Intelligent CIO Europe Issue 65 - Page 17


Identiv and collectID activate new collection of digitised jerseys for French Football Club ES Troyes AC

Identiv , a global digital security and identification leader in the Internet-of-Things ( IoT ) space , has announced that French Ligue 1 football club , ES Troyes AC , has partnered with collectID to digitise the club ’ s 2022 – 23 season jerseys . collectID also provide the club with a powerful communication tool for interacting and engaging with fans and a sales channel that extends fans ’ shopping experience .

It has done this using an innovative combination of collectID ’ s Blockchain technology and Identiv ’ s near-field communication ( NFC ) tags to offer its dedicated fans a unique collection of digitised merchandise that enhances engagement with their favourite club .
Following a successful pilot activation with players ’ jerseys in May 2022 , ES Troyes AC expanded its engagement with collectID to include all its home , away and goalkeeper jerseys sold to fans during the 2022 – 23 season .
The club ’ s jerseys are embedded with Identiv NFC tags that , when combined with collectID ’ s technology , unlock a unique digital ID , authenticated by a non-fungible token ( NFT ), providing Troyes fans access to bonus features like proof of authenticity , special promotions and giveaways , video highlights and more when tapped with a smartphone .
User data from digitised jerseys enable Troyes to gain strategic insights into its fan base . The digitised jerseys powered by Identiv and

Progress study reveals 65 % of organisations suffer from data bias

Progress , a trusted provider of application development and infrastructure software , has announced the results of its global survey , Data Bias : The Hidden Risk of AI .

Conducted by independent research firm , Insight Avenue , the Progress survey is based on interviews with more than 640 business and IT professionals – director level and above – who use data to make decisions and are using or plan to use Artificial Intelligence ( AI ) and Machine Learning ( ML ) to support their decision-making .
Biases are often inherited by cultural and personal experiences . When data is collected and used in the training of Machine Learning models , the models inherit the bias of the people building them , producing unexpected and potentially harmful outcomes . Yet , despite the potential legal and financial pitfalls associated with data bias , there is a lack of understanding of the training , processes and technology needed to tackle data bias successfully .
The Progress survey indicated that 78 % of business and IT decision-makers believe data bias will become a bigger concern as AI / ML use increases , but only 13 % are currently addressing it and have an ongoing evaluation process . The biggest barriers they see are a lack of awareness of potential biases , understanding how to identify bias as well as the lack of available expert resources , such as having access to data scientists .
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