Machine learning risks are real. Do you know what they are? 

Machine Learning Risks

Machine Learning RisksMachine Learning Risks are real and can be very dangerous if not managed / mitigated.

Everyone wants to ‘do’ machine learning and lots of people are talking about it, blogging about it and selling services and products to help with it. I get it…machine learning can bring a lot of value to an organization – but only if that organization knows the associated risks.

Deloitte splits machine learning risks into 3 main categories: Data, Design & Output. This isn’t a bad categorization scheme, but I like to add an additional bucket in order to make a more nuanced argument machine learning risks.

My list of ‘big’ machine learning risks fall into these four categories:

  1. Bias – Bias can be introduced in many ways and can cause models to be wildly inaccurate.
  2. Data – Not having enough data and/or having bad data can bring enormous risk to any modeling process, but really comes into play with machine learning.
  3. Lack of Model Variability (aka over-optimization) – You’ve built a model. It works great.  You are a genius…or are you?
  4. Output interpretation – Just like any other type of modeling exercise, how you use and interpret the model can be a huge risk.

In the remainder of this article, I spend a little bit of time talking about each of these categories of machine learning risks.

Machine Learning Risks


One of the things that naive people argue as a benefit for machine learning is that it will be an unbiased decision maker / helper / facilitator.  This can’t be further from the truth.

Machine learning models are built by people. People have biases whether they realize it or not. Bias exists and will be built into a model. Just realize that bias is there and try to manage the process to minimize that bias.

Cathy O’Neill argues this very well in her book Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Now, I’m not a huge fan of the book (the book is a bit too politically bent and there are too many uses of the words ‘fair’ and ‘unfair’….who’s to judge what is fair?) but there are some very good arguments about bias that are worth the time to read.

In addition to the bias that might be introduced by people, data can be biased as well. Bias that’s introduced via data is more dangerous because its much harder to ‘see’ but it is easier to manage.

For example, assume you are building a model to understand and manage mortgage delinquencies. You grab some credit scoring data and build a model that predicts that people with good credit scores and a long history of mortgage payments are less likely to default.  Makes sense, right?  But…what if a portion of those people with good credit scores had mortgages that were supported in some form by tax breaks or other benefits and those benefits expire tomorrow.  What happens to your model if those tax breaks go away?  I’d put money on the fact that your model isn’t going to be able to predict the increase in numbers of people defaulting that are probably going to happen.

Data bias is dangerous and needs to be carefully managed. You need domain experts and good data management processes (which we’ll talk about shortly) to overcome bias in your machine learning processes.

From the mortgage example above, you can (hopefully) imagine how big of a risk bias can be for machine learning.  Managing bias is a very large aspect to managing machine learning risks.


The second risk area to consider for machine learning is the data used to build the original models as well as the data used once the model is in production. I talked a bit about data bias above but there are plenty of other issues that can be introduced via data. With data, you can have many different risks including:

  • Data Quality (e.g., Bad data)– do you know where your data has been, who has touched it and what the ‘pedigree’ of your data is? If you don’t, you might not have the data that you think you do.
  • Not enough data – you can build a great model on a small amount of data but that model isn’t going to be a very good model long-term due unless all your future data looks exactly like the small amount of data you used to build it. When building models (whether they are machine learning models or ‘standard’ models), you want as much data as you can get.
  • Homogeneous data – similar to the ‘not enough data’ above risk above, this risk comes from a lack of data – but not necessarily the lack of the amount of data but the lack of variability of the data.   For example, if you want to forecast home prices in a city, you probably want to get as many different data sets as you can find to build these models.  Don’t use just one data-set from the local tax office….who knows how accurate that data is. Find a couple of different data sets with many different types of demo-graphical data points and then spend time doing some feature engineering to find the best model inputs for accurate outputs.
  • Fake Data – this really belongs in the ‘bad data’ risk, but I wanted to highlight it separately because it can be (and has been) a very large issue.  For example, assume you are trying to forecast revenue and growth numbers for a large multi-national organization who has offices in North America, South America and Asia. You’ve pulled together a great deal of data including economic forecasts and built what looks to be a great model.  Your organization begins planning their future business based on the outcome of this model and use the model to help make decisions going forward.  How sure are you that the economic data is real?
  • Data Compliance issues – You have some data…but can you (or should you) use it?  Simple question but one that vexes many data scientists – and one that doesn’t have an easy answer.

