Soul in the Machine – Top AI/ML Orgs and Unconscious Bias

This is Part 3 in the Soul in the Machine series.

In the Soul in the Machine series, I will be delving into our collective responsibility to ensure that computing systems are treating users fairly and responsibly. Specifically, I will be raising the ethical questions around current trends such as data privacy regulations and machine learning capabilities.

Right now, in Part 3, we are going to quickly look at the top organizations that are investing in the development of Artificial Intelligence, Machine Learning, and Deep Learning.

The rankings (average ranking value)

  1. Amazon (1.8)
  2. Google (1.8)
  3. Nvidia (2.6)
  4. Microsoft (4.4)
  5. Apple (4.75)
  6. Intel (5)
  7. IBM (5.5)
  8. Facebook (5.8)

Leading “around the world”

During my research, I wanted to know what the top companies were that were investing in AI and ML, but depending on who you asked, you get a different answer. For this reason, I decided to collect the results from various rankings and then identify the locations for the top companies that are investing in major research “around the world”. The big players, not unexpectedly, were present on most lists as being the leaders in artificial intelligence and machine learning, but their geographic locations showed they all have an unfortunate diversity problem.

Map - Leading AI Research

Primarily they are based out of North America, specifically the west coast. These groups can be doing their best but their lack of team diversity will lead to unconscious bias in their solutions.

These groups have been growing outwards in recent years, as you can see in the map,  to try to add more diversity and more reach into other cultures. They have recognized that they do not have a global view of the problem and it is impacting their ability to deliver solid solutions.

However, the growth in diversity is limited and slow. And you’ll notice a lot of grey areas on the map. Not to mention an entire half of the world not even showing up! We are a long way off from a balanced workforce in leading artificial intelligence research. And this means we are a long way off from removing unconscious bias.

Determining the leaders

I’m a big fan of “showing your work”. As such, I am providing you with all the data I used. Maybe you can find another use for it?

The approach:

  1. Identify 10 sources with AI/ML rankings that valued different things (revenue, innovation, services, etc.)
  2. Collect all companies listed across all sources and gather identified ranking from that source.
  3. Select the subset of companies (8) which appear in at least 4 different listings.
  4. Calculate the average ranking for each company based on an even weighting across all sources.

It should be noted that Datamation did not rank the organizations and only listed a group of top companies, therefore they were all given ‘top rank’ in the data since this would evenly skew them across the set.

Location data is also incomplete for many organizations as I only focused on identifying locations for leading firms.

What’s next?

It will be interesting to see how this continues to unfold in future years as we see more acquisitions from the bigger players but also newcomers on the scene grow in different areas of the world. Is Africa or Southern Asia the next major growth area?

References

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