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Jan. 6, 2021 - Epoch Times
50:31
Exclusive With Data Scientists: Public Data Shows 432,000 Trump Votes Removed in Pennsylvania | American Thought Leaders
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When we see over 400,000 errors in the state of Pennsylvania, when we see direct switches like we saw in Bibb County, Georgia, where there's over 12,000 votes that were swapped from Trump to Biden, these are people's votes.
This matters.
The Data Integrity Group, a group of data scientists, has been dissecting publicly available data on the presidential election in multiple states.
Most recently, in Pennsylvania, they found over 432,000 votes were removed from President Donald Trump in at least 15 counties.
Time series election data shows Trump's votes decreasing in various counties at many time points, instead of increasing.
In an election, as you count votes, you typically only see vote increments, not decrements, unless some error occurred that needs to be assessed.
So if you say, well, there was this human error here, that's fine, that's one.
I need to know all these other 37 votes.
Are they human error as well?
Are these machine error?
I don't know.
The group also testified before the Georgia Senate that more than 30,000 votes were removed from President Trump in Georgia.
The improbabilities of some of these things that we're bringing out are just way off the charts in terms of what you'd expect in a normal distribution or any type of expected behavior.
This is American Thought Leaders, and I'm Jan Jekielek.
Linda McLaughlin, such a pleasure to have you on American Thought Leaders.
Thank you so much for having me.
I can't thank you enough, Jan.
Well, and actually, it's not just Linda McLaughlin, it's actually the Data Integrity Group that the show is with today.
And you're the communications person for the Data Integrity Group.
We recently published your work on the Pennsylvania data.
All your work is done with publicly available data, which is a really interesting approach.
Tell me about what you're doing.
Absolutely.
So I think like a lot of people, you know, the night of the election, I was confused by what was happening.
I was seeing these strange vote totals.
The data wasn't performing the way that many of us saw it happening in different parts of the evening.
And we saw these strange drops and declines live on television.
And I think for a lot of people, you know, there's a simple explanation, which is that voting is supposed to be very straightforward.
You know, this is an additive process.
One candidate gets more votes than the other and we move on.
But that night we saw votes actually dropping live on television, you know, whole swings of votes, hundreds of thousands.
And I was sitting there with my family and friends saying, what is happening?
I don't understand.
And I'm not a data scientist.
I work in politics.
I work in media.
It's been my business for 15 years.
And so when I started seeing this happen, I thought, I've got to find this out.
So I did.
I started looking around for data scientists, finding people that were much smarter than myself to understand these numbers and make sense out of them.
And that's really how this came about.
Well, so why don't you tell me who are the core members here of the group and what do they contribute?
And then we'll get into a bit more about what you actually did.
Absolutely.
So we've got an amazing team of people that come from all walks of life.
They have very different political ideologies and philosophies on elections, on the constitution, on our republic.
But I think the one thing that we all agree on, and it's a very interesting point, is that data is language.
It's a language that very few of us speak to the depth and intricacy that these individuals speak.
And so what we have found is that they all came to this.
So John Basham is a meteorologist, but he has a background in data science because that's used in that profession.
And he's also a patriot to this country and served his country and has a background in some of those operations, understanding some of that intelligence.
Then we have Justin Neely, an NSA analyst, also a data scientist.
Who took a look at the numbers and how they were speaking to one another and where those strange corrections were happening.
Then we have Dave Laboo, an artificial intelligence expert, another data scientist.
So we have these people who use data in very, very different ways.
And then we all came together in this group to look at the numbers and say, This doesn't make sense.
It doesn't matter what role, what walk of life you come from, what political field, it doesn't make sense.
And it's not fair to the citizens of the country.
It is not fair to the voters who stayed up, waited in line for hours during a pandemic to get their vote counted and to keep our republic what it is meant to be, free and fair.
You've done a number of these explanatory videos that detail these irregularities that are found in the data in Pennsylvania and Georgia.
I think those are the two we'll cover a little more in depth today because they're the most recent and also obviously topical on the Georgia side.
In essence, you are looking at unexplainable errors in data.
Do I have this right?
Absolutely.
Basically, what we have seen is that when you start to clean what we call cleaning the data and looking at it—and I get in trouble all the time because it's data, not data, so you'll hear me flip-flop on that quite a bit— But basically what we've seen is these numbers just don't make sense.
There's a pattern that numbers follow based upon previous voting elections looking at demographics, looking at geographical location.
And suddenly these same votes that are happening in one location have these spikes, grand spikes, out of nowhere and then return to what would be considered normal or consistent with what we would expect from the numbers.
And then we were also seeing these influx of changes And every time a change would happen, whether the votes were removed, whether they were swapped, whether it was an even swap or just a partial swap, it always ended up benefiting Biden.
And every time that rule never changed.
And that was our constant.
And then we saw these data numbers and these everything's moving around and it makes no sense.
Nothing is following the pattern as it should be seen.
And it's basically abusive data.