Lack of Model Variability (aka over-optimization)

You spend weeks building a model. You train it and train it and train it. You optimize it and get an outstanding measure for accuracy. You’re going to be famous.  Then…the real data starts hitting the model.  Your accuracy goes into the toilet.  Your model is worthless.

What happened? You over-optimized.  I see this all the time in the financial markets when people try to build a strategy to invest in the stock market. They build a model strategy and then tweak inputs and variables until they get some outrageous accuracy numbers that would make them millionaires in a few months.  But that rarely (never?) happens.

What happens is this – an investing strategy (e.g., model) is built using a particular set of data. The inputs are tweaked to give the absolute best output without regards to variability of data (e.g., new data is never introduced). When the investing strategy is then applied to new, real world data, it doesn’t perform anywhere near as well as it did on the old tested data.  The dreams of being a millionaire quickly fade as the investor watches their investing account value dwindle.

In the world of investing, this over-optimization can be managed with various performance measures and using a method called walk-forward optimization to try to get as much data in as many different timeframes as possible into the model.

Similar approaches should be taken in other model building exercises.  Don’t over-optimize. Make sure the data you are feeding your machine learning models are varied across both data types, timeframes, demo-graphical data-sets and as many other forms of variability that you can find.

Some folks might call ‘lack of model variability’ by another name — Generalization Error. Regardless of what you call this risk…its a risk that exists and should be carefully managed throughout your machine learning modeling processes.

Output Interpretation

You spend a lot of time making sure you have good data, the right data and the as much data as you can. You do everything right and build a really good machine learning model and process.   Then, your boss takes a look at it and interprets the results in a way that is so far from accurate that it makes your head spin.

This happens all the time. Model output is misinterpreted, used incorrectly and/or the assumptions that were used to build the machine learning model are ignored or misunderstood. A model provides estimates and guidance but its up to us to interpret the results and ensure the models are used appropriately.

Here’s an example that I ran across recently. This is a silly one and might be hard to believe – but its a good example to use. An organization had one of their data scientists build a machine learning model to help with sales forecasting. The model was built on the assumption that all data would be rolled up to quarterly data for modeling and reporting purposes. While i’m not a fan of up-sampling data from high to low granularity, but it made sense for this particular modeling exercise.

This particular model was built on quarterly data with a fairly good mean error rate and good variance measures. Looking at all the statistics, it was a good model. The output of the model was provided to the VP of Sales who immediately got angry.  He called up the manager of the data scientist and read her the riot act. He told her the reports were off by a factor of anywhere from 5 to 10 times what it should be. He was furious and shot off an email to the data team, the sales team and the leadership team decrying the ‘fancy’ forecasting techniques declaring that it was forecasting 10x growth of the next year and “had to be wrong!”

Turns out he had missed that the output was showing quarterly sales revenue instead of weekly revenue like he was used to seeing.

Again – this is a simplistic example but hopefully it makes sense that you need to understand how a model was built, what assumptions were made and what the output is telling you before you start your interpretation of the output.

One more thing about output interpretation…a good data scientist is going to be just as good at presenting outputs and reporting on findings as they are at building the models.  Data scientists need to be just as good at communicating as they are at data manipulation and model building.

Finishing things up…

This has been a long one…thanks for reading to here. Hopefully its been informative. Before we finish up completely, you might be asking something along the lines of  ‘what other machine learning risks exists?’

If you asked 100 data scientists and you’ll probably get as many different answers of what the ‘big’ risks are – but I’d bet that if you sit down and categorize them all, the majority of them would fall into these four categories. There may be some outliers (and I’d love to add those outliers to my list if you have some to share).

What can you do as a CxO looking at machine learning / deep learning / AI to help mitigate these machine learning risks?  Like my friend Gene De Libero says: ‘Test, learn, repeat (bruises from bumping into furniture in the dark are OK).”

