Have you ever filled out a job application which asks the question, “have you ever been convicted of a crime?” with a little checkbox next to it? That’s “the box” that everyone’s talking about.
For many years, HR professionals across the nation were relying on this box to pre-screen applicants without ever conducting a compliant background check.
In our previous article we talked about predictive AI and how it can be used in the data industry to model human behavior, identify fraud, and predict future events. We also talked about the difficulty with creating predictive models with a lack of ground-truth data.
In this article, we’ll talk about deep learning, image classification, and facial recognition. These are all pretty spicy topics in a very complex industry, so let’s get started.
There has been a lot of talk about AI over the last few months – mostly because of generative AI platforms like ChatGPT, Github Copilot, Stable Diffusion, and DALL-E – just to name a few. But how exactly are these technologies being used in the data industry?
We’ll answer all of that and more in this multi-part series entitled, “How is AI being used in the data industry?”
There are several “flavors” of bankruptcy, commonly known by their chapter number in federal code. A brief overview of the more common ones is as follows:
So why does all of this matter?
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Bankruptcies data, unlike Liens and Judgements, are collected and maintained on a federal level by the United States Bankruptcy Court and is made available through several pubic sources including PACER (https://pacer.uscourts.gov/)
Liens and judgments are legal claims made against an individual or business for failure to pay debts owed to creditors. Private companies often collect this information through various methods such as searching public records, conducting interviews, or purchasing data from other sources. The information is then compiled into databases that can be accessed by potential creditors or other interested parties.
On February 27th 2023, The Texas Tribune reported that the Texas DPS (Department of Public Safety) accidentally sent more than 3,000 drivers licenses to an organized crime group which was targeting Asian Texas. Read the full article here: https://www.texastribune.org/2023/02/27/texas-drivers-license-theft-dps/
That’s a lot to unpack! How did this happen, how did they discover it, and what could have been done to prevent it?
So far in this series of Data Classification articles, we’ve talked about each type of data at a glance and have gone in depth on Consumer Marketing Data. This time we’re going to take a deep dive into Public Data.
Though the name implies that the data is publicly available (and therefore unrestricted), when we say, “public data” we’re talking about data that belongs to the public because it’s paid for by taxes. It’s produced by government entities or government agencies. It can still contain PII (names, DOBs, addresses, phone numbers, etc.) and it can still have legal restrictions in how it can be used.
As we discussed previously, there are many classifications of data that are commonly used in this industry. We previously gave a broad overview of all the major types: Public, Consumer, and Regulated. In this article, we’ll be exploring the world of Consumer Data in greater depth.
In our line of work, there are many classifications of data available for consumption. In this article we’ll outline each of the major types at a high level. In the subsequent articles, we’ll dive deep into each of them to talk about the nuances involved with each. We should also make it clear that there are plenty of other classifications of data and many of these will overlap – nothing is black and white in this industry. So let’s get started!
Earlier this year, the US Supreme Court made an unexpected ruling in favor of the State of Oklahoma which sets a brand new precedent. Previously, tribal lands and Indian reservations were subject to only their own legal oversight except for in cases of certain violent crimes and sexual offenses. In the 5-4 decision, the US Supreme Court vastly expanded the power that states have over Native American lands.
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