But that's not a language that many in our society speak.
So you need to have it broken down in a way that is consumable and digestible and understandable by your average American.
So we try to make analogies and graphs and animations so that you can understand some of the things that are happening in everyday conversation.
And that's been really important to us because we want people to understand what's happening.
You know, data is not partisan.
It's not blue.
It's not red.
It's binary.
It's just numbers.
The numbers tell the story.
It's the truth.
And the thing that bothers us is that this data is publicly available.
Now, the Secretary of State are not making it available.
We have to go and find it.
We have to work for it.
But it's there.
And when you look at it, it doesn't make any sense.
And not a single person, whether Secretary of State, Edison, they're not debunking or debating our data.
Not one.
So tell me about what all your data sources are and how they compare to one another.
Of course.
So I think that's been a really big question for a lot of people is, if you're doing this data, why hasn't anybody else?
And that's a good question.
We don't have the answer to it either, especially for our elected officials, many of whom we've asked to take a very close look at this.
Because you don't need warrants.
You don't need subpoenas.
You don't need to do anything.
This data is readily available.
It is public information, and you can access it.
You can get it from the Edison website.
You can get it from the New York Times website.
It's taken directly from the JSON feeds of those websites.
We scrape the raw data, and then we've compared it to the Secretary of State's websites.
And in most cases, they sync up completely.
There are some indiscernible changes.
There are some numbers that are different.
It's a state-by-state case basis, and we can definitely talk about that in a more particular and precise way with our data analysts.
But there are changes, but they are not changes where it would change the outcome.
There's always a balancing act of the data, and so it always totals out.
So even if we see these strange errors and we compare them from the Secretary of State's website, we compare it to the raw data feeds from the JSON feeds on the Edison website, where it Different states that have the CITL server.
You know, we compare all these data sets and they're all the same numbers and they're comparing the same way and they're balancing out.
So either they're certifying results that have errors in them or Edison is giving information that's incorrect to the networks.
But regardless, there are errors in the data.
And that is not what we should be certifying any election on.
Let me get this straight.
Basically, you're saying that there's more errors in these contested states that election results have been certified around than would justify that certification.
That's correct.
You know, the one mantra that was going on during this election was that every vote counts, that every vote was important, that every vote mattered.
But it doesn't seem that that's very true.
When we see over 400,000 errors in the state of Pennsylvania, when we see direct switches, like we saw in Bibb County, Georgia, where there's over 12,000 votes that were swapped from Trump to Biden, these are people's votes.
This matters.
And for states to say, well, we're going to certify it.
You know, we saw that one big change in Pennsylvania where it went from a million six, nine to a million six, seven.
And they said, well, that was a clerical error.
Okay, let's just say that I give you that clerical error.
And maybe that's true that it was human error.
What about all the other errors?
Don't those matter?
Don't those deserve answers?
And why aren't we allowed to have them?
Why aren't the Secretary of State making all of this data completely available to us as the citizens of this country?
Why can't we see it?
How many of these errors as you describe them, and there's different categories, have you found overall across all your analyses up to now?
So speaking to Georgia specifically, right?
So Georgia is happening today.
This is a runoff election.
They've made zero changes to the way that they did the general election.
There's a lot of questions about the way that things were handled.
And there's a lot of questions about how the data was analyzed, the chain of custody of it.
And we took a very close look at that.
We spoke with People on the ground that understood that voting process.
Because remember, every state is very different how they do their votes.
I mean, we just did Pennsylvania, and there's between five and seven different processes.
So I was like, hmm.
So when we looked at Georgia, you look at Putnam, Dodge, Daughtry, all these different counties, there were over 30,000 votes that just disappeared.
We're not saying who did it.
We're not saying why they did it.
We just know that they did it because the data tells us that it happened.
And no one's saying that it didn't happen.
They're just not explaining it, and they're not addressing it.
Okay, and when you say they did it, is this machines or people or do you even know?
That's a very complicated question.
In some cases, human hands in the adjudication process, for example, do touch these votes and can change them and can modify them.
They can decide what they call voter intent.
I mean, if you talk about Richard Barron from the state of Georgia in our Georgia video, there was 113,000 votes.
He adjudicated 106,000 of them to determine voter intent.
So what that basically means is that that voter goes in and they put their ballot, and that ballot says, I want to vote for whomever.
They can go in and say, that actually doesn't look like what they meant.
We're going to put this down as someone else.
And you'll notice that I don't say one candidate over another, because it's not about that.
It's about actually having every vote count and actually allowing that vote to be the voice of the person who cast it as opposed to the adjudicator.
Have you tried to get answers as to what actually happened here?
Of course.
So the data is publicly available, as I've said, but it would be nice if the Secretary of States had released the data on the public side of their websites.
In order for us to get any of that information, it has to be FOIA'd.
We've been very lucky to work with a lot of congressmen and senators who are just as deeply interested as we are.
We'd like there to be a lot more.