Go slow and go small. Learn about your data and your businesses capabilities when it comes to data and data science. I know everyone ‘needs’ to be doing machine learning / AI but you really don’t need to throw caution to the wind. Take your time to understand the risks inherent in the process and find ways to mitigate the machine learning risks and challenges.

I can help mitigate those risks. Feel free to contact me to see how I might be able to help manage machine learning risks within your project / organization.

Beware the Models

Beware the Models

Beware the Models“But….all of our models have accuracies above 90%…our system should be working perfectly!”

Those were the words spoken by the CEO of a mid-sized manufacturing company. These comments were made during a conversation about their various forecasting models and the poor performance of those models.

This CEO had spent about a million dollars over the last few years with a consulting company who had been tasked with creating new methods and models for forecasting sales and manufacturing. Over the previous decade, the company had done very well for themselves using a very manual and instinct-driven process to forecast sales and the manufacture processes needed to ensure sales targets were met.

About three years ago, the CEO decided they needed to take advantage of the large amount of data available within the organization to help manage the organization’s various departments and businesses.

As part of this initiative, a consultant from a well known consulting organization was brought in to help build new forecasting models. These models were developed with many different data sets from across the organization and – on paper – they look really good. The presentation of these models include the ‘right’ statistical measures to show that they provide anywhere from 90% to 95% accuracies.

The models, their descriptions and the nearly 300 pages of documentation about how these new models will help the company make many millions of dollars over the coming years weren’t doing that they were designed to do. The results of the models were so far from the reality of what was happening with this organization’s real-world sales and manufacturing processes.

Due to the large divergence between model and reality, the CEO wanted an independent review of the models to determine what wasn’t working and why.  He reached out to me and asked for my help.

You may be hoping that I’m about to tell you what a terrible job the large, well known consultants did.  We all like to see the big, expensive, successful consulting companies thrown under the bus, right?

But…that’s not what this story is about.

The moral of this story? Just because you build a model with better than average accuracy (or even one with great accuracy), there’s no telling what that model will do once it meets the real world. Sometimes, models just don’t work. Or…they stop working. Even worse, sometimes they work wonderfully for a little while only to fail miserably some time in the near future.

Why is this?

There could be a variety of reasons. Here’s a few that I see often:

  • It could be from data mining and building a model based on a biased view of the data.
  • It could be poor data management that allows poor quality data into the modeling process. Building models with poor quality data creates poor quality models with good accuracy (based on poor input data).
  • It could be a poor understanding of the modeling process. There a lot of ‘data scientists’ out there today that have very little understanding of what the data analysis and modeling process should look like.
  • It could be – and this is worth repeating – sometimes models just don’t work. You can do everything right and the model just can’t perform in the real world.

Beware the models. Just because they look good on paper doesn’t mean they will be perfect (or even average) in the real world.  Remember to ask yourself (and your data / modeling teams) – are your models good enough?

Modeling is both an art and a science. You can do everything right and still get models that will make you say ‘meh’ (or even !&[email protected]^@$). That said, as long as the modeling process is approached correctly and the ‘science’ in data science isn’t forgotten, the outcome of analysis / modeling initiatives should at least provide some insight into the processes, systems and data management capabilities within an organization.


Are your machine learning models good enough?

Are your machine learning models good enough?

Are your machine learning models good enough?Imagine you’re the CEO of XYZ Widget company.  Your Chief Marketing Officer (CMO),  Chief Data Officer (CDO) and Chief Operations Officer (COO) just finished their quarterly presentations and were highlighting the success from the various machine learning projects that have been in the works. After the presentations were complete, you begin to wonder – ‘are these machine learning models good enough?’

You’ve invested a significant portion of your annual budget on big data and machine learning projects and based on what your CMO and CDO tell you, things are looking really good. For example, your production and revenue forecasting projects are both delivering some very promising results with recent forecasts being within 2% of actual numbers.

You don’t really understand any of the machine learning stuff though. It seems like magic to you but you trust that the people doing the work understand it and are doing things ‘right’. That said, you have a feeling deep down that something isn’t quite right.  Sure, things look good but just like magic – the output of these machine learning initiatives could just be an illusion.

