[Annotated] How data-driven policing threatens human freedom

An excerpt^[각주:1] and interview with Andrew Ferguson, author of "The Rise of Big Data Policing^[각주:2]"

"Minority Report", a 2002 film directed by Steven Spielberg, features a squad of police officers who arrest people for murders^[각주:3] they are predicted to commit^[각주:4]. The film was science fiction; yet police departments around the world increasingly use predictive analytics to identify^[각주:5] people who might become perpetrators^[각주:6] or victims of crime. In "The Rise of Big Data Policing", Andrew Ferguson, a former public defender^[각주:7] and now professor at the University of the District of Columbia, discusses the promise and perils of^[각주:8] data-driven policing^[각주:9]. 

The Economist asked him about how data and predictive analytics are changing modern policing. After his responses, you can read an excerpt from his book that shows what data-driven policing looks like on the ground. 

The Economist: Police have always used data to make decisions. What makes this era different?

Andrew Ferguson: Policing has traditionally been reactive^[각주:10]: officers respond to calls for service, and experience determines where they patrol^[각주:11]. Big-data technology lets police become aggressively more proactive^[각주:12]. New data sources now allow police to visualize crime differently^[각주:13], targeting individual blocks, at-risk individuals^[각주:14] and gangs in innovative ways. New surveillance technologies let police map physical movements^[각주:15], digital communications and suspicious associations in ways that^[각주:16] can reveal previously hidden patterns of criminal activity in otherwise overwhelming amounts of data. All of this information can be quite useful to law enforcement^[각주:17] seeking to track criminal elements in society^[각주:18]. The same technology can also be quite threatening to civil liberties^[각주:19] and personal privacy in already over-policed communities^[각주:20]. 

The Economist: How pervasive is^[각주:21] the use of tech in policing - how different is the day-to-day work of^[각주:22] police officers today as opposed to 20 or 30 years ago?

Mr Ferguson: Technology is shaping where police patrol, whom they target, and how they investigate crime. More than 60 American police departments use some form of "predictive policing" to guide their day-to-day operations^[각주:23]. In Los Angeles, this means that police follow patrols based on computer-forecast crime hot-spots^[각주:24]. In Chicago, an algorithmically derived^[각주:25] "heat list" ranks people at risk of becoming victims or perpetrators of gun violence. The result is that police prioritize particular places and people for additional contacts and monitoring. In addition, new surveillance technologies - including police body cameras, automated^[각주:26] licence-plate readers^[각주:27], Stingray cell phone trackers and high-definition surveillance cameras^[각주:28] - provide powerful monitoring tools. All of this technology changes how officers see the communities they patrol and the citizens they police. The technology also changes the job of policing, forcing officers to become data collectors and analysts as they act on real-time inputs^[각주:29] and assessments. 

The Economist: Does big-data policing work? Has it made people less likely to be victims of crime?

Mr Ferguson: The jury is still out on effectiveness. The scientific studies are few in number^[각주:30] and largely inconclusive^[각주:31]. In some cities crime rates have trended down with the introduction of^[각주:32] new technologies, but in others there has been no significant effect. Crime rates correlate with a host of^[각주:33] economic and environmental forces that make it difficult to demonstrate any casual connection with a specific technology. But really, the benefit of big-data policing for police departments is political. New technology gives police chief^[각주:34] an answer to the age-old question asked by^[각주:35] every politician in every community forum^[각주:36]: "Chief, what are you doing about crime?" They now have a progressive-sounding^[각주:37], technologically inspired answer^[각주:38]: "We are using a new black-box technology to predict and deter crime^[각주:39]." Whether it works is secondary to^[각주:40] having a response to^[각주:41] the otherwise^[각주:42] unanswerable^[각주:43] (and somewhat unfair) question that every police chief faces. 

The Economist: What are the biggest potentials for^[각주:44] abuse?

