{"id":33544,"date":"2023-10-02T03:00:00","date_gmt":"2023-10-02T10:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=33544"},"modified":"2023-09-27T14:42:10","modified_gmt":"2023-09-27T21:42:10","slug":"what-does-the-commercialization-of-generative-ai-mean-for-society","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/10\/02\/what-does-the-commercialization-of-generative-ai-mean-for-society\/","title":{"rendered":"What Does the Commercialization of Generative AI Mean for Society?"},"content":{"rendered":"\n

ACM TechBrief Lays Out Risks of Generative AI Technologies and Delineates Policy Implications for New\u00a0 “Black Box” Technology<\/p>\n\n\n

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ACM, the Association for Computing Machinery has released “TechBrief: Generative Artificial Intelligence.\u201d<\/a>\u00a0It is the latest in\u00a0the quarterly\u00a0ACM TechBriefs<\/a>\u00a0series of short technical bulletins that present scientifically grounded perspectives on the impact and policy implications of specific technological developments in computing.\u00a0<\/p>\n\n\n\n

\u201cTechBrief: Generative AI\u201d begins by laying out a core challenge: the rapid commercialization of GenAI poses multiple large-scale risks to individuals, society, and the planet that require a rapid, internationally coordinated response to mitigate.<\/p>\n\n\n\n

\u201cWhile generative AI technologies have seemingly burst onto the scene, we\u2019ve long been familiar with many of the risks they pose, though at a smaller scale,\u201d explains David Leslie, Director of Ethics and Responsible Innovation Research at The Alan Turing Institute, Professor of Ethics, Technology and Society at Queen Mary University of London, and co-lead author of the new TechBrief. \u201cWe need swift and global policy action to regulate generative AI applications as well as the so-called foundation models upon which they are built. We see this ACM TechBrief as a first step in that direction.\u201d<\/em><\/p>\n\n\n\n

The TechBrief defines GenAI as \u201ca broad set of computing technologies and techniques that enable a computer to create content such as text, code, image, audio, speech, voice, music, and video,\u201d also noting that \u201cover the past decade, the development of sophisticated neural network architectures, combined with increasing computing capacity and access to vast bodies of training data, has led to a great expansion in their utility and uptake.\u201d<\/p>\n\n\n\n

Potential harms from generative AI identified by the new TechBriefinclude misinformation, cyberattacks, and even environmental damage. To mitigate these risks, the authors contend that AI law and policy should incorporate end-to-end governance approaches that address risks comprehensively and \u201cby design.\u201d Specifically, laws and policies must address how to govern the multiphase character of GenAI systems, and the foundation models (FMs) used to construct them. <\/p>\n\n\n\n

\u201cAlthough the innovation potential of generative AI is clear, so too are risks of this technology that we must address through a complete, end-to-end governance approach that protects business and society,\u201d said Francesca Rossi, IBM AI Ethics Global Leader and co-lead author of the TechBrief. \u201cFrom critical early design-stage mechanisms such as stakeholder engagement and bias self-assessment, to implementation of guardrails in the application and post-deployment phases grounded in legal and policy measures, as well as technical features such as traceability, auditability and third-party oversight, we must establish a holistic process that ensures a GenAI system\u2019s behavior improves how we live, learn, and work while being aligned to human values.\u201d<\/em><\/p>\n\n\n\n

Noting that a few major companies have taken the lead in developing and deploying generative AI technologies, the TechBrief also emphasizes that policymakers must pay close attention to potential power imbalances at the ecosystem level that could substantially affect the public interest. The TechBrief states \u201c\u2026when large-scale private sector organizations control the critical digital infrastructures on which the production and use of FMs and GenAI applications depend, this may create distorted financial incentives to further centralize economic power and disadvantage smaller or less well-resourced commercial, academic, and public sector stakeholders. Effective AI policy should consider addressing such power asymmetries directly.\u201d<\/p>\n\n\n\n

The key conclusions of \u201cTechBrief: Generative AI\u201d are:<\/p>\n\n\n\n