The turnaround of Airbnb

9

October

2017

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A well-known example of the sharing economy is Airbnb. Airbnb is a company with its origins in San Francisco, California. It is a community-like platform, where people from all over the world can let their apartment/house.

Airbnb facilitates the use of underutilized resources. This means that people’s homes never have to be idle for a longer period of time. Also, Aribnb claims that their guests stay 2.1 times longer than typical visitors (Airbnb, 2017). They also spend 2.1 times more than typical visitors, and also spend it in the neighbourhood, rather than one central place. In this sense, Airbnb is argued to be good for the local economy. In addition, Airbnb helps homeowners and renters with a generally moderate to lower income, who rent out a part of their home and use the respective earnings in paying their living expenses.

However, the downside of the sharing economy has received less attention, in particular for Airbnb. Typically when we look at the larger economy. If we take the hotel industry, we can understand what the downside of a sharing economy means. Most cities charge taxes for tourists who stay in hotels (Baker, 2014). People who make use of Airbnb are not paying these taxes required under the law, which directly means that Airbnb allows them to evade regulations and taxes (Hello Czech Republic, 2017). In this sense, the company is facilitating rip-offs. Any national economy is highly benefited by the taxes from tourists received from the hotel industry. Some countries even depend on tourism, which makes this quite problematic for the macro economy.

A more social problem is that Airbnb also creates ghost cities, where people move out just to let their place for a profitable price (Hello Czech Republic, 2017). People fear that their beloved city will be destroyed and no one will actually live in the city anymore. In addition, neighbours often deal with nuisance, for which guests cannot really be addressed, which contrasts with hotels. When a house appears to be suitable for renting on Airbnb, the valuation of this real estate goes up for no reason, so it also becomes harder for people to buy a house in the city.

I believe stricter regulation is necessary and transparency on Airbnb’s side should be demanded in order to enforce the regulation.

Airbnb (2017). Airbnb’s positive economic impact in cities around the world. Retrieved October 08, 2017, from https://www.airbnb.com/economic-impact

Baker, D. (2014, May 27). Don’t Buy The Hype – Airbnb And Uber Are Terrible For The Economy. Retrieved October 08, 2017, from http://www.businessinsider.com/airbnb-and-uber-are-terrible-for-the-economy-2014-5?international=true&r=US&IR=T

Hello Czech Republic (2017) Prague’s central district warns of Airbnb ghost town scenario. Retrieved October 08, 2017, from http://www.czech.cz/en/Touristen/Prague%E2%80%99s-central-district-warns-of-Airbnb-ghost-to

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Predictive policing: a good or a bad thing?

17

September

2017

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Last year, the Dutch National Police launched a system that will predict crimes using accurate and specific data from criminality. The Dutch National Police have already experimented with this Crime Anticipation System (CAS) in Amsterdam and The Hague. (NOS, 2017) With this system, the police use previous data and real-time data to make decisions about high-risk profiles. Like any other machine learning system, the CAS system classifies people as ‘low-risk’ and ‘high-risk’, and also defines ‘hotspots’ anywhere in the city. An example of the use of AI to predict crimes is by using an algorithm that uses ‘crowd analysis’ to identify suspicious patterns of individuals to classify them into a ‘low-risk’ or a ‘high-risk’ group: ‘If someone buys a kitchen knife that’s OK, but if the person also buys a sack and a hammer, that person is becoming suspicious’ (Martin, 2017).

Benefits
‘Predictive analytics that incorporate social factors and local demographics can play a significant role in enhancing the intelligence-led law enforcement that will help police anticipate crime, tackle chronic recidivism and manage risk more effectively’ (Accenture, n.d.). By using the machine learning capabilities, the police can make more informed decisions, which lead to better use of police resources.

Problems
We can use machine learning and algorithms to try to figure out who are going to be the bad types; which are the people to focus on. But those algorithms and machine learning capabilities have to be based on data we define to be sufficient as training set. We, humans, define what are high-risk characteristics. The problem with these machine-learning systems is that the National Police might be provided with striking gender and racial biases. People might be defined as ‘high-risk’, because they are part of a class higher ranking in criminal statistics, and can be precautionary arrested while having done nothing wrong.
Another problem is that criminals might learn how these machine-learning systems work, and create situations where attention is drawn to a certain place, away from where these criminal actually planned a criminal activity.

If we can figure out where the trouble is coming from, and therefore not intrude on the lives of people that are not going to be causing trouble, that is terrific. Yet again, the trick is to be honest about what is available to us, to have a frank conversation about it, to developed rules we can all see upfront, and apply those rules to make policing the best it can be.

References
Accenture, (n.d.) Policing gets smarter. Retrieved September 15, 2017, from https://www.accenture.com/us-en/insight-outlook-policing-gets-smarter-public-safety-government
NOS, (2017, May 15). Politie gaat misdaad voorspellen met nieuw systeem. Retrieved September 15, 2017, from https://nos.nl/artikel/2173288-politie-gaat-misdaad-voorspellen-met-nieuw-systeem.html
Martin, S. (2017, July 24). AI POLICE STATE: China to use technology to predict crimes BEFORE they happen. Retrieved September 15, 2017, from http://www.express.co.uk/news/science/832390/AI-minority-report-POLICE-China-predict-crimes-BEFORE-they-happen

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