But there have been individuals who are deeply concerned about what's happening, and they are putting those FOIA requests in to get the information directly from the source, whether it's from the Secretary of State, you know, their communications directors explaining to them that we need to find out answers.
In Maricopa County, there were five Board of Elector Supervisors in that specific county that all said that they were on board for a forensic audit, but the Secretary of State wouldn't allow it.
So what is the recourse in a situation like this?
What is it that you hope to accomplish with these examples?
It's a great question.
I think what we want to do is we want to make sure that all of these errors, when we start getting into numbers where we're talking about hundreds of thousands of errors, tens of thousands of switches, when we start to see these anomalies, corrections, You know, whatever adjective you want to give them happening over and over again in all these different states.
We're talking about six swing states, but it happened in several states.
And then you compare them to the states where it was a normal election.
If you can A and B those two, and you can look at what a normal election would be, you start to see it even more clearly.
And you understand something very strange happened here, and why doesn't anyone want to look into it?
Why would they move forward in Georgia today with an election having changed nothing?
With so many people coming forward, giving all of this information.
These are people that have been in voting polls and working their precincts for 30 years, some of them.
And they're saying things like, I've never seen anything like this.
This doesn't make any sense.
And so we just want to get those answers.
The data tells us the story.
We just need to read it and find out why.
For Georgia, do you think with the, I suppose, heightened level of scrutiny, which this election is going to be getting or is getting, I guess, as we speak and has been, do you think that makes a difference?
I do.
Specifically in Georgia with the adjudication process, one of the main arguments from Georgia has been, well, we've done two hand recounts.
Well, counting counterfeit money twice doesn't give you a different outcome.
It gives you the same outcome.
So what we keep trying to explain to people, and we testified to this point when we were in front of the Senate committee in Georgia, and we explained, if you put a ballot in and it's adjudicated, that original ballot with the voter's original intent is gone.
So now there's a new ballot that you have that can be printed, and then they can keep that as their record.
So now when you're comparing them, they all add up.
And it looks like, oh yeah, that voter wanted to vote that way.
But that's not how the voter voted.
That's how the adjudicator determined voter intent.
And that changes that outcome.
And that's why that doesn't make any sense.
And adjudication is supposed to have two witnesses from either party.
Sometimes there's even an independent as well.
Those processes didn't happen.
We spoke to the adjudicators.
They didn't have that at all.
So, bottom line is, with many of these ballots, it may just simply be impossible at this point to know the original voter intent.
Exactly right.
Especially because the ballots are separated from their envelopes.
They're put into a machine.
There's no audit logs.
There's no login credentials.
The machine is already logged on.
They don't even have to log in.
You don't even know who made the change.
There's no tracking.
This is our most important civic duty.
And we have absolutely no receipt or record of what we've done.
And now that we're pointing out the errors and we're showing that there is a trail and that the data speaks very clearly to that, they're saying, no, nothing to see here.
We don't want to look at that.
Why?
Why don't you want to look at that?
That would be important for any election.
That's our republic we're talking about.
And that should matter to everybody.
Well, great.
So now let's look at some of the data details here.
We'll bring the data scientists on.
I can't wait for you to talk to them.
They're fantastic human beings.
Well, Justin Mealy, Dave Laboue, great to have you here.
I want to start by actually finding out a little bit about you.
Let's not talk data right away.
Okay.
So for starters, Justin, a little bit about your background and what kind of motivates you to be doing this work?
Okay, yeah.
So I basically was in the military for nine and a half years.
When I was in there, you know, obviously that's about halfway to retirement and, you know, people have to make a big decision.
And I thought, you know, I love what I'm doing, but at the same time, I could be making a lot more money than the military.
I left and became a contractor.
So that's how I kind of got into the whole, you know, I was a contractor for the ODNI, you know, on a CIA contract for the ODNI. And as a contractor, I worked at the National Counterterrorism Center.
Now, when I was working at the National Counterterrorism Center, I did a couple things that were really, really cool data-wise, where I was able to do some things that save Probably about $10 million worth of money from the government.
And so when I did that, I thought, well, if I can save $10 million, can I make $10 million?
So that's when I left the government completely and then started off on myself and then just failed business after business.
That was that and eventually I had to kind of get a job and understand a couple things and get into a couple industries, got into advertising industry, got into working and building software and everything like that for commercial products and it kind of spawned a whole career That's what I do now.
I build software for one of the big four accounting firms.
I really love it.
When I got notice of what happened November 3rd, I thought I have the ability to look at this stuff in ways that maybe are different than some of the other people in the way that they're looking at it.
I thought, what can I contribute to see what happened?
Because it doesn't make sense to me.
Just looking from the outside, kind of intuitively, the data didn't really make sense and I didn't have the raw data, so I wanted the raw data.
I wanted to look at the numbers and say, oh yes, this is good and Trump legitimately lost, or this is not good and Trump legitimately won and Biden legitimately lost.
I need to know because I think that's our type of nerd passion.
