Election forensics

Last updated

Election forensics are methods used to determine if election results are statistically normal or statistically abnormal, which can indicate electoral fraud. [1] It uses statistical tools to determine if observed election results differ from normally occurring patterns. [2] These tools can be relatively simple, such as looking at the frequency of integers and using 2nd Digit Benford's law, [3] or can be more complex and involve machine learning techniques.

Contents

Method

Election forensics can use various approaches. Methods include :

Application

Between 1978 and 2004, a 2010 review concluded that 61% of elections examined from more than 170 countries showed some signs of election fraud, with major fraud in 27% of all examined elections. Since the early 2000s, election forensics has been used to examine the integrity of elections in various countries, including Afghanistan, Albania, Argentina, Bangladesh, Cambodia, Kenya, Libya, South Africa, Uganda, Venezuela and USA. [7] [2] [8]

Election forensics tools have been used to conclude, with high probability, that vote counts have been manipulated in official elections in Russia, [9] Ukraine, [10] Egypt, [11] and USA. [12]

Compared to other methods

Relative to other methods of monitoring election security, such as in-person monitoring of polling places and parallel vote tabulation, election forensics has advantages and disadvantages. Election forensics is considered advantageous in that data is objective, rather than subject to interpretation. It also allows votes from all contests and localities to be systematically analyzed, with statistical conclusions about the likelihood of fraud. [2] Disadvantages of election forensics include its inability to actually detect fraud, just data anomalies that may or may not be indicative of such. Election forensics expert Walter Mebane has noted that various election forensics methods might actually flag non-fraudulent behaviour like tactical voting as fraud. [13] Further some experts believe that 2BL and other methods are useless for analyzing elections.

This can be addressed by combining election forensics with in-person monitoring. Another disadvantage is its complexity, requiring advanced knowledge of statistics and significant computing power. Additionally, the best results require a high level of detail, ideally comprehensive data from the polling place regarding voter turnout, vote counts for all issues and candidates, and valid ballots. Broad, national-level summaries have limited utility. [2]

Related Research Articles

<span class="mw-page-title-main">Steganography</span> Hiding messages in other messages

Steganography is the practice of representing information within another message or physical object, in such a manner that the presence of the information is not evident to human inspection. In computing/electronic contexts, a computer file, message, image, or video is concealed within another file, message, image, or video. The word steganography comes from Greek steganographia, which combines the words steganós, meaning "covered or concealed", and -graphia meaning "writing".

<span class="mw-page-title-main">Benford's law</span> Observation that in many real-life datasets, the leading digit is likely to be small

Benford's law, also known as the Newcomb–Benford law, the law of anomalous numbers, or the first-digit law, is an observation that in many real-life sets of numerical data, the leading digit is likely to be small. In sets that obey the law, the number 1 appears as the leading significant digit about 30% of the time, while 9 appears as the leading significant digit less than 5% of the time. If the digits were distributed uniformly, they would each occur about 11.1% of the time. Benford's law also makes predictions about the distribution of second digits, third digits, digit combinations, and so on.

<span class="mw-page-title-main">Packet analyzer</span> Computer network equipment or software that analyzes network traffic

A packet analyzer, also known as packet sniffer, protocol analyzer, or network analyzer, is a computer program or computer hardware such as a packet capture appliance that can analyze and log traffic that passes over a computer network or part of a network. Packet capture is the process of intercepting and logging traffic. As data streams flow across the network, the analyzer captures each packet and, if needed, decodes the packet's raw data, showing the values of various fields in the packet, and analyzes its content according to the appropriate RFC or other specifications.

An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Any intrusion activity or violation is typically either reported to an administrator or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources and uses alarm filtering techniques to distinguish malicious activity from false alarms.

Electoral fraud, sometimes referred to as election manipulation, voter fraud, or vote rigging, involves illegal interference with the process of an election, either by increasing the vote share of a favored candidate, depressing the vote share of rival candidates, or both. It differs from but often goes hand-in-hand with voter suppression. What exactly constitutes electoral fraud varies from country to country, though the goal is often election subversion.