Mr Ferguson: There are several. First, data can distort policing. Officers sent to an area flagged as being^[각주:45] at risk of violent crime may see routine encounters as^[각주:46] more threatening, thus making them more likely to use force. Second, the growing web of surveillance threatens to chill associational freedoms^[각주:47], political expression and expectations of privacy by eroding^[각주:48] public anonymity^[각주:49]. Third, even with the best use policies in place^[각주:50], officers have access to vast amounts of^[각주:51] personal information of people not suspected of any crime^[각주:52]. Finally, without carefully chosen data inputs, long-standing^[각주:53] racial, societal^[각주:54] and other forms of bias will be reified in the data^[각주:55]. 

The Economist: How can citizens best protect themselves from such abuse?

Mr Ferguson: The time to respond to the threat of big-data policing is now. Every city should have formal written policies in place detailing the approved use of^[각주:56] new big-data policing technologies. Every citizen should be educated about the dangers to privacy, liberty and the imbalance of power that surveillance technologies bring. Every police department should engage impacted communities about^[각주:57] the risks and rewards of new predictive technologies with official answers to concerns about^[각주:58] transparency^[각주:59], racial bias and constitutional rights^[각주:60]. Every community should host annual "surveillance summits" where^[각주:61] the city officials, engaged citizens and police chiefs can come together for a moment of public accountability about the use^[각주:62] and potential misuse of^[각주:63] new big-data technologies. Education, empowerment^[각주:64], and engagement are the only protections against an encroaching data-driven surveillance state^[각주:65]. 

The Violence Virus 

An excerpt from "The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement" 

A knock on an apartment door. A man gives the prognosis to^[각주:66] a worried mother^[각주:67]. Your son might die. He is at grave risk^[각주:68]. Others he knows have already succumbed^[각주:69]. An algorithm has identified the most likely to be stricken^[각주:70]. He is one of a few hundred young men (approximately 0.048% of the city) who may die. In Chicago, Illinois, this scene has played out hundreds of times at hundreds of doors^[각주:71]. The danger, however, is not some blood-borne^[각주:72] pathogen^[각주:73]. This is not a doctor giving a cancer diagnosis but a police detective giving a life diagnosis. Violence is contagious^[각주:74], and you are exposed. As a young man in Chicago, due to your friends, associates^[각주:75], and prior connection to violence^[각주:76], you have been predicted to be the victim or perpetrator of a shooting. Your name is on the "Strategic Suspects List," also known as the "heat list," and a detective is at your door with a social worker^[각주:77] and a community representative to tell you the future is not only dark but deadly^[각주:78]. A vaccine exists, but it means turning your life around now^[각주:79]. 

In Chicago, 1,400 young men have been identified through big data techniques as targets for the heat list^[각주:80]. Software generates a rank-order list of potential victims and subjects with the greatest risk of violence. In New Orleans, Palantir has partnered with^[각주:81] the mayor's office to identify the 1% of violent crime drivers in^[각주:82] the city. In Rochester, New York, and Los Angeles, similar techniques are being used to identify juveniles who^[각주:83] might be involved in repeated delinquent activity^[각주:84]. This is the promise of big data policing. What if big data techniques could predict who might be violent? What if a policing system could be redesigned to target those who are at-risk in a neighborhood before the shooting occurs? This is the theory behind "person-based targeted policing."

Person-based predictive policing involves the use of data to identify and investigate potential suspects or victims. Part public health approach to violence and part^[각주:85] social network approach to risk assessment^[각주:86], big data can visualize how violence spreads like a virus among communities. The same data also can predict the most likely victims of violence. Police data is shaping who gets targeted and forecasting who gets shot. 

While these predictive technologies are excitingly new^[각주:87], the concerns underlying them^[각주:88] remain frustratingly^[각주:89] old-fashioned. Fears of racial bias, a lack of transparency, data error and the distortions of constitutional protections offer serious challenges to the development of workable^[각주:90] person-based predictive strategies. Yet person-based policing systems are being used now, and people are being targeted. 

***

There are four main ways in which data and predictive analytics fundamentally change how police in liberal societies operate^[각주:91]. 