It's like we need to know.
And so that's what drove me to come to the data integrity group.
So Dave Labu, tell me a bit about yourself and what brought you to this.
Absolutely.
So I've been working in all types of data for over a decade now.
And ever since I've sort of begun my professional career, I've always selectively engineered my own trajectory based on the data available within different types of industries.
Over a decade ago, data wasn't as freely available as it is now.
I worked in some primary research, quantitative research consulting, survey-based data, polling-type data.
From there, as industry caught up with data collection accumulation, I moved into other industries that had Better data collection capacity.
Again, I've always had a passion and interest in pursuing this type of analysis and understanding data and insights.
I moved into a few different areas.
I've worked in telecommunications, financial services, and most recently, obviously, being involved in this project, I've been working in the artificial intelligence space.
This is the next horizon for a lot of data.
New applications are coming out weekly, monthly for ways to utilize this type of data.
And when we saw what happened, obviously, on election night, that's some very interesting data patterns.
And from that, I thought, well, I'll need to explore exactly what happened here because I haven't seen these types of behavioral patterns anywhere in the quantitative research I've done, in the first-hand data collection across any industries.
And so that's what brought me into the mix of understanding what generated this behavioral pattern.
And as I got closer and closer and we started looking into the data and accumulating more and more resources, we were able to sort of Come to the point we are now with an understanding of what exactly happened.
That's pretty fascinating.
You've catalogued all sorts of errors in these contested states.
Now, just looking at the data, is this data anomalous compared to what normally happens?
Absolutely.
That was a key entry point that we went through early on.
We had to establish a benchmark for what is normal versus what is abnormal.
We began to isolate the states that were exhibiting these patterns and the ones that were not.
And as we now know, these sort of five key states were more highly concentrated than others.
Some states exhibited no anomalous patterns at all, which we said, this is great because we have a running benchmark to compare against for what we're seeing on this other hand as suspicious, irregular, and in need of deeper attention.
Bottom line is there are many states that have these irregularities and a deeper concentration within these five or six particularly.
Fascinating.
And how many of these baseline states did you look at, out of curiosity?
Well, we looked across all states.
And we also looked at the state level and at the county level and where available at the precinct level.
So we had Really an abundance of data, and that was across these data sources.
It was over 30 states that had some sort of strange activity to varying degrees of Unexplained behavior, we'll call it, and the remainder incremented normally.
So there was no, as we've seen, the decrements or vote switches or removals happening in that other bucket.
So that was nice to have as, again, the system's operating properly, the data's just flowing properly as it should be in these areas.
It's only these select few that we need to continue to investigate what's going on.
But wait, so you're telling me that these specific five states were just Off the charts compared to, I guess, the remaining 25 that had anomalies.
Absolutely.
I mean, each one individually could have an individual team devoted to it.
And as we know, time is a resource with these components as we've been watching the clock closely to understand that we can find a complete resolution In the due time we have available, we've had to focus on these states, so we've done the deepest dives there.
But even within that, there's quite a bit more going on in a relative scale to all the other states, absolutely.
When it comes to the kind of analysis we're providing on these states, you know, it starts off very, very, this kind of wide and very shallow analysis.
You know, you can think about it like filters at different levels.
So we have these like, you know, very kind of, you know, like filters with very, very large pores, and then as they get smaller and smaller and smaller, we just filter down.
And when we see what falls through is when we determine what we should focus on.
The thing is that when it came down to these swing states, it was every single swing state having things that fell down all the way to the bottom, where as deep as you go, you still find the problems.
That's the thing.
Error or an anomaly by itself isn't enough to just say, oh, well, fraud occurred.
You have to look very, very deep into data and you have to understand and kind of come up with reasons why and reasons, you know, things that could be explained and then test your theories against the data and it's this constant process.
Now to do that in one state, sometimes it could take us like a couple weeks.
For some states, especially when we have to go into a lot of different data sets, scrape data from a lot of different places, it's a very, very labor-intensive process only for the presidential elections.
There is an entire down-ballot ticket of all these things that we haven't even been able to look at.
We want to get to it, but obviously the importance for the country as far as the election is concerned, we had to focus on only the presidential election, and we had to narrow our focus down to just a couple states.
One thing that's really, really interesting to me, again, is this adjudication process that we've been discussing.
Again, you guys documented these large batches of ballots that would switch or be removed all at once.
I'm scratching my head trying to figure out how this could have happened and how this works.
Perhaps you can dig into that a little bit for me.
Sure.
I mean, you know, when we see some of these vote switches, it's completely theory to go into how the actual votes are switched or, you know, are decremented and things like that.
We're not really able to say that because we don't have the full trail of all the data.
But when you talk about adjudicated ballots, that in itself we pointed out specifically because it was a very, very high probability that there was some problem with that process because it was so insecure.
Because when you go and adjudicate a ballot, it destroys the audit history.
You can't go back in time and look at what that original voter vote intended to do with that.