Are these machine learning models good enough? — Getting past the illusion

While machine learning, deep learning and big data can provide an enormous amount of value to an organization, there is ample opportunity to mess things up dramatically. There are plenty of times where small errors (and even massive errors) can be introduced into the process. For example, during the data munging / exploration phase, a simple error can introduce changes in the data, which could cause massive changes in the results of any modeling.

Additionally, bias can easily be introduced to the process (either on purpose or by accident). This bias can push the results to tell a story that people want the data / models to tell.  It is very easy to fall into the “let’s use statistics to support our view” trap that many fall into.  Rather than look for data and/or  outputs to support your view (and hence build an illusion), your machine learning initiatives (and any other data projects) should be as bias free as possible.

When done right, there’s very little ‘illusion’ in machine learning. The results are the results just like the data is the data.   You either find answers to your questions (and hopefully find more questions) or you don’t.   The results may not be what you wanted to see, but they are what they are…and this is the exact reason you need to be able to trust the process that was used to find those results. You need to understand if (and where) bias was introduced. You need to understand the process in general.

Can your team describe how was the data gathered and cleaned? Where the models used in the process optimized and/or overfit. Can your team explain their rationale for doing what they did?   Your forecasting models are within 2% of actual numbers in recent months, but that doesn’t mean your models are well built and will hold up over time…it could just mean they are overfit and are doing well with very similar numbers to what you’ve given your machine learning algorithm. What do your models really show for things like R-Squared and Mean Absolute Error (MAE)?  Do you understand why R-Squared and MAE are important?  If not, your teams need to make sure they are explained in general terms and describe why those things are important.’s a few links for you to learn more about R-Squared and MAE.

You don’t have to become an expert

It takes time and a willingness to ‘get your hands dirty’ to get anywhere close to being an expert in machine learning. Most business leaders don’t need to become an expert but you if you spend a little time understanding the basics and the process that your team follows, it might help remove the ‘magic’ aspect associated with machine learning

My suggestion is to spend some time talking to your team(s) about the following topics to get a basic understanding of the three main steps / processes in machine learning.  Below, I’ve outlined the three main areas and included some questions for you to consider.  Note: These aren’t a definitive list of questions / areas but they’ll get you started.

Data Gathering / Preparation / Cleaning

  • How was the data gathered?
  • What data quality measures / methods were undertaken to ensure the data’s accuracy and provenance?
  • What steps were taken to clean / prepare the data?
  • How is new data being gathered / cleaned / prepared for inclusion in existing / new models?
  • Who has access to the data?


  • Why was the model (or models) chosen?
  • Were other models considered? If so, why weren’t they used?
  • Did you ‘build your own’ or use existing libraries to build the model?
  • Where the proper data preparation steps taken for the model(s) selected?

Evaluation &Interpretation of Results

  • How do you know the model is ‘good enough’?
  • When and why did you stop iterating on the model / data?
  • What accuracy measures are you using for the model(s)?
  • Are we sure the data isn’t being overfitted? How?
  • Why are the visualizations that are presented used? (Note: the use or non-use of certain visualizations can be a tip-off that something isn’t right about the data / model).

Again – these aren’t meant to be a definitive list of questions / topical areas for you to consider but they should get you started asking good questions of your team.   I particularly love to ask the How do you know the model is good enough question because it sheds a lot of light on the entire process and the mental approach to the problem.

Are these machine learning models good enough?

The answers to the above questions should help you get a better feel for how your team(s) approached the issue at hand and help you (and the rest of your leadership team) understand the approach to data preparation, modeling and evaluation in your machine learning initiatives.

The above questions and answers might not specifically answer the ‘are your machine learning models good enough’ question, but they will get you and your team(s) to a point where they are constantly thinking about whether ‘good enough’ is enough. Sometimes it is…others it isn’t. That’s why you need to understand a bit more about the process to understand whether good enough is good enough.

Of course, if you need help trying to understand all this stuff…you can always hire me to help. Give me a call or drop me an email and let’s discuss your needs.