<span class="mw-page-title-main">Computer forensics</span> Branch of digital forensic science

Computer forensics is a branch of digital forensic science pertaining to evidence found in computers and digital storage media. The goal of computer forensics is to examine digital media in a forensically sound manner with the aim of identifying, preserving, recovering, analyzing and presenting facts and opinions about the digital information.

<span class="mw-page-title-main">Forensic accounting</span> Branch of accounting which investigates financial misconduct and fraud

Forensic accounting, forensic accountancy or financial forensics is the specialty practice area of accounting that investigates whether firms engage in financial reporting misconduct, or financial misconduct within the workplace by employees, officers or directors of the organization. Forensic accountants apply a range of skills and methods to determine whether there has been financial misconduct by the firm or its employees.

A host-based intrusion detection system (HIDS) is an intrusion detection system that is capable of monitoring and analyzing the internals of a computing system as well as the network packets on its network interfaces, similar to the way a network-based intrusion detection system (NIDS) operates. Compared to NIDS, HIDS is superior in its fine granularity and its ability to detect internal attacks. This was the first type of intrusion detection software to have been designed, with the original target system being the mainframe computer where outside interaction was infrequent.

Computer-assisted audit tool (CAATs) or computer-assisted audit tools and techniques (CAATTs) is a growing field within the IT audit profession. CAATs is the practice of using computers to automate the IT audit processes. CAATs normally include using basic office productivity software such as spreadsheets, word processors and text editing programs and more advanced software packages involving use statistical analysis and business intelligence tools. But also more dedicated specialized software are available.

<span class="mw-page-title-main">Digital forensics</span> Branch of forensic science

Digital forensics is a branch of forensic science encompassing the recovery, investigation, examination, and analysis of material found in digital devices, often in relation to mobile devices and computer crime. The term "digital forensics" was originally used as a synonym for computer forensics but has expanded to cover investigation of all devices capable of storing digital data. With roots in the personal computing revolution of the late 1970s and early 1980s, the discipline evolved in a haphazard manner during the 1990s, and it was not until the early 21st century that national policies emerged.

Lie detection is an assessment of a verbal statement with the goal to reveal a possible intentional deceit. Lie detection may refer to a cognitive process of detecting deception by evaluating message content as well as non-verbal cues. It also may refer to questioning techniques used along with technology that record physiological functions to ascertain truth and falsehood in response. The latter is commonly used by law enforcement in the United States, but rarely in other countries because it is based on pseudoscience.

Anti–computer forensics or counter-forensics are techniques used to obstruct forensic analysis.

In data analysis, anomaly detection is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. Such examples may arouse suspicions of being generated by a different mechanism, or appear inconsistent with the remainder of that set of data.

In machine learning, one-class classification (OCC), also known as unary classification or class-modelling, tries to identify objects of a specific class amongst all objects, by primarily learning from a training set containing only the objects of that class, although there exist variants of one-class classifiers where counter-examples are used to further refine the classification boundary. This is different from and more difficult than the traditional classification problem, which tries to distinguish between two or more classes with the training set containing objects from all the classes. Examples include the monitoring of helicopter gearboxes, motor failure prediction, or the operational status of a nuclear plant as 'normal': In this scenario, there are few, if any, examples of catastrophic system states; only the statistics of normal operation are known.

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.

The 2009 Iranian presidential election was characterized by huge candidate rallies in Iranian cities, and very high turnout reported to be over 80 percent. Iran holds a run-off election when no candidate receives a majority of votes, and this would have been held on 19 June 2009. At the closing of election polls, both leading candidates, Mahmoud Ahmadinejad and Mir-Hossein Mousavi, claimed victory, with both candidates telling the press that their sources have them at 58–60% of the total vote. Early reports had claimed a turnout of 32 million votes cast although the actual figure could not be determined until all of the votes were counted. Mousavi warned the Iranian people of possible vote fraud.

Fraud represents a significant problem for governments and businesses and specialized analysis techniques for discovering fraud using them are required. Some of these methods include knowledge discovery in databases (KDD), data mining, machine learning and statistics. They offer applicable and successful solutions in different areas of electronic fraud crimes.

