How Waze uses Crowdsourcing in its best Waze

13

October

2019

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Have you ever peacefully driven down the road, when suddenly a huge wall of cars hit you? You quickly try to switch lanes, or you try to take the first turn, however, no matter what you try to do soon you are completely stuck in all the traffic. In recent years, traffic congestion has become a major problem in cities due to the booming concentration of population and activities in urban areas. Today, 55% of the world’s population lives in urban areas and this number is expected to reach 65% by 2050 (United Nations, 2018). Navigating through the maze of traffic congestion is for many people one of life’s biggest headaches, unless you use the ‘Waze’ application.

waze

Waze is a free, real-time, crowdsourced traffic- and navigation application empowered by word’s largest community of drivers. By using GPS navigation software, Waze calculates routes to help drivers navigate to their destination, warns about potential traffic congestion on the road and suggests the optimal, shortest or fastest routes to this destination (Harburn, 2016). Furthermore, Waze enables users to alert each other about road situations, accidents, police control or other route details (Parr, 2009). On top of that, Waze gathers real-time data from its users (drivers in this case) to monitor and relay traffic information for its maps in more than 185 countries around the globe. This data is collected from the crowd in three ways: 1) users actively report on live events that occur on the road; 2) users passively relay information about driving speed and traffic conditions when they actively make us of Waze, or when the app is open in the background of their mobile device; 3) Waze contains a network with volunteers who continuously edit the maps that is used in the app (Muller, 2018). By doing so, Waze collects the most accurate and latest information from drivers who are currently on the road and helps other drivers of the community to save time for being stuck in traffic jam, money spend on gasoline (Harburn, 2016) as well as it may save you a fine.

Although Waze may sound as a promising solution for the rapidly increasing population and traffic in urban areas, we should also critically ask ourselves about potential risks or downsides that may occur. Since Waze redirects drivers to avoid traffic jams or cut travel times, they often suggest more dangerous alternative side roads. Can Waze be held responsible if accidents or dangerous traffic situations happen when drivers use the Waze application? Also, as Waze subtracts large amount of data from its users around the globe, we have to think about the consequences of Waze’ data collection. What can be the impact of gathering so much data (e.g. driver, drive style etc.) on our privacy and the law? Moreover, what could be the consequences if Waze misuses the data?

 

Sources:

Muller, K. (2018). How crowdsourcing is changing the waze we drive. Digital HBS. [Online] Available at:https://digital.hbs.edu/platform-rctom/submission/how-crowdsourcing-is-changing-the-waze-we-drive/

Parr, B. (2009). Waze Uses Crowdsourcing to Bring You Real-Time Traffic Info. Mashable. [Online] Available at: https://mashable.com/2009/05/18/waze/?europe=true

Harburn, L. (2016). One of the best waze to use crowdsourcing. Social Media for Business Performance. [Online] Available at: http://smbp.uwaterloo.ca/2016/06/one-of-the-best-waze-to-use-crowdsourcing/

United Nations. (2018). 68% of the world population projected to live in urban areas by 2050, says UN. United Nations. [Online] Retrieved from:  https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html

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AI a double edged-sword: What are the risks next to its promises?

5

October

2019

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Many people still see Artificial Intelligence (AI) as a science fiction dystopia. However, as artificial intelligence has improved at a fast pace in recent years, many organizations across different industries are already widely embedding AI within their business processes (Uzialko, 2019). AI gives us great potential to tackle major challenging problems we face nowadays, for example, it could optimize our electrical grids to reduce our increasing energy and it can help us to improve accuracy of medical diagnoses (Lemon, 2019). Even though this prospective might sounds promising for our society, AI may also involve some serious unexpected risks which are not considered thus far. Hence, AI is proving to be a double-edged sword, as it comes with great power but also great responsibility (Cheatham, Javanmardian, Samandari, 2019).