Unfortunately, we can only look at the data as it is raw, and that's what our focus is limited to.
Bringing out those processes.
So part of that process of figuring out where the votes switch and everything like that, we have to map out that whole process.
So when we came to that adjudication point in the process, that's when we really learned, like, wow, there's something really weird going on here.
Because how could you have a process that is a complete break in the chain of custody?
It wouldn't stand up in court.
If I put that ballot up in court and said, well, this is the ballot, The opposition would just say, how can you prove it?
If this vote wouldn't hold up in court, how is it supposed to hold up against all the other votes in the land?
That's a real problem that we have with that.
That's why we focus on the adjudication.
There's some problem here.
It doesn't mean that this is how fraud occurred, because all we're doing is proving that that fraud occurred.
But this is a huge area of exploit, that somebody could easily exploit this situation into creating ballots for another candidate or for the candidate that they want to win.
But you have this scenario where you're documenting that you believe it's physically impossible to adjudicate the numbers of ballots in a reasonable way that were adjudicated in a relatively short period of time.
I just want you to kind of explain that to me.
Absolutely.
So, you know, what's really, really nice is that we do have documented one of the people in charge of the elections in Fulton County saying 113,000 ballots were cast, 106,000 were adjudicated.
Now, if you've seen our video or anything like that, obviously you put some of that information up.
That is a process that is pretty much all machine to machine to machine to machine, right?
And when we're looking at the adjudication piece of that, we're thinking, okay, what happens when you adjudicate, if we have 106,000 ballots that are adjudicated, when we talk to people who were adjudicators, we ask, what's the fastest you can adjudicate a ballot?
And you imagine it's me and another person sitting there, and we're looking at a screen and we're saying, oh, okay, it looks like it might be this, and then this person has to say, okay, yes, it's that, and whatever like that.
And it's about 30 seconds.
It's about the fastest you can adjudicate a ballot.
So if you could adjudicate a ballot at 30 seconds, Then that's basically two per minute, and you're saying 106,000.
I think that's like 53,000 ballots.
It comes out to about 883 man hours to do that, but the logs don't show that there's enough people to actually adjudicate that by the time that he actually gave that interview.
There's not enough physical time, and that's if you did every single one working 24 hours straight, working on all this stuff, and you had 30 teams or whatever that is, but you would be having all these people adjudicating in order to achieve that number specifically.
Of course, if you looked at a waterlogged ballot, or if you looked at a ballot that had an X here and a check there, it's not going to take 30 seconds anymore because now we have to talk about it.
But this is assuming the best case scenario, 30 seconds, is physically impossible without an amount of time to adjudicate that many ballots.
So if that's the case, and when you adjudicate a large amount of ballots, you can adjudicate a batch of 100 and just say, okay, 100 for Biden, 100 for Trump, however it works, and that entire batch will now destroy the record for every single one of those ballots and move the vote to exactly what they put.
These are supposed to be adjudications individually, but that doesn't necessarily happen.
Let me just get this straight.
Basically, the process could be one where these ballots are actually, in theory, adjudicated individually, but then when they're put into the system, there's a hundred or more at a time that are put in for a particular person.
They've kept a little tally or something like that.
I would say more like that it's physically impossible to adjudicate that many ballots one by one, because they could have started their adjudication on November 3rd, and they would maybe get through about 58,000 today.
So how could you do 106,000?
For the amount of teams that they had working on it, and we were asking, How long are you guys working on this?
Are you working in shifts?
And everything like that.
We were going really deep into their process.
And from what we could tell, it looked like you had a team of two people over here, and then they would be replaced by a shift of another two people.
And then there was another group.
So two separate groups adjudicating.
That's four people adjudicating.
How can you physically, even if you were to do that 24 hours a day, you still wouldn't be able to do that in time for today, you know?
So let alone one day later after the election.
But, you know, obviously that's just doing it individually.
Now if you did adjudicate them in large batches, Which goes against the entire adjudication process because you're supposed to look at that to determine voter intent.
You are supposed to physically inspect that with another person agreeing with that.
How could you physically inspect 100 ballots at once?
The machine doesn't allow you to do that.
It only brings up one in front of you and you both talk about it.
So how can that even be possible?
It doesn't make any sense to us.
What has been the response to you finding this out and sharing this with the Secretary of State?
Absolute silence.
Not one Secretary of State has refuted a single statement.
In fact, they've spent time to write articles about other people that testified in Georgia.
But they have not refuted our statement because ours cannot be refuted based off the knowledge of the data.
It is just actually how the data is.
There is no refutation for it.
I would love to hear it.
And that's the thing.
When we released these videos, we put out the data set.
This is what we used.
See what you can come up with.
If you can come up with something and an explanation for what we're seeing, that's what we're here for.
It's the data integrity group.
This is not the get Donald Trump elected group.
We want to know what happened and why these errors occurred.
That's what's important to us.
It is that nerd curiosity that led us to even look at this thing.
