USA Law and Practice Contributed by: Irwin A. Kishner, Daniel A. Etna, Joel Wagman and Barry Werbin, Herrick, Feinstein LLP
Other states (including California, Colorado, Connecticut, Delaware, Indiana, Iowa, Montana, Nevada, Oregon, Tennessee, Texas and Virginia) have regulated how entities can use automated processing systems to utilise customers’ per - sonal data. A couple of other states (Colorado and New York) have enacted legislation aimed at preventing discrimination through automated or algorithmic decision-making tools. A few other states (including California, Colorado, and Utah) have enacted AI governance laws. Many more states are in the process of introducing pro - posed legislation aimed at further regulating AI. Applications of AI in Sports While regulation remains in the early stages, AI has already found many applications in the professional sports world. Generative AI (eg, ChatGPT) in particular, has been seen to play a role in content creation, maximising athlete performance, advertising and marketing, and operational efficiency. In content creation, AI has been used to auto - mate the generation of highlight reels by ingest - ing footage and identifying portions with action or excitement that match the user’s prompts. Another use case is exemplified by the PGA Tour, which began harnessing AI in 2018 to cre - ate automated recaps for each player. The PGA Tour’s AI collaborations have evolved over the past several years, yielding AI-produced video highlights, the tour’s “Every Shot live” feature, and a chatbot that can answer questions about golf history, betting odds, and tournament logis - tics. Further opportunities exist to develop auto - mated commentary, which could even adopt cloned voices of celebrities, athletes, or existing newscasters – subject to licensing and/or data privacy restrictions.
AI has also been harnessed to maximise perfor - mance within the sports arena, where it is used (in some cases, embedded, into shoes, rac - quets, clubs or other equipment) to analyse ath - letes’ biomechanics to optimise performance, and it has even been used to develop predictive modelling programmes to forecast (and there - fore help prevent) injuries. It can also simulate game scenarios, allowing coaches to better plan training programmes and develop play strate - gies. AI has also been utilised to help many MLB franchises ingest large amounts of player data to generate reports, thereby helping to automate the talent acquisition process, provide analytics for sports betting, and offer comprehensive data to players, coaches and fans. It has also found applications in game analytics; for example, umpire, referee and judge assistance in order to make more accurate decisions during play and competitions. AI has also presented new oppor - tunities for sports advertising. AI also promises enhanced efficiency within sta - diums. Facial recognition AI has already been introduced to streamline ticketless entry fea - tures. Other current applications, such as auto - mated checkout experiences employed by some retailers, might be utilised in sports arenas to facilitate sales of merchandise, food and bever - ages. Opportunities and Risks As with any new technology, AI presents oppor - tunities for further evaluation and has already revealed certain risks. For example, certain generative AI tools have been reported to “hal- lucinate” data, meaning that they respond to prompts with invented information. Other poten - tial stumbling blocks include licensing and other intellectual property concerns. Another main concern around AI is its potential to render the human workforce obsolete – although, as with
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