First, big data makes police more proactive^[각주:92]. Traditionally, officers might react to calls for service^[각주:93], rely on observations made^[각주:94] while on patrol^[각주:95], or respond to community complaints. With person-based predictive targeting, police can instead target suspects for surveillance or deterrence before a call comes in. For local prosecutors, this represents a significant change. As a former head of the Manhattan Criminal Strategies Unit stated^[각주:96], "It used to be we only went where the cases took us. Now, we can build cases around^[각주:97] specific crime problems that communities are grappling with^[각주:98]."

Second, seeing violence as a public-health problem, rather than just a law-enforcement problem, lets societies rethink how best to identify^[각주:99] and respond to criminal risk. Violence-reduction strategies in New Orleans, for instance, included social-service programmes. The idea that violence is contagious suggests that it can be prevented. If a good percentage of shootings are retaliatory^[각주:100], then one can design a cure that interrupts the cycle. Every time police summon people whom predictive analytics have identified as potential perpetrators^[각주:101] or victims of violence, social-services representatives should be there, ready to offer those young men and women the opportunity to change their environment. 

Third, moving from traditional policing to intelligence-led policing creates data-quality risks that need to be systematically addressed^[각주:102]. Intelligence-driven systems work off many bits of local intelligence^[각주:103]. Tips, crime statistics, cooperating witnesses, nicknames, and detective notes can get aggregated into a large data system^[각주:104]. But the quality of that data is not uniform^[각주:105]. Some tips are accurate; some are not. Some biases will generate suspicion^[각주:106], and some informants^[각주:107] will just be wrong. An intelligence-driven policing or prosecution system that does not account for^[각주:108] the varying^[각주:109] reliability^[각주:110] and credibility of sources^[각주:111] - and just lumps them all together as "data"^[각주:112] - will ultimately result in an error-filled^[각주:113] database. Especially when these systems are used to target citizens for arrest or prosecution, the quality-control measures of^[각주:114] black-box algorithms must be strong. 

Fourth, other data-integrity^[각주:115] concerns^[각주:116] may arise when^[각주:117] detectives, gang experts, or police intelligence officers control the target lists. While these professionals generally have close connections to the community and valuable knowledge of local gangs and potential targets, risk scores^[각주:118] can be manipulated by police interested in prosecuting certain individuals^[각주:119]. People can get added to the target lists - which are often riddled with errors^[각주:120] - with no way to challenge or change their designation^[각주:121]. After all, joining a gang is rarely a formal process; rumour, assumptions^[각주:122] or suspicion^[각주:123] can be enough to earn an elevated risk score^[각주:124]. Worse, there is usually no easy way to get off the list^[각주:125], even as people's circumstances change^[각주:126], time passes, and the data grows stale^[각주:127]. The people on these lists, and most impacted by these risks, are primarily young men of color. This reality raises serious constitutional concerns^[각주:128] and threatens to delegitimize^[각주:129] person-based predictive policing strategies. 

