Artificial intelligence is no good, software is not inclusive, IT leaders are irresponsible. Everyone in IT can encounter such statements on regular bases, sometimes in the media, other times at a café, it does not really matter where, when you hear a lie often enough it becomes the truth. But is that the truth though?
There are two main believer camps when it comes to opining on the impact of IT on society. People in the first camp believe in apocalyptic future if technology starts stagnating whereas people in the second camp believe in the end of the world if technology advances.
Who is right and which camp’s values should you adhere to when deciding what to do with your data center, software design and user adoption? The answer lies in understanding what influences such decisions in the first place and how to satisfy the needs of the target user group while focusing on the bigger picture.
In the book Industries of the future, Alec Ross observes that while westerners see AI as a Hollywood kind of thread, Japanese have a more philosophical view on the topic and see it as an opportunity. Several observers of Japanese society say that the country’s indigenous religion, Shinto might explain its fondness for robots. Shinto is a type of animism that attributes spirits, or kami, not only to humans but to animals, natural features like mountains, and even everyday objects like pencils, in essence all things have a bit of soul. Therefore, a product manager in Tokyo will base decisions very differently to her counterpart in Munich.
Biases that influence the two are the same but opinions that get generated are due to varying previous experience very different. To put it in data science terminology, training each of the two neural networks with two distinct data sets will over time produce two very differently trained neural networks.
To fully grasp the concept, put yourself in the shoes of each of the product managers and think about how below outlined cognitive biases and product managers’ respective cultures influence their decisions. Take AI infused App that helps the elderly invest their consciously saved money as an example.
- Confirmation bias involves favoring information that confirms your previously existing beliefs or biases. For example, One’s grandparents love their smartphone, hence this person believes that smartphone is great for the elderly.
- Halo effect is the tendency for positive impressions of a person, company, brand or product in one area to positively influence one’s opinion or feelings in other areas. For example, based on the sporty looks of a car one perceives it as safe, powerful and comfortable as well.
- Normality bias is a tendency for people to believe that things will always function the way they have normally functioned and therefore underestimate both the likelihood of a disaster and its possible effects. For example, Computer has never been backed up and nothing ever happened so backup is not needed.
Yes, their decisions are likely to be different. This is who they are and their beliefs might resonate with camp number one , number two or neither one of them.
The Big Picture
In reality, if you are in the IT industry you don’t have much choice when it comes to Camp 1 or Camp 2 question. You have to believe in advancement of technology and the positive impact it can have. The question is how to ensure that the impact is indeed positive.
At the bottom of business KPIs, long after profitability and revenue growth, one can usually find Corporate Social Responsibility (hereafter: CSR).
CSR BUILDING BLOCKS
Representation of practical implementation of CSR at an international organization Mazars
The good thing about CSR metrics is the ease of defining them in historically slower moving industries where processes and products are well understood. A good example is the automotive industry where waste stemming from the manufacturing process and emissions incurring while driving are well regulated. If the regulation is not followed as in the VW case, authorities are quick to act.
The bad thing about CSR is defining policies in fast paced industries such as IT where metrics are much more difficult to establish. The main reason are countless ways of technology application, lack of understanding and light-speed innovation. For instance, a new dating platform gets released and tested on thousands of users in alpha and beta version before it is even fully functional.
To ensure positive impact of technology, one has to face one own’s biases and mindfully decide which CSR principles and metrics to implement. Two simple yet universally applicable IT CSR metrics are:
- Hardware utilization – it has to exceed a predetermined threshold According to research, servers in company-owned data
canterson an annual level run on only five to fifteen per cent of their maximum output. This has several implications:
- A company is wasting unnecessary physical space, often time located in metropolitan areas with an insufficient number of flats and houses. A company is wasting unnecessary physical space, often time located in metropolitan areas with insufficient number of flats and houses.
- servers on a global level are throughout their lifetime (typically 5-7 years) not utilized as much as they should have been, getting replaced and disposed without being maximally leveraged.
- Power Usage Effectiveness – it has to be lower than the predetermined threshold Techopedia defines Power Usage Effectiveness (hereafter: PUE) as “a metric used to determine energy efficiency measurements. PUE is calculated by comparing the total power used by a data center to the actual power delivered to a computing device.”
“Ideally, the value of PUE should be 1. For example, if a facility has a PUE of 2, it indicates that the amount of power used to run the IT equipment is half of the power consumed by the entire data center facility.”
To put it in context, one study analysed 500 data centers and concluded that the average PUE amounts to 1.8. This number can with scale and optimization be brought down significantly, a perfect example of this motion are major cloud providers such as Microsoft. Through innovation such as placing the data centers below the sea surface and at strategic geo-locations with favorable weather conditions they brought their PUE down to approximately 1.1.
Prioritizing CSR is not easy, it might result in sacrificing growth, profit and personal beliefs, but it has to be done. Even though there are countless reasons not to prioritize CSR, there is one reason that outweighs them all- ensuring positive technological impact.