In this discussion, Dr. SHIVA Ayyadurai talks about the recent extended study newly published by the Election Systems Integrity Institute that confirmed the results of an earlier pilot study which found that no less that 200,000 mail ballots with mismatched signatures were counted without proper review (“curing”) by Maricopa County during the 2020 General Elections.
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Key Points
- Dr.SHIVA Ayyadurai, MIT PhD – Inventor of Email, Systems Scientist, engineer, educator – gives an interview to Real Talk 93.3 on how opaque Election Signature Verification process is.
- At minimum, 215,856 early voting mail ballots (EVBs) should have been cured in Maricopa versus the ~25,000 cured by the County in the 2020 General Election.
- This updated Extended Study (“the Study”) along with the Pilot Study are the first to calculate signature mismatch rates of EVBs for Experts – Forensic Document Examiners (FDEs), Trained Novices (non-FDEs), and in a Two-Step Review process using non-FDEs and FDEs.
- One constraint of this Study is not having access to the signature files from the County.
- Given the nearly 10x difference in EVBs to be cured between this Study and the County’s actual number cured, if the County were to provide their signature files, an update to this Study can be performed.
- Maricopa County Election Dept. states it has a “rigorous signature verification process.”
- Of the 1,911,918 EVB signatures verified, the County reported only 25,000 were flagged as signature mismatches requiring review – “curing;” and after curing, the County concluded only 587 of the 25,000 (2.3%) to be “Bad Signatures.”
- This Extended Study confirms the findings of the earlier Pilot Study and concludes that the process used for signature verification in Maricopa is a flawed signature verification process.
SUMMARY KEYWORDS
envelopes, ballot images, signature, vote, curing, arizona, people, called, match, analysis, election, counted, process, audit, led, determined, systems, contracted, maricopa, signature verification, ballot envelopes, mail ballot, AI
Interviewer: I’m almost speechless. I’m so excited by our guests coming on Dr.Shiva dialing in from the east coast. He holds four degrees from MIT. I should share that. The inventor of the email, and a Fulbright Scholar and an individual who got into the elections early on. And Jim and I, we connected with him back in November of 2020.
And had a couple presentations with him where we were looking at the numbers of these patterns and things like that things just didn’t seem to add up. And since then he’s continued, we continue to dig into this. At least from my perspective, there’s absolutely something wrong and a lot going on. So anyway, Dr. Shiva, it’s an honor to have you on the show. Thanks for dialing in,
Dr. Shiva: It’s great to be here Joe. Just to let you know I went to MIT. I used to teach there but I am not a professor there, just to correct.
Interviewer: You just have an incredible resume as I’ve read through here. And I shared it with our audience too, before in the last segment. So, we’ve tried to give you a little bit of a heads up of our audience and what’s going on. So, do you want to share how you kind of got into we’ve got about 15 minutes, how you got into the entire election thing. What you found, and then more recently, what you found in Arizona?
Dr.Shiva: Sure, I think the first thing, my background, Joe, is in large scale engineering systems. You know, biological systems, be it, different types of systems in mechanical engineering, electrical engineering, even large scale media systems. So, I’ve been fascinated by this. It has been sort of my life’s work since I was a 14 year old kid when I created the first email system back in 1978.
So, it’s not usual for an engineer or a scientist to enter, you know, electoral politics. But in 2017, I did it, to go against Elizabeth Warren, you know, under the campaign slogan, “Only the Real Indian Can Defeat the Fake Indian”. It was very, very, light, people liked it a lot. But we were the ones who forced her to take a DNA test, we had a massive ground movement. And that slogan was really exposing the hypocrisy of this woman who is in many ways the real racist. She used race for her own advancement.
And then in 2020, we ran as a Republican. In the Republican primary, we had 3,000 volunteers on the ground, 20,000 lawn signs, the word on the street by everyone was, Dr.Shiva is going to win that election. That was, you couldn’t get away in Massachusetts from seeing that slogan. That’s why I used the third person, Dr.SHIVA. We were on billboards, massive signs, massive support, and the word was that I would win by a massive landslide.
But on September 1, 2020, when the results come in, we win in all the hand counted counties. One of the hand-counted counties, the main one that counted their votes all by hand, Franklin County, we won by 10 points. And everywhere else, Joe it was 60/40 60/40 60/40, to an opponent who no one even heard of. Who would have maybe had one lawn sign up.
So that’s why it led to my view that there’s something seriously wrong in the US electoral system. Led to my journey and understanding of election systems led to my realization that in Massachusetts, they deleted, illegally, the Ballot Images. Every vote, every paper ballot gets converted into a Ballot Image, which the electronic AI on the voting systems actually reads and analyzes and converts to what they want.
The AI determines the vote when it has a problem, it gets adjudicated. So I learned all this about the election systems. As many of your viewers know, between 2020 September, all the way till today, I did a lot of work on analysis of various counties and towns.