1. excerpt ; [명사] ~ (from sth) (글・음악・영화 등의) 발췌[인용] (부분) ;; 미국식 [|eksɜ:rpt] 영국식 [|eksɜ:pt] [본문으로]
2. policing ; 2. (산업체 등의 규칙 준수를 위한) 감시 활동 [본문으로]
3. arrest (sb) for ; …의 죄로 체포하다. [본문으로]
4. be predicted to ; ~할 것으로 예상되다 [본문으로]
5. predictive analytics ; 예측 분석 [본문으로]
6. perpetrator ; [명사] (범행・과실・악행을 저지른) 가해자[범인] ;; 미국식 [|pɜ:rpətreɪtə(r)] 영국식 [|pɜ:pətreɪtə(r)] [본문으로]
7. public defender ; [명사] (법률) (미국에서) 국선 변호인 ;; 국선(公選) 변호인(가난한 사람들을 위해 공공 비용으로 일하는 변호사)(=a lawyer who represents poor people at public expense). [본문으로]
8. peril ; 2. [C] [주로 복수로] ~ (of sth) 위험성, 유해함 ;; 미국∙영국 [|perəl] [본문으로]
9. data-driven ; (컴퓨터) <프로그램이> 데이터에 따라 처리를 하는 [본문으로]
10. reactive ; 1. (격식) 반응[반작용]을 보이는 ;; 참고 ; proactive [본문으로]
11. patrol ; [타동사] (~led; ~·ling) 1. <지역을> 순찰[순시, 순회]하다 ;; [자동사] 순찰[순시]하다 [본문으로]
12. proactive ; [형용사] 사람・정책이 상황을 앞서서 주도하는, 사전 대책을 강구하는 ;; 참고 ; reactive [본문으로]
13. visualize ; 2. 마음 속에 선하게 떠오르게 하다, 상상하다 ((as)) ;; 3. 예상하다, 예견하다 [본문으로]
14. at-risk ; [형용사] (명사 앞에만 씀) 사람이나 집단이 (특히 가정에서) 위험한 환경에 있는 [본문으로]
15. map ; 2. 〔지도 위에〕 나타내다. ;; 3. …을 면밀히 계획하다, 배치하다(out). [본문으로]
16. association ; 2. [C , U] ~ (with sb/sth) 연계, 유대, 제휴 ;; 3. [C] [주로 복수로] 연관; 연상 ;; 4. [C] 연관성 [본문으로]
17. law enforcement ; [명사] 법 집행[치안유지]에 종사하는(경찰·검찰 기타 각종범죄 단속에 종사하는 기관·사람의 총칭으로 사용, 약간 딱딱한 말투) [본문으로]
18. criminal element ; 범죄 분자 [본문으로]
19. civil liberty ; [명사] (주로 복수로) 시민적 자유(법의 한도 내에서 말하고 행동할 자유) [본문으로]
20. overpoliced ; Excessively policed. [본문으로]
21. pervasive ; [형용사] 만연하는, (구석구석) 스며[배어]드는 ;; 퍼지는, 보급하는 ;; permeative [본문으로]
22. day-to-day ; [명사 앞에만 씀] 1. (장기적으로 계획하는 것이 아니라) 그날그날 꾸려 가는 ;; 2. (일이) 매일 행해지는, 그날그날의 [본문으로]
23. day-to-day operation ; [명사] 일상 업무(작업) [본문으로]
24. hot spot ; 1. (정치·군사적) 분쟁 지역 ;; 2. [구어] 위험한 장소; 곤란한 상태 ;; 3. 나이트클럽, 환락가 [본문으로]
25. derived ; 유래된, 파생된 [본문으로]
26. automated ; [형용사] 자동화된, 자동의 [본문으로]
27. licence plate ; 자동차 번호판, 차량 번호 표지판 [본문으로]
28. high-definition ; [명사 앞에만 씀] (전문 용어) 고화질[고선명도]의 [본문으로]
29. act on ; 2. [주의·명령 등]에 따라서 행동하다, 따르다 [본문으로]
30. in number ; 1. 합계, 모두 ;; 2. 숫자상으로 [본문으로]
31. inconclusive ; [형용사] (확고한 결정・결과에 이를 정도로) 결정적이 아닌, 결론에 이르지 못하는 [본문으로]
32. trend ; [자동사] 2. 어떤 방향으로 쏠리다, 기울다, …하는 추세[경향]이다 [본문으로]
33. correlate with ; ~ 와 관련 있다 [본문으로]
34. police chief ; 경찰서장 [본문으로]
35. age-old ; [형용사] (주로 명사 앞에 씀) 아주 오래된, 예로부터 전해 내려오는, 매우 낡은 [본문으로]
36. community forum ; (사회복지학) 지역사회포럼 [본문으로]
