Biometric data – the future or the doom of Privacy

7

October

2021

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What is Biometric data?

Unlike with a password, that consists of letters and numbers or with an e-mail address, any collected, stored and processed biometric data, such as fingerprints or facial scans, is harder to forge, making it a reliable way to identify people. However, this also means that once this data is exposed or compromised in a data breach, it is compromised for good. While you can change your e-mail address or your telephone number, you cannot change your iris or your face as easily. Biometric data uniquely identifies a person, making the security and privacy measures implemented for the processing crucial.

But what are the current legal safeguards in place to protect biometric data?

The EU GDPR establishes a harmonized framework within the European Union for the processing of personally identifiable data. The regulation has been in place as of May 25th 2018 and is now the same for 500 million people. The E.U. data privacy law​ defines biometric data in Article 4 as “personal data resulting from specific technical processing relating to the physical, physiological or behavioural characteristics of a natural person, which allow or confirm the unique identification of that natural person, such as facial images or dactyloscopic (fingerprint) data”.

As the GDPR considers biometric data to be a special category of sensitive personal data, processing and protecting it must proceed under the framework reserved for sensitive personal data generally. While the GDPR broadly prohibits the processing of special category of personal data, it recognizes certain bases to justify its processing. Another critical aspect of the GDPR with regards to biometric data is that the GDPR expressly permits Member States to impose additional conditions and limitations on the processing of biometric data.

Several other jurisdictions, including Canada (PIPIDA), Australia (Privacy Act), and China, also strictly regulate the collection and use of biometric identifiers (CSL). A number of US states directly regulate biometric data, including Texas (Capture or Use of Biometric Identifier Act), Washington (H.B. 1493), and Illinois (BIPA). Biometric identifiers are included in the definitions of Personal Information or “Sensitive Personal Information” in California, Virginia, and Colorado. In addition, biometric data is included in a number of other states’ breach notification regulations (e.g. New York’s SHIELD Act.).

The evolving nature of biometric technology while not having a clear, unified approach to the correct processing of biometric data globally, creates a grey zone with potential high risk implications for people all over the world.

Biometric data – a potential security issue

The most recent international crisis in Afghanistan shows how Biometric data can be an active threat for citizens when not protected accordingly. The existence of a biometric system containing the personal information of millions of Afghans is one of the main concerns for the privacy and security community. This system contains millions of fingerprints, iris scans, and face photos of Afghans whose biometric information was collected by US and coalition forces. The system was built more than 15 years ago to facilitate tracking and quickly identifying people for a variety of purposes, ranging from the World Food Programme’s distribution of e-vouchers to the upkeep of an electronic national identity card system. Should this data get into the wrong hands, it can have serious safety implications for the Afghan people.

This case should act as a lesson for governments that biometric data should be protected and biometric systems should be built with security and emergency cases in mind.

References:

https://www.natlawreview.com/article/anatomy-biometric-laws-what-us-companies-need-to-know-2020

https://edps.europa.eu/data-protection/our-work/publications/techdispatch/techdispatch-12021-facial-emotion-recognition_en

https://www.thalesgroup.com/en/markets/digital-identity-and-security/government/biometrics/biometric-data

https://www.weforum.org/agenda/2021/09/untangling-the-benefits-and-risks-of-biometrics/

https://gdpr-info.eu/art-4-gdpr/

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AI knows what you will wear next summer

30

September

2021

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As one of the last industries, the fashion industry is starting to adopt technologies that will predict and design the fashion of the future. Unlike for other industries, fashion technology is still a relatively new and emerging field. Newer technologies like AI can be seen being applied in almost any industry today, and fashion companies are now beginning to understand the potential advatages that fashion technology can bring them. Fashion brands are reshaping their approach to product design and development by predicting what customers will want to wear next season.

Trend forecasting

While it may seem that new fashion trends appear just out of nowhere. In reality, the clothes we see on the runway or on the pictures of the most influential influencers are usually the result of months or years of intense observation and planning by trend forecasters.