In network theory, link analysis is a data-analysis technique used to evaluate relationships between nodes. Relationships may be identified among various types of nodes (100k), including organizations, people and transactions. Link analysis has been used for investigation of criminal activity, computer security analysis, search engine optimization, market research, medical research, and art.

<span class="mw-page-title-main">Mark Nigrini</span>

Mark J. Nigrini, born in Cape Town, South Africa, is an Associate Professor of Accounting at the College of Business and Economics at West Virginia University in Morgantown, West Virginia.

A social bot, also described as a social AI or social algorithm, is a software agent that communicates autonomously on social media. The messages it distributes can be simple and operate in groups and various configurations with partial human control (hybrid) via algorithm. Social bots can also use artificial intelligence and machine learning to express messages in more natural human dialogue.

References

  1. ↑ Stewart, Charles (2011). "Voting Technologies". Annual Review of Political Science. 14: 353–378. doi: 10.1146/annurev.polisci.12.053007.145205 .
  2. 1 2 3 4 Hicken, Allen; Mebane, Walter R. (2017). A Guide to Elections Forensics (PDF) (Report). University of Michigan Center for Political Studies.
  3. ↑ Mebane, Walter Jr (2006). Election Forensics: The Second-digit Benford's Law Test and Recent American Presidential Elections (PDF) (Report). Cornell.
  4. 1 2 Klimek, Peter; Yegorov, Yuri; Hanel, Rudolf; Thurner, Stefan (2012-10-09). "Statistical detection of systematic election irregularities". Proceedings of the National Academy of Sciences. 109 (41): 16469–16473. arXiv: 1201.3087 . Bibcode:2012PNAS..10916469K. doi: 10.1073/pnas.1210722109 . ISSN   0027-8424. PMC   3478593 . PMID   23010929.
  5. ↑ Deckert, Joseph; Myagkov, Mikhail; Ordeshook, Peter C. (2011). "Benford's Law and the Detection of Election Fraud". Political Analysis. 19 (3): 245–268. doi: 10.1093/pan/mpr014 . ISSN   1047-1987. JSTOR   23011436.
  6. ↑ Zhang, Mali; Alvarez, R. Michael; Levin, Ines (2019-10-31). "Election forensics: Using machine learning and synthetic data for possible election anomaly detection". PLOS ONE. 14 (10): e0223950. Bibcode:2019PLoSO..1423950Z. doi: 10.1371/journal.pone.0223950 . ISSN   1932-6203. PMC   6822750 . PMID   31671106.
  7. ↑ Noonan, David (30 October 2018). "What Does a Crooked Election Look Like?". Scientific American. Retrieved 10 August 2020.
  8. ↑ "Notes on Election Forensics, Exit Polls, and Baseline Validation". CODE RED-Computerized Election Theft. 2018-08-08. Retrieved 2020-11-28.
  9. ↑ Kobak, Dmitry; Shpilkin, Sergey; Pshenichnikov, Maxim S. (March 2016). "Integer percentages as electoral falsification fingerprints". Annals of Applied Statistics. 10 (1): 54–73. arXiv: 1410.6059 . doi: 10.1214/16-AOAS904 . ISSN   1932-6157.
  10. ↑ "The forensics of election fraud: Russia and Ukraine | Request PDF". ResearchGate. Retrieved 2020-11-28.
  11. ↑ Ketchley, Neil (2019-10-03). "Fraud in the 2018 Egyptian presidential election?". Mediterranean Politics. 26: 117–129. doi:10.1080/13629395.2019.1673634. hdl: 10852/75493 . ISSN   1362-9395. S2CID   211466789.
  12. ↑ Simon, Jonathan. "Believe It (Or Not): The Massachusetts Special Election For US Senate" (PDF). Code Red 2014.
  13. ↑ "Election Fraud or Strategic Voting? Can Second-digit Tests Tell the Difference?". ResearchGate. Retrieved 2021-04-22.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.