Data Privacy Series

When’s the last time you read the terms and conditions before clicking “accept” as you downloaded the hottest new app to your smartphone? Do you really know what companies are doing with your data from your search history, the pictures on your phone, or even your personal health records?

How our data, personal details and digital patterns, is being used by the scores of apps, programs, and devices we interact with on a daily basis remains a mystery. When we click “Accept” on the terms and conditions page, usually in a hurry, we are blindly  “choosing” to opt in to whatever data collection and privacy infringements the developer has built into the technology. What’s more, most companies use vague statements on their websites regarding what they are doing with your data and even threaten to impact your service if you decide not to share your data.

For example, this snippet comes directly from Nest’s FAQ’s:

Nest FAQs

How important is that app or device?  Is it worth signing over your digital rights?


Join the SolvetheUnsolvable team this month to explore how private your data really is…

Facial Recognition, friend or foe? Family Heritage Mapping, a key to the past or losing control of your future?… Who else is checking in on grandma? The hidden dangers in Smart Home technology. Are you using your cell phone, or is it using you?

Check back in on Wednesdays this October to learn more about data privacy…

Up next: DNA Testing: Is knowing your heritage worth risking your privacy?


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Solved: Dismantling the Silo

We are living in a world that is obsessed with connectivity yet so many large corporations are still working in silos. How can a corporation become connected and move towards Industry 4.0 if their data and systems are trapped in silos? This is a problem many corporations, big and small, face today. In order to understand and address the problem, first we must understand the silo. There is a solution, there is a path forward and Artificial Intelligence can lead the way.

Silos are created when information, goals, tools, priorities and processes are not shared with other departments and this pitfall becomes enforced by corporate culture. An effort to achieve the lowest overall cost and best functionality for different departments, or in the case of some manufacturers the same department in different plants, has created disparate data. Many systems are programmed to not function well together or only function in a stack but there is technology out there dedicated to dismantling the silo.

Executives often get into the trap of thinking the only way to advance into Industry 4.0 is to update disparate systems and increase capital expenditures. What many execs and people in all corporate functions do not yet understand is that Artificial Intelligence (AI) can be the systems connector. The entire premise of AI is built on the notion of interconnected information that may have previously been thought to be unconnected entirely. It is entirely unnecessary for a corporation to increase CAPEX when working with the right AI provider.

In order to get the most accurate picture of underlying issues within the corporation, AI must be able to connect to a vast amount of data from many different silos. This doesn’t mean that you have to dismantle the silos, you just need the right AI connector…. Let Artificial Intelligence be your Silo Dismantling Agent.

Data Bias on the Daily: What’s in your Amazon Cart?

With multiple billions of packages shipped per year, and even
more billions of items purchased, it’s no
wonder that Amazon is a household name. In the time it takes you to read this
article, an estimated 100,000 items will be purchased on Amazon. But how many
of those purchases will be impacted by data bias? It’s likely every single one.

Have you ever
stopped to think about the algorithms behind those convenient home page
suggestions, people also purchased, and related to items you viewed? Maybe in
passing or idly but likely not in detail. In this post we will be exploring
just how much one quick search on Amazon can be littered with bias.

Bias can end up in
your shopping cart in many different stages along the way. It’s safe to assume
that Amazon wants to maximize it’s bottom line, even though they claim they
want to provide the consumer with a great deal. When the engineers set to
writing shopping algorithms at Amazon corporate, they have to add parameters
that are computable and achieve a certain goal. With this in mind, they are
likely adding the bias that they would like to increase Amazon’s bottom line
(aka, profitability of your purchase). Let’s apply this to an example search.

Perhaps you’ve been
searching for storage solutions for your messy closet. The moment your results
appear for “closet storage” you are encountering data bias. Exhibit
A, the screenshot below:

You’re looking at the first instance of bias because a majority of the page is showing you “sponsored” results. “Funding Bias” is written into many algorithms all over the internet and is the reason why sponsored items always show up first. These brands: JYYG, TYPE A, OUMYJIA and JEROAL are paying to bias your search. This is one of, if not the most common search bias encountered but we see so much “sponsored” content that, at this point, we may not even think of the bias’s effects.

Regulations have
been placed on funding bias, note the “Sponsored” tag and other
evidence for example on social media platforms when influencers post with
“#ad.” These indicators are set up to clue in the consumer that their
purchasing decisions are being biased by funded posts, something relatively new
the last several years. Corporations spent years biasing search history towards
paid posts without having to let the consumer know, this is a huge stride
against hidden data bias. There’s nothing wrong with “pay to play!”
As a consumer, this can be a helpful way to find one of your new favorite
brands, products or technology solutions but people now believe it’s important
for the consumer to know when funding is biasing their purchase.