In fact, it was I would say, with all humility, that work I did and everyone knows in Arizona that led to the audit. And then I got involved in the Arizona audit, unfortunately towards the end of it. The initial work I was contracted to do was to analyze the Ballot Images. Interesting enough here the Ballot Images again, right! In Arizona it was unfortunate because I was never, even though I was contracted by the auditing main contractor Cyber Ninjas.
They had the Ballot Images in their position and gave us corrupted Ballot Images. It was only after a lot of persistence did I finally get them in December after the audit was done. But during the period from September to December, I was contracted directly by the Arizona Senate on a different project to analyze the envelopes, which are what the ballot travels in. In Arizona nearly 92- 93% of all the votes were done through mail in ballots or early voting ballots, 93%. Only 7 or 8% was done in person.
So we got provided with all the envelopes images, and the first project was to analyze if there was even a signature. Basic analysis on the cover of the envelopes. We detected various anomalies, and when we shared that with the public we were attacked. Unfortunately, viciously by the election officials, every time we put something out, they would say, Oh, you don’t know this, you don’t know that. But through that process, we brought out to the public various processes, which most of the public wasn’t even aware of.
One of those processes was called the curing, C U R I N G, what is curing? Curing means when an envelope comes in, with a ballot inside of it. All of those envelopes, Joe, are scanned into what is called envelope images by an organization called Runbeck.
So in Arizona, nearly 1.9 million ballot envelopes are scanned. And the reason and they cannot be opened until a very, very important process occurs called signature verification. So a trained novice, a trained volunteer, quote unquote, “trained staff” sits in front of a monitor and they are given two images on a screen. The image of the signature, let’s say Joe Hoft.
So the envelope on the right side, the genuine signature that appears on the voter registration form, say from DMV, and within about four to 30 seconds in this initial review process, that train staff member has to make a decision. Is this a match or not? Okay, if it is, if it is, in fact, a match, then the envelope is open, and then the vote is counted.
If it is not a match, in this initial review, it goes up to an expert, quote unquote, expert, called the manager who I believe has more skills, and they do the initial review of anything that they said was not a match.
If that manager determines it’s not a match, then it’s sent to curing, in curing they call the voter up, make sure the person didn’t have a medical handicap or they’re alive and all those processes. Then it is determined in fact, if it’s determined after that they can’t get through, they can’t verify then it becomes what is known as a bad signature.
So let’s look at the numbers in Maricopa as we put out in our report. Out of the 1,911,918 envelope images that came in from those voters, 25,000 was determined not to be a match, which is 1.3%. And then, of those 25,000, 587 were determined to be bad signatures. Which is three one hundredths of a percent of all the envelopes that came in, or 2.3% after curing.
Okay, so we have been asking the county as a part of 1 of the statements of work we filed with the Senate to get the envelopes. You know, the signatures on file, because we have the envelopes, so we could run our process and match signatures and we never got that.
So we went forward, Joe, and we literally were very fortunate in Maricopa for the recorder’s office. And people can go to, I think it’s called the recorder’s office / Maricopa, people can find it on the internet in Arizona. They have every deed that someone has ever filed, you know, so if you bought a home, there’s your deed and your signature at the bottom.
So what we did was initially in the initial pilot study that we filed. What we did about two, three weeks ago, we took about 500 samples of the 1.9 million, again, in sampling theory, that would be considered 95% confidence. And I basically wanted to do
an initial investigation, as most scientists do. And we found out from that initial investigation, about 200,000 votes were counted and those ballots never went to curing.
Okay? And we shared that findings. But you know, we could easily be attacked by critics saying, Hey, you only did 500 out of a sample of 1.9 million while that’s still, you know, 95%.
So we cracked it up to 2700 samples that we did, which is 99% confidence, with a margin of error around 2.5%, and that we released yesterday. In those findings yesterday, we also did much more sophisticated analysis, where for every envelope image that we had, we hired three trained novices and three trained forensic document examiners, these are people brought into court as experts, and we did the same process.
First, the novices reviewed the ballots, if they said something was a no match, then it went over to the FDs. You know, we had their numbers and we did what’s called the joint probability, we came up with 11.29%. 11.29% is about 215,000 ballots, images, or envelopes that should never have been open until they were sent to curing. So that is about 10 times more than what the county actually cured.
Now, the critics could attack us, they could say, hey, well, you didn’t have the genuine signature from the county.
When we did that analysis, we actually went and removed those even if all six people said, all three trained novices and three trained experts here said that hey, all of these are absolutely not matching. Okay? So instead of accepting even that no match, guess what we did, Joe?
We eliminated that even from the pool, okay. Because we said, oh, maybe they were all no matches, not because they were no match. But because maybe the signature that we got from the deeds repository was wrong, that’s quite a conservative thing we did.
