IET\, AAIA and NASSL is a Professor at The University of Melbo urne. Previously\, he was a member of the Australian Research Council gran t assessment panel and the Head of Engineering School at Australian Nation al University. He also served as Associate Dean for the Faculty of Enginee ring at the University of Melbourne.\n\nHe obtained the Dipl.-Ing and Ph.D . degrees in data engineering from the Technical University of Darmstadt\, Germany. He is listed as a top 2% most cited researcher for AI and Image Processing in the Stanford database. He is a distinguished visitor appoint ed by the IEEE Computer Society (2025-27) and was a distinguished Lecturer of IEEE Computational Intelligence Society (2018-21).\n\nHis research is funded by Australian Research Council\, National Health and Medical Resear ch Council\, US DoD Biomedical Research program and international industry (e.g. Bosch Germany\, Google US).\n\nHe graduated over 50 PhD students in Australia. https://scholar.google.com.au/citations?hl=en&user=9cafqywAAAA J&pagesize=80&view_op=list_works&sortby=pubdate\n\nCo-sponsored by: Pierre Tiako\n\nAgenda: \n08:55am - 09:00am Virtual Meeting Speaker Introduction \n\n09:00am - 09:45am Virtual Meeting Keynote\n\n09:45pm - 10:00am Virtual Meeting Q &A\n\nVirtual: https://events.vtools.ieee.org/m/567625 LOCATION:Virtual: https://events.vtools.ieee.org/m/567625 ORGANIZER:Pierretiako@yahoo.com SEQUENCE:23 SUMMARY:AI Social Responsibility Perspectives URL;VALUE=URI:https://events.vtools.ieee.org/m/567625 X-ALT-DESC:Description: <br /><p>2026 Fall IEEE OKC Webinar Series | IEEE C omputer Society Distinguished Visitor</p>\n<p>IEEE Oklahoma City invites y ou to join the event below\, organized by Oklahoma International Publishin g\, as part of its 2026 Fall OkIP Conferences on Thursday\, October 1\, 20 26.</p>\n<p><br>This virtual event is free of charge to IEEE members. Plea se register ahead of time to receive the proper instructions for remote pa rticipation:</p>\n<p>&gt\;&gt\; IEEE Computer Society Distinguished Visito r Speaker:<br>Prof. Saman Halgamuge\, Fellow of IEEE\, IET\, AAIA\, and NA SSL<br>Full Professor<br>The University of Melbourne\, Australia<br><br><b r></p>\n<p>&gt\;&gt\; Talk:<br>AI Social Responsibility Perspectives</p>\n <p>&gt\;&gt\;&gt\; Abstract:</p>\n<p>The rapid adoption of Artificial Inte lligence (AI) since the break through paper on Transformers in 2017\, foll owing breakthroughs in deep learning and Large Language Models (LLMs)\, ha s transformed almost every discipline and sector of society. In this talk\ , I will first examine why the unprecedented uptake of AI requires us to t hink seriously about the social responsibilities that accompany its develo pment and use.</p>\n<p>From a technical perspective\, LLMs are trained on vast corpora of human-generated text and subsequently fine-tuned to produc e user-friendly and task-specific outputs. Their capabilities arise from t he collective knowledge\, creativity\, and experiences embedded in this hu man-curated data. However\, the same dependence on human-generated content also makes these systems vulnerable to bias\, manipulation\, misinformati on\, and misuse.</p>\n<p>The societal implications of deploying LLMs train ed on largely unseen datasets deserve critical examination. Questions of f airness\, accountability\, transparency\, and regulation become increasing ly important when a small number of institutions control the development o f systems that influence millions of people. I argue that several concerni ng trends have emerged alongside the rapid adoption of AI:&nbsp\;</p>\n<p> Overreliance on AI-generated answers: Many users increasingly assume that LLMs provide correct answers in the same way a calculator produces a corre ct numerical result. This misconception is particularly concerning when yo ung people seek advice from AI systems on personal\, educational\, or emot ional matters\, potentially replacing trusted friends\, educators\, or tra ined professionals.<br>Unconscious influence on human behaviour: Commercia l\, political\, or cultural biases embedded (e.g. marketing information) w ithin training data may subtly shape opinions\, preferences\, and consumpt ion habits without users being fully aware of the influence.