37. -sounding ; [COMB in ADJ] -sounding combines with adjectives to indicate a quality that a word, phrase, or name seems to have. [본문으로]
38. inspired ; [ADJ] aroused or guided by or as if aroused or guided by divine inspiration [본문으로]
39. deter[prevent] crime ; 범죄를 막다, 저지하다[예방하다] [본문으로]
40. be secondary to sth ; …에 버금가다, …보다 2차적이다, …의 다음인. [본문으로]
41. have a response ; 반응을 얻다. [본문으로]
42. otherwise ; [형용사] 2. [[A]] 그렇지 않았더라면 …인[일지도 모르는] [본문으로]
43. unanswerable ; 2. (질문이) 답이 없는[대답할 수 없는] [본문으로]
44. potential ; [U] 1. (종종 a potential) (…의) 가능성, 잠재(능)력[for]. [본문으로]
45. flag ; (-gg-) 1. [타동사][VN] (중요한 정보 옆에) 표시를 하다 [본문으로]
46. encounter ; [명사] 1. ~ (with sb/sth) | ~ (between A and B) (특히 예상 밖의・폭력적인) 만남[접촉/조우] [본문으로]
47. associational ; [형용사] 협회의, 사단의; 연상의, 연합의 ;; 참고 ; Freedom of association ; Freedom of association encompasses both an individual's right to join or leave groups voluntarily, the right of the group to take collective action to pursue the interests of its members, and the right of an association to accept or decline membership based on certain criteria [본문으로]
48. erode ; 2. (서서히) 약화시키다[무너뜨리다]; 약화되다[무너지다] [본문으로]
49. anonymity ; [U] 1. 익명(성) ;; 미국∙영국 [|ӕnə|nɪməti] [본문으로]
50. in place ; 2. 가동 중인; 가동할 준비가 되어 있는 [본문으로]
51. have access to ; …에게 접근[출입]할 수 있다, …을 면회할 수 있다 [본문으로]
52. be suspected of ; …의 혐의를 받다 [본문으로]
53. long-standing ; [형용사] 오랫동안[여러 해]에 걸친, 다년간의, 오래도록 계속되고 있는 [본문으로]
54. societal ; [형용사] 사회의 ;; 미국·영국 [sə|saɪətl] [본문으로]
55. reify ; 〔추상 개념 따위〕를 구체화하다, 구상화하다, 현실적[구체적]인 것으로서 보다[다루다]. ; materialize ;; 미국∙영국 [rí:əfài,réiə-] [본문으로]
56. approved ; [형용사] 인가된; 입증된, 정평 있는, 공인된 [본문으로]
57. impacted ; 1. (쐐기처럼) 꽉 끼인(wedged in) ; 죄어진, 빈틈없이 다져 넣은(closely packed). ;; 3-a. 인구가 조밀[과밀]한 ;; 3-b. <지역이> (인구 증가에 따라 공공 시설의 증설이 부득이하여) 재정적으로 애먹고 있는 ;; 4. 충돌된, 충격받은 [본문으로]
58. concern about[for, over, with] ; …에 대한 관심[염려]. [본문으로]
59. transparency ; 3. [U] (변명・거짓말 따위가) 속이 빤히 들여다보임[명백함] ;; 4. [U] (상황・주장 따위의) 명료성 [본문으로]
60. constitutional rights ; 헌법에 규정된 권리, 헌법상의 권리 [본문으로]
61. host ; [타동사] 2. (국제 회의 등에서) 주최국 노릇을 하다 [본문으로]
62. public accountability ; 공공 책임 [본문으로]
63. potential misuse ; 잠재적 오용 [본문으로]
64. empowerment ; [명사] 권한[기능] 부여[이양]; 〈경영〉 권한[기능] 분산, 권한[기능]의 하부 이양. [본문으로]
65. encroach ; [자동사] 1. (남의 나라·땅 등을) 잠식(蠶食)하다, 침략하다, 침입하다(intrude) ((on, upon)) ;; 2. (남의 권리 등을) 침해하다(infringe), (남의 시간을) 빼앗다 ((on, upon)) ;; 3. <바다가> 침식하다 ((on)) ;; 미국식 [ɪn|kroʊtʃ] 영국식 [ɪn|krəʊtʃ] [본문으로]