And then when we got that 11.9%, that was done with it, reducing the potentiality of error. The bottom line is the 10 times curing rate, which should be cured is significant. Maybe it’s, maybe we’re wrong? Maybe we didn’t get all the right genuine signatures, but 10x 215,000 versus 25,000, it’s significant.
So the next move is now obviously, we’ve given all the data to the Attorney General of Arizona, and to the Senate. And I think the next step that needs to happen is the county, if they want to be you know, they’re so confident about their 25,000, which I believe they are. Then they should give us access to the signature files that they have.
And we’ll sign whatever non-disclosure agreements, and we will rerun – update even ours. And that’s where we’re at but the curing process was actually set up by the left, because they were saying, hey, you know, a lot, you know, the mail in ballots and people sign. You know, we’re throwing away votes. So the curing process was set up, if something was a potential mismatch, you give them a shot.
So even by that standard, the bottom line is that even if you accede to that, and you say, okay, mail in ballots are what you want, well, you’re not curing enough of them. In this case, our estimate, with 99% confidence, two and a half to 2.7 margin of error was 215,000 should have been cured. So, that’s the net of it Joe.
Interviewer: Yeah, that was fantastic. And it doesn’t mean necessarily that there’s 200,000 errors. But there’s 200,000 ballots that should have been checked.
Dr. Shiva: Exactly right right
Interviewer: By the way, I want to mention a couple other things. Biden was given the election in Arizona by about 10,000 votes. We know that previously, in the Senate presentation, where you presented your findings on the envelope analysis. The analysis that you were able to do some, some time ago now. I think you came up with even then there were 17,000 duplicates?
Dr. Shiva: Well, what we found was, this is where we brought to the forefront the concept of curing. So we found that out of all the envelope images that we got, there were 17,133 duplicates from 17,126 voters. Okay. So they said, Well, you know, you don’t know anything about how we process.
That’s when we found out they have this process called curing. Joe, and what they say was, oh, that’s an artifact cure. We take you know, when we find a questionable signature, we then take the original ballot, we copy it, and then we put the verified and approved stamp.
Okay, so great. So that led to well, why don’t you tell people that you do all this stuff? You know, why isn’t this widely known? Why? So that led to us doing this? And what’s interesting, Joe is in their report they didn’t say the exact number that was cured on January 22 they said up to 25,000! I find that very curious. Why don’t you tell us the exact number? Why are you leaving it very vague, up to 25,000?
Interviewer: The other thing that grabbed me from this whole analysis was the fact that Runbeck who is the printer of the ballots. Is the same contractor who they hired to be
the firm that receives these ballots and then does the signature verification? And I thought that’s kind of an odd process, I have some concerns with that same company that can print out a ballot, and it’s gonna do this adjudication?
Dr. Shiva: Yeah, I mean, I think the key takeaway is for people to recognize that from an engineering standpoint, you have stuff coming in one end of the system and stuff coming out the other end. Matter cannot be created nor destroyed. So if you have X number of people showing up to vote, you should have X number of votes. Right?
And if there’s a difference in that, you should be able to exactly know that in a very simple way. When we did the Ballot Image analysis, which is separate from the envelopes, we found out that there were certain cases where things weren’t matching. And that revealed a very important process which was that artificial intelligence machines are what is counting the votes? And it is what is determining what should go to human adjudication.
Now, this is very important for legislators listening to understand. I would guarantee you right now, no person who has been elected to any office in the United States actually knows how a vote is actually determined by any of these AI machines. Does the voter have to fill in that circle all the way? Or 50% of the way? Or, you know, 1%? You see what I’m saying? In the old days, when human beings counted these votes, they’re very clear instructions, you know. iI you poked a hole during the punch, Oh, that would have to be all the way.
You know, there’s very clear processes, but we’ve outsourced the actual determination of what a vote is to artificial intelligence. Fine, if you want to do that, then legislators should be people should be telling Oh robot, this is what is a vote, this is an undervote, this is an over-vote. And this is what came out with a Ballot Image analysis.
We also found that we could do audits, you know, within weeks! If you want to count the paper ballots and that – if the paper and the Ballot Images are the same, right? Then first do the Ballot Images, save taxpayers money! We could do that very, very fast. That was done at the end of the audit, not, you know, in April.
Interviewer: So it saves a lot of money?
Dr. Shiva: It saves a ton of money!
Interviewer: During the audit, there was one more piece that really grabbed me that you were not the one who did the signature match during the audit.
Dr. Shiva: That wasn’t part of the contract, which you know, the Senate had to fight a lot to get this data – subpoena the envelopes, images, the images. Well, the next fight is to get the voter file signatures. There you go.
Interviewer: Wow, I tell you what, hats off to you. It’s an honor to have you here. We appreciate all you do and would love to have back some time. Just really appreciate all you have done doctor. Thank you so much.
Dr. Shiva: Be well, thank you Joe, you and your viewers.
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