And we need to know why this happened, what the reasons are, line by line.
So if you say, well, there was this human error here.
That's fine.
That's one.
I need to know all these other 37.
Are they human error as well?
Are these machine error?
I don't know.
Dave, tell me a little bit about these 400,000 errors that you found in Pennsylvania that are detailed in this video that we published exclusively on the Epoch Times.
It's been great to partner with you on that.
Absolutely.
In Pennsylvania, I'll just say off the bat, as we're sort of getting deeper here into the background, obviously has its unique thumbprint in terms of what happened there within that data structure.
It has its unique processes of how the data is transmitted on the ground from when everyone puts their vote in the machine or the tabulator, and then how it gets transmitted through the computer system and onto the eventual certification process.
And in Pennsylvania, We noticed a few just major glaring irregularities, as we see in the video, to varying magnitudes and some of deeply concerning magnitudes.
And like Justin had said, if there is an explanation, then that's fine.
That's all that we would ask for is a thorough explanation and not simply a brushing off of an explanation that it's simply human error.
As data integrity, we'd like to know Well, the processes and the code in the background to confirm, given the gravity of the situation, that that is in fact what happened.
As a programmer, you like to know exactly what part of the program malfunctioned.
From a system log perspective, you want to know which of the logs had the error.
I don't think anyone out there with a background or knowledge of these systems and programming would be satisfied with a simple Oh, it's a human error.
Oh, it was just a glitch.
Because what does a glitch mean?
There's always a precise way within a computing system to identify exactly what happened and what went wrong.
So to get back to specifically Pennsylvania, a series of irregularities.
And this is something that is really within the data that we saw from either Election Day calculations or in absentee vote-type buckets, that votes are Oddly, and again not universally, I talked about benchmarks of we want to make sure that certain counties look right because if it's an error that is universal across all counties,
then it's likely to be some sort of system error that just was tripped up or some part of the code that is consistently erroring.
That's not the case.
It's irregular and More so infrequent, because we have more regular updates across these counties than we do the anomalies, and that's hence why they're the anomalies.
So focusing on those is where we bring out that deeply concerning drop in data.
And again, it's not just for Donald Trump.
We see the irregularities moving across third-party candidates, write-in candidates, and there's just been no explanation, if you were to evaluate this from a strictly data standpoint, why this would occur.
I mean, whether it be You know, intentional manipulation or a system, there needs to be an answer.
And I think that anyone, like I said, with a background and understanding of these processes, deserves to know precisely what that answer was, not a simple brushing off of, oh, it's a human or a log or something.
We'd like to see the log.
I do want to bring out one point, too, which was we're not saying that negative votes didn't occur for other candidates, which is still worrying that you're seeing any kind of negative drop in votes, you know.
So that is still a problem.
It occurs for other candidates.
It occurs for Raiden.
It occurs for Joe Jorgensen.
It occurs for Joe Biden.
But the degree is, you know, we're talking about magnitude degree of votes.
And the thing is, is a lot of times when you see those drops in votes for other candidates, you also see them go back up.
But with Donald Trump, he'll have a drop in vote, and then he just won't recover.
So that does point to another characteristic that separates it.
So I think the line that we had about that, to clarify that, is to say that while we did see drops in votes for other candidates, it's not the same type of drop.
And I'd like to say something further in Pennsylvania, too, which is our processes, there's a data engineering sort of database structural component, which is almost the entry point.
And that's where we say, okay, well, now we know where to focus.
And then sort of the analytics analysis kicks in where we look at statistical anomalies at that point.
So there's a multi-tiered process that we're going through to ensure that what we're seeing With all the knowledge we have on the team, horizontally, vertically, that none of us can explain with the depth of what we know what's happening there.
And it's when we reach the end of that rope and say, there's no reasonable explanation that we say, So what is it?
Because the combination of database oddities, anomalies, irregularities, and statistical, I mean just sheer, I mean the improbabilities of some of these things that we're bringing out are just way off the charts in terms of what you'd expect in a normal distribution or any type of expected behavior.
And once again, you're making these data that you've scraped available to everybody to run whatever analyses they want on to try to offer alternate hypotheses as to what happened.
Yes, and the other thing, too, is it is complicated, obviously, for those that have a knack for programming.
We have all the code available.
We've written it across a few languages and sort of cross-verified that obviously everything we're doing is accurate and precise.
So everything is open and out there in the spirit of transparency because we want to know the answer to the question as to why this happened.
There's been no reasonable explanation.
We've actually talked to a couple people that, you know, you can consider to be opposing arguments for the things that we've brought forward.
And one example would be in Arizona.
We talked to a person that has a Twitter handle.
I think it's like the data guru or something like that.
His name is Garrett Archer.
And he took a look at this stuff and he He provided some reasons why he thought it didn't work.
And we had a conversation, and we talked to him about all that stuff.
He started off saying basically that there's absolutely no tomfoolery going on in that election, and by the end of the conversation, he agreed, yeah, maybe we should have a forensic audit.
