Comprehensive Software Reviews to make better IT decisions
How to Remove Unfair Bias From Your AI – DataRobot Demo
So, you know about AI biases but want to see a demonstration of what’s involved in identifying and removing them from a machine learning (ML)/AI application? A recent webinar by DataRobot does just that: it walks you through a small ML project and explains step by step what to do and how. (The demo starts about 25 minutes into the recording.)
The project’s goal is to predict which job applicants should be hired by a fictitious high-tech firm, looking at their historical hiring data. Among DataRobot’s capabilities is a set of features that help you visualize and inspect various dimensions and components of the data and the predictive model built from that data.
For example, the graph below ranks the features used – internships, educational level, extra-curricular activities, gender (at the bottom of the screenshot), etc. – based on how much they weigh in predicting whom we should hire. (Note: Gender should not be used in ML as it is a sensitive attribute. It is used in the demo for illustration purposes.)
All images: How to remove unfair bias from your AI, webinar, DataRobot.
You can also zoom in to any of the features and see what is going on there. For example, a person is more likely to get hired if they had at least one internship:
You can further analyze which features predict highest at the individual level (the rows below the graph are individual records/job applicants):
When dealing with textual data – e.g. extra-curricular activities – you can see by way of a word cloud which words and phrases are proxies for gender. (Here, red indicates male and blue indicates female.)
Clearly, some sports names are highly correlated with gender, and so we can either remove this feature from the model completely or use it in a modified form: activity counts instead of names, how many different activities a “desired” employee has on average listed on their resume, and so on.
While nothing in the above demonstration is earth shattering, we appreciate the clear, easy-to-use interface and the fact that these features are part of the DataRobot ML/AI platform. And we appreciate that they have been explained through the example, which is easy to understand and relate to, without much technical jargon.
This demo also sheds some light on the tasks and activities involved in machine learning and its iterative nature. (ML and data science are many times more art than science.)
All this means that you now have a better understanding of the approach and the mechanics of removing hidden biases and that you can start addressing them before they harm your customers or your organization.
Want to Know More?
Not all AL biases, however, come from data. To learn about other types of AI biases – those stemming from algorithms used, project design, team composition, etc. – and to learn about how identify and mitigate them, consult Info-Tech’s blueprint Mitigate Machine Bias.
Databricks, a data processing and analytics platform with a strong focus on AI and ML, has partnered with Immuta to deliver automated end-to-end data governance for AI, data science, and ML projects.
CognitiveScale has been named one of the 50 Smartest Companies of the Year 2019 by The Silicon Review. The recognition is for “transforming customer engagement and lifetime value with Artificial Intelligence.”
Facebook agreed to pay $550 million to settle a class action lawsuit with a group of users in Illinois over its use of facial recognition technology (FRT) to tag individuals in photographs, reports the BBC.
AI has been making headlines in healthcare for some time, and the current outbreak of the coronavirus in Wuhan, China, (with cases now in other parts of the world) – or, more specifically, the early warning of the outbreak – is another example.
Google founders Larry Page and Sergey Brin are stepping down as CEO and President of Alphabet, respectively. Google CEO Sundar Pichai will take over as Alphabet’s CEO. Both Page and Brin will remain actively involved as board members, shareholders, and cofounders.
I recently had an opportunity to speak with a KPMG partner in the Canadian risk consulting practice and with the head of data science for Canada about several things, including KPMG Ignite. This is what I learned.
SAS is creating a new agricultural technology business unit and has partnered with the North Carolina Plant Sciences Initiative to help next-generation farmers and agribusiness leaders harness artificial intelligence to transform agriculture and feed the world.
We recently covered Google’s lackadaisical approach to data privacy in the context of its partnership with Ascension, a US healthcare giant. Last month, Google was under fire again, along with Facebook, from Amnesty International.
Last week, Google’s CEO, Sundar Pichai, called for new AI regulations. The next day, IBM called for rules to eliminate AI biases that can discriminate against consumers, citizens, and employees based on their gender, age, and ethnicity.