Yet, before organizations are able to bear the responsibilities that come with AI, they have to understand where potential risks may be hidden. Especially, because business leaders, according to Cheatham et al. (2019) “tend to overlook these pitfalls and overestimate their capability of mitigating these pitfall”. So, it is crucial to point out some of these pitfalls that organization, as it could help them to recognize the risks that come with AI before they might prey fall to. This blog highlights five potential risks, whereas the first three relate to the empowerment of AI, and the final two refer to the interaction of humans and machines driven by AI algorithms.

Risk 1 – Data difficulties. Using, sorting, connecting of data has become more complicated as the amount of unstructured data from mobile devise, social media or the Internet of Things has increased tremendously over in recent years. Consequently, companies, often unknowingly, trap into pitfalls such as unintentionally using or exposing highly private information hidden among anonymous data. It is crucial for companies to be aware of this, as they have to comply with the General Data Protection Rules (GDPR) to avoid reputation risk (Cheatham et al., 2019).

Risk 2 – Technology Trouble. Technology and process complications in the companies’ operating system can have a negative effect on the performance of the AI system (Cheatham et al., 2019).

Risk 3 – Security snags. Furthermore, a rising concern is the possibility of unauthorized parties to have access to data, such as marketing, financial or health data, which is collected by companies. Especially, if the company itself does not consider this data as sensitive at first sight, they might prey fall to unauthorized parties taking advantage of the data that fuels their AI systems. As a result, companies could experience consumer distrust leading to reputation damage, as well as regulatory consequences (Cheatham et al., 2019).

Risk 4 – Models misbehaving. Also, AI models themselves may form a potential risk for companies. AI powered models collect, track and analyse huge amounts of data, as a result they can deliver biased outcomes, draw conclusions of which the actions make no common-sense in the real-world (Uzailko, 2019) or become unstable (Cheatham et al., 2019). Bad data used to train AI can cause models to misbehave (IBM, n.d.) but AI models can also accidently misbehave. This can be exemplified by AI models accidently discriminating, like gathering zip codes and income data to create targeted advertisements for people with a only an above average amount of income (Cheatham et al., 2019).

Risk 5 – Interaction issues. The misalignment between humans and machines could be another critical pitfall, as humans often set the goals for AI machines (Cheatham et al., 2019) If we are not clear with the goals we set for AI machines or if we make script errors when developing algorithms, the AI system can become dangerous because it is not armed with the same goals that we aimed for. For instance, when traffic rules are not defined well enough in the AI system by humans, automated transportation can lead to terrible accidents (Marr, 2018).

Since AI models will be embedded within more and more business operations in the future, it is crucial for companies to recognize the potential risks of AI in order to bear the responsibilities and to deal with its consequences. If companies have a better understanding of what AI risks might drive, they will have a greater chance of catching the risks before the risks catches them (Cheatham et al, 2019). Furthermore, it enables companies to deal with the double-edged sword due to developing AI models in its best way, while anticipating on potential danger and consequences for the society as a whole.

 

Sources:

Burkhardt, B. Hohn, N. & Wigley, C. (2019). Leading your organization to responsible AI. McKinsey. [online] Retrieved from https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/leading-your-organization-to-responsible-ai

Cheatham, B. Javanmardian, K. & Samandari, H. (2019). Confronting the risks of artificial intelligence. McKinsey Quarterly. [online] Retrieved from https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/confronting-the-risks-of-artificial-intelligence

IBM. (n.d.). 5 in 5: AI and bias. IBM. [online] Retrieved from https://www.research.ibm.com/5-in-5/ai-and-bias/

Lemon, M. (2019). 5 Weird Problems AI Could Solve. Postfunnel. [online] Retrieved from https://postfunnel.com/5-weird-problems-ai-could-soon-solve/

Marr, B. (2018). Is Artificial Intelligence Dangerous? 6 AI Risks Everyone Should Know About. Forbes. [online] Retrieved from https://www.forbes.com/sites/bernardmarr/2018/11/19/is-artificial-intelligence-dangerous-6-ai-risks-everyone-should-know-about/#46d1f1542404

Uzailko, A. (2019). How Artificial Intelligence will transform business. Business News Daily. [online] Retrieved from https://www.businessnewsdaily.com/9402-artificial-intelligence-business-trends.html

 

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