Looking at the data.
And that's the power of looking at data.
And I give him a very great amount of credit for understanding it from an objective standpoint.
He's a reporter.
He looked at it from an objective standpoint and understood that, yeah, we do need to look at this, maybe.
And he agreed that we should possibly have some sort of forensic audit of the election inside of Arizona.
Again, the different...
The different types of things we found are different per state.
We were looking at specifically his state and talking to him about his process, which he knew intimately.
John Basham, great to speak with you as part of the Data Integrity Group.
Good to be here, Jan.
You played a role in getting these people together.
You're one of the on-camera personalities in the group.
How did this group actually come together?
Well, right after election night, and even on election night, I think there were a lot of us who were working data.
My expertise is in numerical weather predictions.
We deal in large numbers and vast data sets.
And those of us who were watching the election noticed that the numbers Didn't work in any scientific sense.
They didn't work statistically.
They didn't work in the simplest thing, which is votes should never go backwards.
There was a group of folks who were online within the first 24 hours who were data scientists, and we all started kind of comparing notes.
In the first week, I think it really started, I put a tweet out that had leveled a few of the states, I think six states if I recall correctly.
That said, these are just initially some of the first things that we've seen in the raw data that we are desperately trying to get.
And there's something wrong here.
Something doesn't look right.
And that tweet garnered so much attention that Linda McLaughlin reached out to me and said, hey, where are you going with us?
What's going on?
What can we do with this?
And from that moment on, we kind of started putting our heads together and thought, there is no group out there that's looking at the data.
Everybody's looking at it from a political standpoint.
It's got to be you're either for Trump or you're against him, you're either for Biden or you're against him.
That wasn't the case.
It was you're either for A real vote counting for one person and what their vote was, or you're against that vote being counted.
It was really that simple.
It is the American Republic.
America's exceptional because of what we've got.
And if we ignore that, it'll be gone.
I reached out to a large group of data folks, and a lot of them were very worried about even coming into the fold.
There are some incredible folks who have contributed, but wouldn't put their name on stuff because they are afraid that they would be attacked because they would think, oh, you're taking a political stance.
Well, the numbers aren't political.
We actually just looked at the numbers, which, by the way, was hard to do to get those numbers.
After going back and forth, and it did take some time, it's not like there was a company in place or a group in place or even systems in place to figure out how to attack all of these states and all of this data that's done different in every state and many times done different in every county.
We had to cobble together a group of experts, and it took some time to put that group together.
Once we did, we have the top level, which is the data integrity group, the group that you have seen and talked to.
There are a tremendous amount of support people behind that who are experts in very specific things, whether it be programming in different languages, or whether it be large number theory or machine learning.
So what we started doing was coordinating to get these group of very intelligent, very driven people who all saw the same thing.
And it wasn't that they saw that Joe Biden had won.
It wasn't that they saw that Donald Trump had lost.
As a matter of fact, our group has liberals, it has conservatives, and we have someone in our group.
I won't call him out.
But who didn't even vote in the election.
So it really came down to we were passionate about the data, the numbers we were seeing, and the fact that those numbers, every one of those numbers, represented a vote and a person's intentional vote towards something.
So it had to be right.
And that's how the group started to get cobbled together.
And then thousands, and I really mean thousands of man hours since Election Day, Of non-stop video conference calls with the group in 12 and 14 hour runs, putting together data, gathering data, checking the data to make sure we were right.
The biggest thing that we've seen in this entire scenario is people will come and they'll attack you because you've said something that's against the narrative.
Well, look, there's no narrative in numbers.
It's either The number is correct or it's not.
So for us, it was very important that we got the numbers right, which was a very hard thing to wind up doing, is gathering those numbers to begin with.
You actually mentioned that it wasn't so easy to get this data.
It's publicly available.
There is no central gathering point in government for this data.
You would think that there is one.
There's not.
You have to go to the individual states and counties to compare it with the Edison data.
And there's also, I think, there's no central place that shows you what's called the time series of the data as the data came in throughout the night.
Most of the things that we see are The end result, okay?
At the end of the day, the next day, oh, we've had 500,000 votes in this county for this person, 400,000 for this person, that's it.
Well, what we found was it was very important to look at the time series.
That's where we found these errors where votes would either go backwards or they would disappear completely and then maybe reappear later in another account.
But the fact was Government did not cooperate with us.
We reached out, in many cases, trying to get data and trying and trying, and we had no real help.
There were some places where it was much easier to get what we were looking for, but we actually had to invent systems, write computer scripts, write programs, to read things.
Data either off of raw PDF forms in one case or whether it was in what's called a JSON data in another case and we would have to go in and rip the data in pieces one piece here and one piece there and cobble it together and then it became a very important thing for us to make sure what we had cobbled together was correct because the last thing we wanted to do was say this is what we found and then later say our data was wrong so we had to compare In this case,
we did three sources.