66. prognosis ; (pl. -ses[-siːz]) 1. (의학) 예후(豫後)(opp. diagnosis) ;; 2. [[UC/]] 예지, 예상, 예측 ;; 미국식 [prɑ:g|noʊsɪs] 영국식 [prɒg|nəʊsɪs] [본문으로]
67. worried ; [형용사] <표정 따위가> 걱정[근심]스러운, 괴로움받는; 당황[걱정, 안달]하는, 곤란한 듯한 ((about, over, that [절/] )) [본문으로]
68. grave[serious] risk ; 중대한 위험 [본문으로]
69. succumb ; [자동사] 1. (유혹 등에) 굴복하다, 압도당하다, 굽히다, 지다(give way) ((to)) ;; 2. (병·부상·노령 등으로) 쓰러지다, 죽다(die) ((to)) ;; 미국∙영국 [sə|kʌm] [본문으로]
70. stricken ; [etc.] Stricken is the past participle of some meanings of strike. ;; [ADJ] If a person or place is stricken by something such as an unpleasant feeling, an illness, or a natural disaster, they are severely affected by it. [본문으로]
71. door ; 3. 한 집, 1호(戶); 한 방 [본문으로]
72. blood-borne ; (medicine) Usually of a pathogen, carried in the bloodstream and other body fluids. [본문으로]
73. pathogen ; [명사] (전문 용어) 병원균, 병원체 ;; 미국∙영국 [|pӕθədƷən] [본문으로]
74. contagious ; 2. (동작 따위가) 옮기 쉬운, 퍼지기 쉬운, 영향을 미치는. [본문으로]
75. associate ; [명사] 1. (사업・직장) 동료 [본문으로]
76. prior ; 2. (…보다) 우선하는 [본문으로]
77. social worker ; [명사] 사회복지사 [본문으로]
78. deadly ; (dead・lier , dead・li・est), (more deadly와 deadliest가 일반적으로 쓰이는 형태이다. most deadly도 쓰이기는 한다.) 1. 생명을 앗아가는[앗아갈], 치명적인 [본문으로]
79. turn (sth) around ; 4. 변절하다[시키다]; 의견[태도]을 바꾸(게 하)다 [본문으로]
80. heat ; 8. [미·속어] 압력, 추적, 조사, 수사; (수사의) 강화;[the ~] 경찰; 경관 [본문으로]
81. partner with ; ~와 협력하다 [본문으로]
82. driver ; 4. 추진 요인, 동인(動因) [본문으로]
83. juvenile ; (격식 또는 법률) 청소년 ;; 미국식 [|dƷu:vənl] 영국식 [|dƷu:vənaɪl] [본문으로]
84. delinquent ; 1. (특히 청소년이나 그들의 행동이) 비행의, 범죄 성향을 보이는 [본문으로]
85. part ; [부사] [흔히 합성어에서] 반쯤; 어느 정도 [본문으로]
86. risk assessment ; 위험도 평가 [본문으로]
87. excitingly ; [부사] 자극적으로, 대단히 재미나게 [본문으로]
88. underlie ; (-lay[-léi]; -lain[-léin]; -ly·ing) [수동태로는 안 씀] (격식) 1. …의 아래에 있다[놓이다] ;; 2. …의 기초가 되다; …의 밑바닥에 잠재하다 [본문으로]
89. frustratingly ; [부사] 좌절감을 느낄 정도로. [본문으로]
90. workable ; 1. (시스템・아이디어 등이) 운용[실행] 가능한 [본문으로]
91. operate ; 4. OF BUSINESS/ORGANIZATION | [자동사][V] (사업체・기관 등이 특정한 방식으로 또는 특정한 곳에서) 영업[작업]하다 [본문으로]
92. proactive ; [형용사] 사람・정책이 상황을 앞서서 주도하는, 사전 대책을 강구하는 ;; 참고 ; reactive [본문으로]
93. react to ; ~에 반응하다, 대처하다 [본문으로]
94. observation ; 3. [pl.] 관측 보고 ((of)), (관측의) 결과, (관찰) 정보, 기록 [본문으로]
95. on patrol ; 순찰[초계] 근무 중 [본문으로]
96. state ; 1. (정식으로) 말하다[쓰다], 진술[서술/언명]하다 [본문으로]
97. build a case ; 소송을 내다[하다]. [본문으로]
98. grapple with ; to try to deal with a difficult situation or solve a difficult problem [본문으로]
