BS Summary: This article contains 21 faulty reasoning types, including Appeal to Authority, Indoctrination, and Framing Effect, with Biased Writer Voice as the most egregious example at 46.6% saturation with 95 hits. Analysis detected 767 faulty-reasoning hits from 204 analyzed words, generating a BS Score of 77.3% and a BS Rank of 85% (3,132 of 20,406 articles). This article is worse (more manipulative) than 84.70% of the article peer group.

Over the past two decades, U.S. security assistance has dramatically increased, with mixed results. 
The United States’ failure to build effective partner security forces in places like Afghanistan and Mali has raised questions about the efficacy of U.S. security assistance approaches. 
Yet, when done right, security assistance to key partners can allow the United States to advance central national security goals, as recently seen in Ukraine. 
Within a rapidly changing strategic context, what is the future of security assistance? 
How can the United States reform its security assistance approaches to increase its return on investment, especially in theaters described as lower priority by the Trump administration like Africa? 
On July 20, join the Strobe Talbott Center for Security, Strategy, and Technology at Brookings for a discussion on this topic with Rep. 
Sara Jacobs (D-Calif.), ranking member of the House Foreign Affairs Subcommittee on Africa. 
Brookings experts Dafna H. 
Rand, the former director of foreign assistance at the Department of State, and Alexander Noyes, the coauthor of “War at Arm’s Length: How America Can Build Effective Partners Through Military Assistance,” will moderate this event. 
Viewers can submit questions via e-mail to <a href="/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="1d786b7873696e5d7f6f72727674737a6e33787968">[email&#160;protected]</a> or via Twitter at <strong>#SecurityAssistanceReform</strong>. 
Article reasoning-pattern comparisonThis article: 14.2%Brookings: 4.2%Confirmation Bias14.2%This article: 0.0%Brookings: 0.8%Anchoring Bias0.0%This article: 13.2%Brookings: 2.6%Availability Heuristic13.2%This article: 0.0%Brookings: 0.8%Representativeness Heuristic0.0%This article: 0.0%Brookings: 0.7%Hindsight Bias0.0%This article: 0.0%Brookings: 2.6%Overconfidence Bias0.0%This article: 23.0%Brookings: 5.2%Framing Effect23.0%This article: 0.0%Brookings: 0.4%Loss Aversion0.0%This article: 14.2%Brookings: 0.6%Status Quo Bias14.2%This article: 0.0%Brookings: 0.1%Sunk Cost Effect0.0%This article: 12.3%Brookings: 3.0%Optimism Bias12.3%This article: 0.0%Brookings: 1.8%Pessimism Bias0.0%This article: 20.1%Brookings: 5.5%Negativity Bias20.1%This article: 0.0%Brookings: 0.7%Self-Serving Bias0.0%This article: 13.2%Brookings: 0.7%Fundamental Attribution Error13.2%This article: 0.0%Brookings: 0.2%Actor-Observer Bias0.0%This article: 0.0%Brookings: 0.7%In-Group Bias0.0%This article: 0.0%Brookings: 0.4%Out-Group Homogeneity Bias0.0%This article: 12.3%Brookings: 1.3%Halo Effect12.3%This article: 0.0%Brookings: 0.0%Horn Effect0.0%This article: 0.0%Brookings: 0.0%Dunning-Kruger Effect0.0%This article: 19.1%Brookings: 1.2%Recency Bias19.1%This article: 17.2%Brookings: 0.3%Primacy Effect17.2%This article: 0.0%Brookings: 0.1%Blind-Spot Bias0.0%This article: 0.0%Brookings: 0.2%Ad Hominem0.0%This article: 0.0%Brookings: 0.2%Straw Man0.0%This article: 23.5%Brookings: 3.9%Appeal to Authority23.5%This article: 14.2%Brookings: 1.8%False Dilemma14.2%This article: 0.0%Brookings: 1.4%Slippery Slope0.0%This article: 0.0%Brookings: 0.2%Circular Reasoning0.0%This article: 13.2%Brookings: 5.3%Hasty Generalization13.2%This article: 0.0%Brookings: 0.1%Red Herring0.0%This article: 0.0%Brookings: 0.3%Bandwagon0.0%This article: 0.0%Brookings: 2.2%Appeal to Emotion0.0%This article: 0.0%Brookings: 0.8%Begging the Question0.0%This article: 0.0%Brookings: 3.8%Post Hoc (False Cause)0.0%This article: 0.0%Brookings: 0.0%Tu Quoque0.0%This article: 0.0%Brookings: 0.2%Burden of Proof0.0%This article: 0.0%Brookings: 0.2%Appeal to Nature0.0%This article: 0.0%Brookings: 0.3%Composition/Division0.0%This article: 12.3%Brookings: 1.5%Anecdotal12.3%This article: 12.3%Brookings: 0.1%No True Scotsman12.3%This article: 21.1%Brookings: 1.7%Ambiguity (Equivocation)21.1%This article: 0.0%Brookings: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Brookings: 0.1%Middle Ground0.0%This article: 0.0%Brookings: 0.0%Personal Incredulity0.0%This article: 0.0%Brookings: 0.1%Special Pleading0.0%This article: 0.0%Brookings: 0.0%Genetic Fallacy0.0%This article: 17.2%Brookings: 1.1%Unattributed Quote17.2%This article: 0.0%Brookings: 0.3%Quote-first Misdirection0.0%This article: 46.6%Brookings: 2.4%Biased Writer Voice46.6%This article: 23.5%Brookings: 2.4%Indoctrination23.5%This article: 0.0%Brookings: 1.2%Politically Left Leaning Bias0.0%This article: 14.2%Brookings: 0.2%Politically Right Leaning Bias14.2%This article: 19.1%Brookings: 0.8%Attempt to Sell a Product or S…19.1%

204 words analyzed.

Speakers

2speakers25%attributed speech152writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 5 words • 0.0% coverageWriter's voice • 14 words • 100.0% coverageWriter's voice • 27 words • 100.0% coverageWriter's voice • 25 words • 100.0% coverageWriter's voice • 13 words • 100.0% coverageWriter's voice • 29 words • 100.0% coverageWriter's voice • 23 words • 100.0% coverageSara Jacobs (D-Calif.) • 13 words • 0.0% coverageBrookings experts Dafna H. • 4 words • 0.0% coverageBrookings experts Dafna H. • 35 words • 100.0% coverageWriter's voice • 16 words • 100.0% coverage
90%flagged-word coverage
39 attributed words75% of attributed speech100% writer coverage
0%45.0%90.0%Unattributed Quote+89.7 ptsWriter: 0.0%Brookings experts Dafna H.: 89.7%89.7%Indoctrination+81.2 ptsWriter: 8.6%Brookings experts Dafna H.: 89.7%89.7%Biased Writer Voice-62.5 ptsWriter: 62.5%Brookings experts Dafna H.: 0.0%0.0%Attempt to Sell a Product -25.7 ptsWriter: 25.7%Brookings experts Dafna H.: 0.0%0.0%Politically Right Leaning -19.1 ptsWriter: 19.1%Brookings experts Dafna H.: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.