We wound up making sure that whatever our final numbers were matched with the Secretary of State, the Edison data, or within just a couple of votes.
And I really mean a couple, two or three.
And then, in many cases, if we were looking at a specific area, for instance, in places in Pennsylvania where we would look at a single county, we would make sure that we were looking at the county data.
None of those things, whether it be Edison, the Secretary of State, or a single county, not one of them uses the same data format.
They don't report it the same way.
They don't put it on the same kind of tables.
They don't use the same programming language.
So this very intelligent group of data scientists and programmers and machine learning experts had to build a system to gather from each individual source At every county.
And it is a different solution for each county in order to gather that data.
So you can see how intensive it was for us to get this data.
The sad part is it should have been something that the government was saying, here, take a look.
It's your vote.
It's our election.
This is our democracy.
Take a look.
This is what we've got.
What do you see?
Because that's how you trust in an election.
You show everyone what happened.
But that's not what's happening now.
Well, I guess you have a few, I'm seeing recommendations that your group has come up with.
One of them is, it seems to be, and correct me if I'm wrong, that you're saying a forensic audit on all of these contested states is essential to deal with these anomalies, to deal with these irregularities.
That's one thing.
Another one it seems to be that you're saying is that the government should actually organize this data in a way that people can access easily.
Right.
Well, I think obviously there's two sides to this.
The future, what we do going forward in our democracy is, yes, there needs to be a single format for the way data is given, and it should be open and transparent at all levels.
You shouldn't have to pay $20,000, $30,000, $50,000 to get a list of the people who have voted or the data that you're going to need to verify that an election was, in fact, up and up.
As far as the forensic auditing, we've kicked this around, and one of the things that hit us was the vote in America is probably one of the most basic human rights we have.
It is a human right, and you have a background in human rights.
This is so important.
Everything hinges on that vote.
Your entire sense of freedom, what you can and can't do, that vote matters.
But take a step back.
People look at that in the big general sense and they don't think about it.
It's very kind of an out there feeling.
Oh, that sounds good and I get that.
Let's make it your bank account.
Would you accept your bank after you went in and you put your check in?
You know how much your check was.
You come back the next day and the total is different.
I don't care if it's $5 different or $500 different, whether it went up or went down.
Would you accept that from your bank that your totals would change just magically?
And when you went to ask them about it, they recounted and then they got another total different than the first two.
And then when you said, wait, wait, wait.
I need to see the data.
Let me see the books.
How did this come in and out?
They say, no, no.
You've got to trust us.
Our tellers tell us that this is right.
Well, that's your bank account.
And I guarantee you, you would fire your banker.
In a heartbeat, you would fire your banker.
But the vote is more important than your bank account because they can take all your money with a vote.
That's where we are.
And I think that people aren't reaching out, reaching out to their senators, reaching out to their congressmen, getting on the phone and saying, we have to get a forensic audit, an actual audit look at the entire trail.
And I would love for someone to explain Completely explain away and say, no, no, no, this is right.
And let me tell you why you're missing something.
We are open to that.
I would love to see that happen.
Because then all of these people, and you've seen some of the polling where we've seen upwards of 40% of the American public, don't believe that this election was fair and that it was valid.
Well, if that's the case, we've got a problem in our republic.
We need to bring back a situation where, I hearken back to the Bush-Gore The election where we had the infamous hanging chad.
Whether you agreed or disagreed with the way it ended up at the end, the bottom line was America got to look over the shoulder of all of those people who went and had to recount those votes.
They had a Republican, a Democrat, and the county worker.
They were holding it up.
There were cameras there.
Everything was Visible.
The courts were engaged.
They did it immediately.
The Florida Supreme Court, and then within hours, the U.S. Supreme Court, and then they'd start the process again.
This election seems so very different than that.
It seems as if the courts don't want to engage, even though there are very clear indications that something's wrong.
Fraud?
Not fraud?
Look, there's something wrong.
The data isn't correct.
One plus one doesn't equal seven.
And if the numbers aren't matching, we need to just have answers as to why those numbers are wrong.
Whether you think it was fraud, whether you think it was mistakes, whether you think there's a valid reason for how it happened, we need transparency in our election and our electoral process to have trust in it.
Any final thoughts before we finish up?
Democracy is a very important thing.
Our country, if you stop and think about the United States, it's an amazing, exceptional place to live, to grow up.
Hundreds and thousands of people immigrate here because we are that great American dream, the shining city on a hill that Ronald Reagan said.
It's true.
If we Throw away the simple part that makes us so great, that the American people's voice matters.
And we ignore when something goes wrong and the people say, hey, we don't trust this process.
If we ignore that, We're throwing away American exceptionalism.
It's not worth throwing away.
We need to save this.
We need to look at this race.
And no matter what the answer is, no matter who sits in the White House, that's secondary.
The important thing is, let's find out what the real answer is to why this election looked so wrong.
Well, John Basham with the Data Integrity Group, such a pleasure to have you on.
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