In comparison to other emerging technologies in the fashion industry, trend forecasting is typically labour-intensive, involves manual or digital observation and data collection from fashion designers, influencers and celebrities. In order to predict trends, trend forecasters take note of runway shows and outfits worn by celebrities, but the process also includes the collection of data on entertainment, technology, social and cultural changes, politics and consumer behaviour. This data is used as the starting point to deduct what colours, shapes and fabrics will be trendy for up to two years (four seasons) in advance.

AI – a new chance for designers

With rapid changes in the way how customers buy clothes, consume content and the overall changing consumer behaviour, fashion designers want to be able to understand what their consumers want better, despite the new rules of engagement with the customer. While the way customers behave changes, simultaneously the competition in the fashion industry is increasing, with more small business brands that serve local markets emerging, increased globalization through world-wide shipping opening the markets to international fashion brands and fast fashion brands picking up and producing latest trends in the span of days. The potential in the Fashion industry is huge, the industry is expected to reach 2.25 Trillion US$ by 2025. Large fashion companies are aware of these shifts and in order to maintain their market positions, they need to be able to fulfil and meet their customers’ needs based on their ideal fashion desires and current trends.

AI is the newest tool to predict customer needs. However, while in other industries AI is used to create uniformity and consistency in the created output, in fashion AI is used to create unique and outstanding clothes for all target groups in line with current and future trends. Furthermore, AI can help to save time and money for designer by predicting what clothes are likely to sell well and which colours or fabrics will appeal to consumers before they are produced, taking into account factors such as social media popularity and appearance in magazines.

A concrete example of an AI tool used in fashion, is IBM’s Watson AI. It can analyse hundreds of thousands of images from runway shows to generate insights into what colours, patterns and silhouettes fashion brands should stock for the coming season. The algorithm ignores irrelevant data such as background types and skin tones of models, while finding and recording the most prominent colours in each image, eventually returning data on how often each colour occurs. Similar analysis can be performed on fabric patterns and find similarities between different runway shows.

RushOrder Tees – what will people wear in 2030:

Another interesting application of AI in the fashion industry is the work of the company RushOrderTees. The American-based high-quality custom apparel brand has been using AI, Generative Pre-trained Transformer (GPT)-3, and StyleGan in combination to not only design new clothes for their brand but also to predict trends.

But how does it work? Generative Pre-trained Transformer 3 (GPT-3) is a language model created by OpenAI that uses deep learning to generate human-like texts. Using identical prompts, GPT-3 was used by RushOrderTees to produce output for current and future fashion trends.  This output was lightly edited for length and repetition, but not for content or fact-checking. These texts were then used to create images using StyleGan, an AI that generates images based on text inputs. The StyleGan-generated images were given by RushOrderTees to human designers to clean up and update to ensure they looked appropriate for when presented to customers.

The following image is an example of the outfits that were created by the AI to represent the current trends in 2021:

Afterwards RushOrderTees has surveyed men and women on how they perceived the created outfits, which lead to the following results:

·  On average, 78% of women and 71% of men thought the AI-generated outfits were stylish.

·  On average, 71% of women and 67% of men would wear the AI-generated outfits

Regarding the AI-generated outfits for women, consumers felt they were all fairly accurate representations of what current fashion trends look like. Respondents of the survey also rated almost all the AI-designed looks as 6 out of 10 or higher when asked if they would wear them.

RushOrderTees went also one step further, and asked the AI to predict the trends for 2030. You can see the result of the AI below. But what do you think, will AI in the future going to be able to predict the next fashion trends or will fashion remain an answer to societal changes that no AI can predict?

References:

Next looks: Can AI technology predict the future of fashion?

https://www.forbes.com/sites/bernardmarr/2021/03/26/three-ai-and-tech-trends-that-will-transform-fashion-industry/?sh=6fd00a1f746c

https://www.forbes.com/sites/anniebrown/2021/09/15/the-changing-face-of-high-tech-fashion-how-two-entrepreneurs-are-bringing-diversity-and-ai-to-clothing-design/?sh=985443e44b40

https://www.statista.com/topics/5091/apparel-market-worldwide/

https://www.rushordertees.com/ai-fashion-trends/

Tech-savvy fashion forecasters already know what you’ll be wearing in two years

https://www.ibm.com/blogs/think/2017/03/cognitive-ny-fashion-week/

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