No ratings yet.

When you let Artificial Intelligence decide on the winner of a beauty contest, you probably don’t expect the system to be racist. However, with more than 6.000 applicants from over 100 countries the first AI international beauty contest was a fact. The jury was a 5 robot panel and were programmed to pick winners from the submitted photos. These were the results: the 44 winners were divided as follows: 5 were Asian, 1 was black and the rest was white (Beauty.ai, 2016).

 

I found another rather disturbing example that was highlighted by BoingBoing (2016) a couple of days ago. They did a Google image search for “Women’s professional hairstyles” and Google returned the following:

 

 

Then, they changes the Google Image search to “Women’s unprofessional hairstyles”, and Google returned the following:

 

 

Despite the exciting AI technology, these results that are based on advanced algorithms and artificial intelligence remain offensive. It might be accidental, but the need for a deeper understanding of how this bias stems from the human bias is rather crucial. According to BoingBoing (2016) it is still unclear where the bias comes from. They’ve seen that most of the ‘unprofessional’ pictures were linked to serious, aware discussion of the issue of ethnicity, hair and professional environments, while the ‘professional’ images were linked to Pinterest boards. Just think about it; these very recent examples from above imply that pale-skinned individuals are more beautiful with professional hairstyles, compared to dark-skinned people that are considered less beautiful with unprofessional hairstyles.

 

Clearly, this is only the beginning and artificial intelligence will become more part of our day-to-day lives. In the examples from above, it is obvious that the current deep learning algorithms are picking up our biases and are training the AI to a faulted next generation AI. Google is a very big firm that should represent equal rights and shouldn’t discriminate based on skin-color. Should this concern us? Shouldn’t we pay more attention to the learning algorithms, rather than fiercely focussing on adding automated AI technologies in every service and offering? Should every firm be able to just blindly adopt AI technologies?

 

If we now start with faulted and biased artificial intelligence techniques, and then gradually (over a very long period in time) people start to unconsciously accept the biased ‘reality’, our collective thoughts will eventually all become the same, right? That would be boring.

 

I do believe in the great AI possibilities, however, we should be careful about the biases we – unintentionally – program into the initial (learning) algorithms.

 

Sources:

http://winners2.beauty.ai/#win

https://boingboing.net/2016/04/06/professional-and-unprofessiona.html

 

No ratings yet.

Big data. Probably you’ve heard of it. It’s a buzzword, it’s complex, and it has many meanings and applications. In large organizations, managers use it to get quick insights to stay ahead of competition, data scientists use it to get meaningful answers, and executives use it to act on market opportunities. Understandably, most start-ups and other small-sized businesses often do not have the resources to hire data scientists, system engineers or research firms to tackle their ‘Big data’ questions. But what does Big-data mean and how does it relate to small-sized firms?

 

What is Big Data?

Let me start by saying that there is no commonly accepted definition of Big-data. According to Gordon (2013) Big-data is data that can be defined by some combination of the following five characteristics: (i) volume (ii) variety (iii) velocity (iv) value, and (v) veracity of data. Both government and large data-intensive organizations, such as Amazon and Google, are interested in Big-data and are taking the lead in some of the developments of the technologies to handle it. They are often involved in the development of so called NoSQL databases; databases that are more scalable and faster in processing large quantities of structured, semi-structured and/or unstructured data, compared to the conventional SQL databases. Storing the data is just one part of the story; it still requires analysis and visualisation. In large organizations, data scientists usually store, transform, and analyse the raw data and visualise it so that it becomes of use.

 

How does this relate to Small Businesses?

In my previous job, I’ve seen many small to medium-sized businesses and I’ve spoken to lots of entrepreneurs. Some of them were, for example, traditional accountants that were comfortable with their day-to-day operations and used ‘just’ information from their website and CRM to make strategic decisions and acquire new clients. They didn’t see, or didn’t want to see the Big-data potential. However, most of the other entrepreneurs wanted to, but were struggling with, processing and analysing both their offline and online information. In many cases, I did see that small firms almost always possessed variety and volume in valuable data (online information gathering through Google Analytics, CRM, market trends, KPI’s, NPS scores, employees networks; and offline information through: surveys, other field research, events etc.) however they simply lacked the tools in making them blend and work together. For small firms, buying a cloud-based ‘Big-Data-ready’ NoSQL database is not the problem; storing, processing and analysing the structured and unstructured data is 9 out of the 10 times the issue. Luckily, also for small firms, there are now beneficial ways to making use of their Big-data.

 

Some practical recommendations

1. First, entrepreneurs and managers of small firms must understand that Big-data is not only something for big firms; dealing with online, offline, and external data sources should be an integrative part of a firm’s strategy.
2. Second, it is important that firms continuously gather and enrich their information. Enriching data means more knowledge about the (potential) customers, which gives firms the possibility to better personalize its offerings.
3. Third, to cope with all the information, a third-party Big-data processing tool should be adopted. A very powerful Big-data tool that helps to store, process, analyse, and visualize information is ‘ClearStory Data’. It combines all types of business’s internal (online and offline) information with publicly available information (through data mining) and creates easy to understand and customizable dashboards to help make better decisions; whether this is on a strategic, tactical or operational level.

 

In conclusion, information management in small businesses should not be considered rocket science, right?. The key learning here is that every business has, and can make sense of, internal and external data; it is just a matter of acknowledging the potential and finding the right Big-data tools and solutions.

 

Sources:

Gordon, K. (2013). What is Big Data?. Itnow, 55(3), 12-13.