The fashion industry has shifted from ‘pushing’ garments on the market through simple forecasting to ‘pulling’ products based on actual demand generated by social media. The power of social media means trends are now more often established by consumers, as opposed to retailers and editors. In addition, digital technology gives small brands an easy way to engage with consumers, who are increasingly glued to their smartphones and the internet. E-commerce provides access to the “endless aisle” that features many more brands than traditional TV advertising or store visits could (Amed et al. 2019). Many fashion brands have difficulties identifying the ever-changing fashion needs of their customers and competing with their competitors that are mainly operating online (Hanbury 2018). A solution for their problem may be using AI to design new trendy garments.

AI is viewed as too mechanical to capture the creative core of fashion, however, it has the possibility to blur the lines between technology and creativity. A new emerging development is that AI and machine learning are utilized in the creative process of designing garments. Thus, AI can not only help with predicting and spotting what customers are most likely to demand, but it can also give design recommendations (Amed et al. 2018).

This new implication of AI makes use of a generative adversarial network (GAN), which incorporates two deep neural networks that operate alongside each other to learn from raw data. In essence, it looks at many examples to then apply that style to existing clothing trends to predict which attributes customers prefer (Knight 2017).

The technology is still new, but Amazon has already developed an ‘AI fashion designer’ that has the ability to analyse images of a particular style of fashion and generate new garments in this same style from scratch (Knight 2017).
Therefore, fashion brands should invest in this new development since AI will not only help them to discover new trends, but it will also help them to design garments that reflect new fashion trends almost instantly. This would give them a competitive advantage compared to their competitors. In addition, lead times will decrease since the designing process will take less time.

Amed, I., Berg, A., Balchandani, A., Andersson. J., Hendrich, S. and Young, R. (2019) ‘The state of fashion 2019’. Accessed on 15 October 2019 on https://mck.co/2twu77j.

Amed, I., Berg, A., Balchandani, A., Beltrami. M., Hendrich, S. and Rölkens, F. (2018a) ‘The state of fashion 2019: A year of awakening’. Accessed on 15 October 2019 on https://mck.co/2DQwiZX.

Knight, W. (2017) ‘Amazon has developed an AI fashion designer’. Accessed on 15 October 2019 on https://bit.ly/2w0n74E.

Hanbury, M. (2018) ‘H&M is caught in a ‘vicious cycle’ of discounting, and now it’s found itself with a mountain of unsold clothes’. Accessed on 15 October 2019 on https://www.businessinsider.com/why-hm-business-is-struggling-201804?international=true&r=US&IR=T

People use their smartphones constantly. We use our smartphones to look up things on the internet, take pictures, chat, find certain locations, buy things online, make payments and the list goes on and on. A lot of valuable data and information is generated through our smartphones. This data is eventually stored on our smartphones and the internet. Most people are unaware of the fact that their smartphone data can be used to solve crimes or to put them in jail. 

In July 2017 a man called Tjeerd van Seggeren has been found dead. The 37-year old father of three went to a music festival and was eventually found dead in a meadow in De Westereen, Friesland. 

After five months the homicide still has not been solved. Still no murder weapon has been found. However, the investigation took a surprising turn and Jessica, the wife of van Seggeren, has been sentenced to 20 years of prison. The police solved the case by using the data stored on the victims’ smartphone and the smartphone of his wife. 

The police used a device called the Ufed-analyzer. This device can extract all the data from a smartphone within 20 minutes. Pictures and messages revealed that the couple had relationship problems. In addition, the search history of Jessica showed that she looked up ways how to poison someone. Messages on Tjeerd’s phone did suggest that he was suffering from severe pains and cramps previously. However, this did not kill him. 

On the day of the murder, Jessica stated that she was home that night and got into the car after midnight to pick up her husband from the festival. She could not find him at the agreed place, after which she went home and warned family members. She also wasn’t able to contact him since here phone battery died. This turned out not to be true. 

The investigation team compared the location history of the two phones and come to a shocking conclusion. Jessica has been near Tjeerd that night and must have seen him, based on the location history on Google Maps. The data on her phone showed that she did not call him while she was looking for him, she lied about turning off her phone,  about the return journey and about the period thereafter. According to the judge, Jessica followed a predetermined plan to kill her husband. She met him after the festival, walked into the meadow with him and subsequently killed him in a violent manner. 

Do you think that this is privacy infringement or do you applaud the investigation team for coming up with this idea of using smartphone data to solve a case?

Modderkolk, H. (2019). Hoe Google-data in een moordzaak leidden naar de echtgenote. Retrieved September 17, 2019, from https://www.volkskrant.nl/nieuws-achtergrond/hoe-google-data-in-een-moordzaak-leidden-naar-de-echtgenote~b092755e/