Another example of
bias in this search are the items marked with the “best seller” tag.
Under “Help & Customer Service” Amazon.com says: “The Amazon
Best Sellers calculation is based on Amazon.com sales and is updated hourly to
reflect recent and historical sales of every item sold on Amazon.com.”
There is a litany of data bias that could be going on here. Was the
“Simple Houseware” organizer at one point a “sponsored”
post which led to it becoming a “best seller?” A quick google search
shows that Simple Houseware only sells on Amazon, this could be another
contributing factor to the status as a “best seller.” We simply do
not know the parameters the calculation is setting for “sales” as
that is quite a broad term.

To play devil’s
advocate, the “sponsored” 
results could be amazing products that you will purchase, love and even
re-order in the future. The data bias written into the “Best Sellers”
calculation could absolutely be favoring great products you will know and love.
However, it’s critical that we don’t turn a blind eye to the bias and we
continue to scroll past the “sponsored” and “best sellers”
to get a full picture for purchasing.

The objective of
this article isn’t to get you to stop buying from Amazon but rather to consider
the data bias right in front of you. If there is this much data bias in one
simple Amazon search, how many more things in your life are impacted by data
bias? How much is your business and its bottom line impacted by data bias?
Algorithms, AI and their inherent bias are a part of daily life. How will you
use them to create change for good?


Up next: DNA Testing: Is knowing your heritage worth risking your privacy?


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Data Bias on the Daily

Our upcoming September Blog series is Data Bias on the Daily: How Data Bias in Artificial Intelligence is Impacting You. This series will focus on data bias in various forms encountered daily and the goal is to educate consumers on how bias can enter algorithms, knowingly and unknowingly. It is important to define bias:

Bias: The systematic favoritism that is present in the data collection process, resulting in lopsided, misleading results.

How to Identify Statistical Bias

Bias in Artificial Intelligence and algorithms is sometimes intentional, can be caused by a number of things including, but not limited to, sample selection or data collection, and can be avoidable, if that is the desired outcome. (Many corporations want to write their algorithms with bias, in order to increase their bottom line.)

Maybe you’re a woman searching for a STEM job, but the data is biased against your application? Perhaps, your Amazon search is biased towards products and brands that will only increase Amazon’s bottom line? Data bias is even entering our judicial system, is it possible that algorithmic “advancement” is simply confirming long standing racial bias?


Stay tuned to learn more about how Data Bias impacts our daily lives by checking in with us on Mondays in September (9/16, 9/23 and 9/30).



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Artificial Intelligence… A Buzz Word?

It’s a super-computer. It’s
technology.  It’s a Computer Brain.  It’s a….
Buzz Word?


Artificial Intelligence or “AI” is all the rage in consumer goods and services, and analysts say it will change the face of business forever, but what exactly is “Artificial Intelligence”. The market has failed to define what this ambiguous buzz word means, but yet investors are readily throwing billions of dollars at companies that claim to be “AI- Focused”.  According to the Financial Times,

“Companies branded as AI businesses have historically raised larger funding rounds and secured higher valuations than other software businesses. The median funding round for an AI start-up last year was about 15 per cent higher than for a software start-up.”

Financial Times

And yet investors struggle to understand what Artificial Intelligence means.  Is it the technology?  Is it the application?  Is it the result?

Artificial Intelligence has become a catch-all phrase for various
types of computer and data science technologies and applications aimed at
automating work and aiding in decision making.  
Articles on the proliferation of AI in the enterprise market speak to
the vast potential use cases, the efficiencies, the streamlined customer
experience, but fail to define the technology. Instead, authors caveat their
prophecies with statements which undermine the entire industry, “Granted, not
all AI systems are alike. Some of them are relatively ‘dumb,’ because they use
pre-determined inputs and outputs”. 
Enterprise organizations have been leveraging “If/Then” logic
programming for decades to optimize their operations, from PLCs on the
manufacturing floor, to excel sheets in the board room or sales office.  Is a simple system that has pre-determined
inputs and outputs anything more than a logic sequence? 

In truth, Artificial Intelligence is a combination of complex technologies which together have the power to change the way we do business, but how many companies have successfully developed the advanced integrated technology to deliver this seismic shift in enterprise value?  True Artificial Intelligence requires much more than a logic sequence – from system agnostic data connectivity, neural networking, autonomous data-cleansing, to machine learning, automated root cause analysis and continuously learning autonomous decision making and self-scripting technologies – the technology that will transform enterprise operations and ultimately, organizational value, has arrived, but true Artificial Intelligence is far less prevalent than a market summary would lead you to believe…