<br>Erosion of human expertise: As AI tools increasingly automate writing\, coding\, des ign\, and analytical tasks\, professionals may lose important skills if ed ucation and assessment systems do not continue to cultivate and evaluate i ndependent human capability.<br>Malicious adaptation of AI systems: AI tec hnologies can be retrained or manipulated to spread misinformation\, deepe n social divisions\, undermine cultures\, or destabilise communities and n ations.<br>AI privilege and geopolitical inequality: Access to advanced AI increasingly depends on computing infrastructure\, specialised chips\, da ta centres\, and energy resources\, which raises concerns that AI may rein force existing global inequalities\, concentrating power and influence amo ng a small number of nations and corporations.<br>These concerns highlight the need for a broader discussion of social responsibility\, extending be yond the technology itself to encompass the institutions\, governments\, a nd societies that develop and deploy it.</p>\n<p>The second part of the ta lk will explore the social responsibility associated with the future traje ctory of AI. Technologies and their future trajectories are rarely neutral \; they are shaped by the priorities\, values\, and resources of the socie ties that create them. AI is no exception. Around the world\, nations are competing to become regional or global AI powers\, investing heavily in in frastructure\, talent\, and innovation.</p>\n<p>Yet the benefits of AI sho uld not be limited to those with economic\, political\, or technological a dvantages. While AI will undoubtedly transform labour markets\, as previou s technological revolutions have done\, social responsibility demands that we also consider the wellbeing of those whose livelihoods and communities may be disrupted. Human dignity\, fairness\, and inclusion must remain ce ntral considerations in the AI transition.</p>\n<p>Similarly\, ethical cla ims about AI cannot be accepted uncritically if the values embedded within these systems are not transparent. Responsible AI requires openness about design choices\, training processes\, governance structures\, and account ability mechanisms.</p>\n<p>Finally\, AI's environmental footprint must al so be considered. Training and operating large-scale AI systems require su bstantial amounts of energy\, water\, computing hardware\, and critical ma terials. The pursuit of AI advancement should not come at the expense of e nvironmental sustainability or equitable access to resources.</p>\n<p>The talk will conclude with a brief overview of our ongoing research addressin g these challenges and exploring pathways towards a more socially responsi ble and inclusive AI future.</p>\n<p>&nbsp\;</p>\n<p>&gt\;&gt\;&gt\; About the speaker:</p>\n<p>Prof Saman Halgamuge\, Fellow of IEEE\, IET\, AAIA a nd NASSL is a Professor at The University of Melbourne. Previously\, he wa s a member of the Australian Research Council grant assessment panel and t he Head of Engineering School at Australian National University. He also s erved as Associate Dean for the Faculty of Engineering at the University o f Melbourne.</p>\n<p>He obtained the Dipl.-Ing and Ph.D. degrees in data e ngineering from the Technical University of Darmstadt\, Germany. He is lis ted as a top 2% most cited researcher for AI and Image Processing in the S tanford database. He is a distinguished visitor appointed by the IEEE Comp uter Society (2025-27) and was a distinguished Lecturer of IEEE Computatio nal Intelligence Society (2018-21).</p>\n<p>His research is funded by Aust ralian Research Council\, National Health and Medical Research Council\, U S DoD Biomedical Research program and international industry (e.g. Bosch G ermany\, Google US).</p>\n<p>He graduated over 50 PhD students in Australi a. https://scholar.google.com.au/citations?hl=en&amp\;user=9cafqywAAAAJ&am p\;pagesize=80&amp\;view_op=list_works&amp\;sortby=pubdate</p>\n<p>&nbsp\; </p>\n<p>&nbsp\;</p><br /><br />Agenda: <br /><p>08:55am - 09:00am Virtual Meeting&nbsp\; Speaker Introduction</p>\n<p>09:00am - 09:45am Virtual Mee ting Keynote</p>\n<p>09:45pm - 10:00am Virtual Meeting Q &amp\;A</p> END:VEVENT END:VCALENDAR