Deep Deception: The story of the spycop network, by the women who uncovered the shocking truth

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Deep Learning is a kind of Machine Learning that represents knowledge as a hierarchical structure, building complex and specific representations over simpler and broader ones [ 20].

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The source of data is essential because Machine Learning is highly dependent on the quality and quantity of input data. To reduce bias, the data samples used as input for Machine Learning algorithms must represent the population as closely as possible. Statistical details can be found in section 7.2 (Dataset origin analysis) in S6 File (Statistical Analysis Notebook). The need for better ways to detect deceptions stimulated the creation of aiding technology to increase the detection accuracy. The most famous example is the polygraph, introduced in the Berkeley Police Department by John Larson [ 17], in 1921. The current polygraph models can monitor several physiological responses from a subject and require a preliminary calibration step to establish a baseline for the operator. Support Vector Machines (SVM) was the second most prevalent technique across all studies (28 times, 25.69%), and was mostly used with what is called a Linear kernel (23 times, 82.14%). The other choice was a Radial Basis Function (RBF) kernel (5 times, 17.86%). It’s clear that Mary’s is not a historical case. Often, our concerns about abusive covert policing practices are dismissed as a thing of the past. We’re told that cases from the 1970s and 80s, as evidenced in the undercover policing inquiry, happened in another era when attitudes were different, and policing didn’t have the rigorous oversight and management it does now. According to the report, however, Mary discovered the cover-up in 2020. Regarding linguistic cues, one article presents a comprehensive study comparing five languages from different parts of the world [ 51]. Structural differences demonstrate the need for specific approaches for each language or, at least, a group of similar languages.

Four of the top five most exploited Machine Learning techniques present variations (see section 5.2 in S6 File). The variation choice gives an idea of how the authors understand the problem, how complex they expected it to be, and what are their hypotheses regarding the data. In addition, other scientific sources such as Google Scholar, Semantic Scholar, and Connected Papers were not queried for the same reasons above. They could have provided other papers to complement the current corpus. LSTM model performances were measured by accuracy in 5 [ 38, 50, 53, 65, 81] out the 9 studies. Those accuracies range from 0.7487 to 1.0, with a mean at 0.8886 ± 0.0965. In two cases [ 106, 110] the measure was F1-score, as 0.6390 and 0.6562. In one study [ 101] the performance was reported as Area Under the Curve, which measured 0.6650, and in the other [ 82] the Unweighted Average Recall measured 0.7471. From a quantitative perspective, we gathered a rich set of metadata to produce many charts and tables. Those numerically and objectively describe all the papers in the selected corpus ( S5 and S6 Files).

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a) What are the best performing Machine Learning techniques applied to automatic deception detection? The Machine Learning techniques that best performed were Decision Trees, Gradient Boosting, Neural Networks, Multi-view learning, Random Forest, and Support Vector Machines (SVM). This finding works as a reasonable explanation for the variety of experiment results. While there are several reliable deception clues, exceptions exist because they may suffer from certain interferences, particularly the so-called Othello error [ 2]. The Othello error occurs when lie-catchers confuse emotions and motivations. The emotion is present, but it does not originate from deception.Nonlinear kernels are used when a linear solution is not possible. When working with RBF kernels (also called Gaussian kernels), the feature space is distorted to a higher-dimensional space where a hyperplane can be used to separate it [ 124].

As a ‘spy cops’ victim, I thought the police couldn’t sink

Lying is a frequent and pervasive social phenomenon [ 3]. While some forms may be accepted as a “social lubricant” [ 4], others are socially harmful. Telling (and being told) lies is frequent but perceiving them is a major challenge for most people. The average person has a lie detection rate around 54% [ 5, 6], rarely reaching 60%, and sometimes falling below 50% [ 7]. Institutional sexism or misogyny has recently become a term more widely recognised in connection with policing. Over the last year, since the murder of Sarah Everard by a serving Metropolitan police officer, we have seen a stream of revelations of sexual misconduct within the Met and more widely. The recently published IOPC report, Operation Hotton dealing with communications and activity from a group of officers at Charing Cross police station, exposed horrifying misogyny as well as racism and homophobia. Interestingly, one example provided in that report of the misogyny was the term, ‘weary’, used in relation to female police officers. This was a secret derogatory term used to describe female activists widely used by the Special Demonstration Squad, the secret unit within special branch which produced most of the offending undercover police officers. Statistical analysis shows that Monomodal approaches achieved high-performance levels, especially considering that Monomodal studies constitute the majority of research on the topic (33 out of 81 studies, 40.74%). However, a deeper look into such data reveals the presence of outliers (see section 8 in S6 File). Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration. Ann Intern Med. 2019;170(1):W1–33. pmid:30596876The institutional misogyny and racism of the undercover units being investigated by the UCPI (set up in 2015) is part of a far bigger, national picture. With its investigations concentrating on two “elite” units in special branch, the UCPI’s focus is on the officers who spied on people involved in political protest and campaigning. But Mary’s case shifts the narrative. If it happened to Mary, it could happen to anyone. How many more women are there like her? Women who are manipulated, deceived, violated and then silenced by the authorities? Women like you, your sister, or your friend perhaps? The story of what happened to these women and how they battled for truth, justice and accountability over the following few years is told by five of the women in the forthcoming publication of their book, ‘Deep Deception’. The process of working together, sharing their stories, overcoming fundamental differences of approach and politics was intense, emotional, frustrating but ultimately deeply rewarding as an act of extraordinary solidarity and one which held the police to account and forced from them an historic public apology. It was through that process of sharing their experiences and identifying the patterns of abuse and deceit, that the women began to name what had been done to them and the culture that enabled it to happen. They described it as ‘institutional sexism’. You can also read all about the movements of Mark Kennedy around Germany and elsewhere: https://powerbase.info/index.php/Mark_Kennedy:_A_chronology_of_his_activities From the qualitative perspective, we interpreted the statistical findings according to some theoretical frameworks on deception detection [ 2, 4, 5]. We discuss how the authors’ approaches align to those frameworks, where they agree and don’t, and what is still to be done. All those comments can be found in the “Discussion” section.