What approaches can an analyst use to reduce cognitive and perceptual biases?
- Provide evidence from the weekly readings to support your arguments via APA parenthetical citations.
- Other sources (if used at all) must be subordinate to your understanding of the readings presented in the class.
Articles for this weeks assignment:
Intelligence Analysis: A Guide to its Study: https://www.afio.com/publications/Guide/index.html… (pages 291-296)
Structured analytic techniques for improving intelligence analysis: https://www.cia.gov/library/center-for-the-study-o… (pages 1-39)
Psychology of intelligence analysis https://www.cia.gov/library/center-for-the-study-o… (Chapters 8 & 14)
I look forward to reading your posts and providing feedback on your topics!
Instructions: Your initial post should be at least 350 words.
Please respond to at least 2 other students. Responses should be a minimum of 250 words to each student and include direct questions.
Student #1 Kyle
When processing new information, we naturally align it with our already formed opinions and notions. If this information aligns well, we tend to accept it and remember it. If it does not align well, we tend to fight it, discredit it, and refuse to accept it. Nowhere is this more prevalent and obvious than in our citizen-government relationship. During the Presidential campaign many people developed a hatred for the Candidate Trump. Now that he is President, many people refuse to accept that somethings are going well. Conversely those that love President Trump, refuse to see any wrong doing. I think this is a natural human response. However, there is no room for this type of information interpretation in the field of intelligence analysis. This does not mean the human tendency is not there, it just means that some procedures need to be forced into the process to identify and eliminate cognitive and perceptual biases. One of those techniques is the â€œKey Assumptions Checkâ€. The idea behind this technique is that intelligence analyst may have preconceived assumptions that they believe to be true, as they analyze knew intelligence, these assumptions may factor into the analysis. This is problematic, especially if the assumptions are incorrect (US Government, 2009). Another procedure that I believe is particularly important, because Iâ€™ve noticed myself doing this in my own school work, is â€œAnalysis of Competing Hypothesisâ€. Essentially, as analysists assess information, they may have a tendency to cling to the first bit of information that supports their hypothesis. This can obviously be problematic as it may cause the analyst to overlook information that could be critical. This procedure involves a group of analysts list all possible hypothesisâ€™ prior to reviewing the information. Once listed, the information can then be attached to the hypothesis that it matches (US Government, 2009). There is also the â€œDevilâ€™s Advocacyâ€ technique. Here an analyst would try and support a theory opposite of their own with the same information (US Government, 2009). Itâ€™s easy to see how this approach would be effective as your forced to work for the other side.
US Government. (2009). A Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis. Washington DC.
Student #2 Brandon
Good Morning Class,
This week we delve into the intelligence analysis phase of the intelligence cycle. For this weekâ€™s forum discussion, we must find the various approaches that an analyst can reduce cognitive and perceptual biases. One of the most critical aspects that analysts must adhere to is the end-user requirements to properly produce useable intelligence. When we use a predicate of a neutral ground for intelligence products, intel analysts must fill in these gaps with the evaluation of evidence, estimating probabilities, and perceiving causality.
When evaluating evidence, it is important for analysts to sort through large amounts of information that can be gaudy and personal with that can take a precedented toll on an analyst. One real-time experience I can provide is the analysis of accident/incident cameras that are installed in the oil trucks. Daily, I must gather information between camera functionalities and among the analysis, I some peculiar instances that can get an employee fired. Some of which, are personable human beings that have no intentions to cause harm and an excellent worker, but I must gather the information of any discrepancies and report back to the safety manager. Regardless of having a qualm of an employee or favoritism, I must set all emotions aside and make sure that all analytic reportingâ€™s must be unbiased upon completion of analysis.
When establishing probabilities through biases, we must formulate base rate fallacies, comparison of failure rates, calculating accuracy in original cities, example where many are terrorists, or the small proportion of targets raises failure rates. The use of statistics among analyzing anything, can be a biased. One example of example where many are terrorists, if we investigate a low-income community that has a crime rate of 45%, and the demographic scenario is as follows; White Americanâ€™s 23%, Black Americanâ€™s 22%, Hispanic 21%, Asian 9%, bi-racial 25%, and the amount of crimes are predominantly based by the White American population of the neighborhood, statistically states that the reason why most of crime is done by White Americanâ€™s because of the percentage of them within the community. However, 90% of all violent crimes are done by bi-racial Americanâ€™s, what would this suggest? How would you take an approach to analysis this scenario?
When we investigate the biases of perception of cause and effect, is a qualitive approach to connecting themes and patterns. One of the common misconceptions of the perception of cause and effect is the illusions and false correlations. Ironically, this may seem like the easiest way to provide biased intelligence since â€œit is in perceiving that a relationship exists when one does not.â€ (APUS, N/A) When I read more into the illusory correlation, I began to think, do I honestly use biases for several scenarios.
The key to providing a non-bias analysis is ruling out the misconception of cause and effect and providing different qualitive angles when assessing the collected intelligence. Incorporating abstract information and vivid information for analyzing intelligence provides a headstrong aspect of handling information first-hand, try to use qualitive intelligence that contains rich information, while getting the objective completed, and remain confident when handling the evidence. If there are any uncertainties of intelligence, ask necessary probing questions to gain a solid understanding of the intelligence you are analyzing.
I hope everyone has a great rest of their week! I do have great news, I have accepted an internship at a cybersecurity-intelligence company and Iâ€™m excited to learn more about the technical aspects of the Intelligence cycle when analyzing threats and mitigating risk!
APUS. (N/A). Lesson 6: Analysis and Production. Retrieved from https://apus.realizeithome.com/RealizeitApp/ContentDelivery.aspx?Token=VQM4aHASPwiyNgXJ%2b00mD1A7x5IQ%2bXU0lZVdc7b8ZG5W790wsfQUUCUnzuq2HtqdJcD%2birWaHT4XcR6PolcrDBnLaaj6Tokw9tcQYifirsYnMbhqCJC%2f08oyNQzQib5AaUsM4uS2mzgtYh5dmVee3iFumAINhJSIFPZjvL1KiE2RUnmMPDPsylsYo%2b3FV%2fVlUPnJMBJpQgcBsbUFH7%2fDyAvNIrRtVgxLWZNJfuO2zLR6BlgrIWEpipcCR7txhnvk%2b9GhN1CWGcgpx63hAqWx%2fJaavwgyHL8snx%2bHhYPoHpy5%2bK5W6MTTd7MneW9VPRMUVlmHHLUYJ%2fWjReePPwuUYux%2fmEHeRMf5%2bP4auZw18JLjCSRynEg8CDeSppV1%2bQMmuGwooxMA3jFejkC8%2f5%2fr24xRjUPgB2okViYrHpd6U50%3d.