99. rethink ; [자, 타동사] (re・thought , re・thought / -'TOːt /) (특히 계획・행동 방침 등을 변경하기 위해) 다시 생각하다[재고하다] [본문으로]
100. retaliatory ; [형용사] 보복적인, 앙갚음의, 복수심이 강한 ;; of retaliation [본문으로]
101. identify as ; …라고 밝히다. [본문으로]
102. address ; 5. ~ (yourself to) sth (격식) (문제・상황 등에 대해) 고심하다[다루다] [본문으로]
103. work off sth ; to use a supply of power or another machine in order to operate [본문으로]
104. aggregate ; [타동사][VN] [주로 수동태로] ~ sth (with sth) (격식 또는 전문 용어) 종합하다 [본문으로]
105. uniform ; [형용사] 획일적인, 균일한, 한결같은 [본문으로]
106. suspicion ; 1. [U , C] ~ (that…) (불법적이거나 부정직한 일을 했다는) 혐의[의혹] ;; 참고 ; [형용사] suspect [본문으로]
107. informant ; 1-a). 통지자, 보고자 ;; 1-b). 밀고자 ;; 2. 피조사자, 자료 제공자(그 지방 고유의 문화·언어 자료를 제공함) [본문으로]
108. account for ; 1. ~을 해명하다[~의 이유가 되다] ;; 2. 설명하다 ;; to explain how or why something happened; to be the explanation for something [본문으로]
109. varying ; [형용사] (연속적으로) 바뀌는, 변화하는; 가지각색의 [본문으로]
110. reliability ; [U] 신뢰할 수 있음, 믿음직함, 신뢰도, 확실성 [본문으로]
111. credibility ; [U] 믿을 수 있음, 진실성; 신용, 신빙성, 확실성 [본문으로]
112. lump ; [타동사] 1. 한 덩어리로 만들다; 일괄하다; (차이를 무시하고) 일률적으로 다루다 ((together, with, in with, under)) [본문으로]
113. -filled ; [접미사] (명사 뒤에서) …을 넣은; …으로 가득한. [본문으로]
114. quality control ; [명사] 품질 관리 [본문으로]
115. data-integrity ; (컴퓨터) 데이터 완전성 ((입력된 데이터가 변경·파괴되지 않은 상태)), 데이터 무결성 [본문으로]
116. concern ; 1. WORRY | [U , C] ~ (about/for/over sth/sb) | ~ (that…) (특히 많은 사람들이 공유하는) 우려[걱정] ;; 참고 ; unconcern [본문으로]
117. arise ; [자동사] (arose / ə'rəUz ; 美 ə'roUz / , arisen / ə'rIzn /), [v] 1. (비교적 격식) (특히 문제나 곤란한 상황이) 생기다, 발생하다 [본문으로]
118. score ; 2. POINTS/GOALS, etc. | [C] (특히 美) (테스트 등의) 점수[지수] [본문으로]
119. prosecute ; 1. ~ (sb) (for sth/doing sth) 기소[고발/소추]하다 [본문으로]
120. be riddled with sth ; (특히 나쁜 것이) 가득하다[–투성이이다] [본문으로]
121. designation ; 3. [문어] 명칭, 호칭; 칭호(title); (명칭 등의) 의미; [미] 자격(qualification) [본문으로]
122. assumption ; [UC/] 1. (증거도 없이) 사실이라고 생각함; 가정, 가설, 억측, 억설 [본문으로]
123. suspicion ; (pl. suspicions[-z]) 1. [U, C] 의심, 의혹, 수상쩍음; 혐의.; 동의어 DOUBT ;; 2. [U, C] 알아챔, 낌새챔; (막연한) 느낌[of]. [본문으로]
124. elevate ; 3. (전문 용어) (정도를) 높이다[증가시키다] [본문으로]
125. get off ; to manage to remove something from something; to remove something from somewhere [본문으로]
126. circumstance ; 1. [C] [주로 복수로] (일・사건 등을 둘러싼) 환경, 상황, 정황 ;; 2. [pl.] circumstances (개인의, 특히 재정적인) 형편[사정] [본문으로]
127. stale ; 3. 신선미가 없는, 진부한, 김이 빠진 [본문으로]
128. constitutional ; 2. 합헌적인, 헌법에 따르는 [본문으로]
129. delegitimize ; [동사] 정당한(적법한) 지위에서 물러나게 하다. [본문으로]