On Machine Intelligence 3
I have a friend who developed such computational systems to predict the likelihood of clinical or postpartum depression from social media data.?
ps：我有一個朋友開發(fā)了這樣的計算系統(tǒng)臊岸，可以從社交媒體數(shù)據(jù)中預測臨床或產(chǎn)后抑郁癥的可能性佛南。
friend朋友;?友人;?贊助者;?支持者;?自己人;?同志;?同伙;?同盟者;?<詩>與…為友
developed發(fā)達的;?高度發(fā)展的;?先進的;?成熟的;?成長如暖，發(fā)展汉形，壯大;?開發(fā);?研制;?患;?出現(xiàn);?開始侵襲;?開始影響;?develop的過去分詞和過去式
computational使用計算機的;?與計算機有關的
systems體系;?方法;?制度;?體制;?系統(tǒng);?身體;?system的復數(shù)
predict預言;?預告;?預報
likelihood可能;?可能性
clinical臨床的;?臨床診斷的;?冷淡的;?無動于衷的;?無同情心的;?簡陋的;?無裝飾的
social media社交網(wǎng)絡媒介
data數(shù)據(jù);?資料;?材料;?數(shù)據(jù)資料;?datum的復數(shù)
The results are impressive.?
Her system can predict the likelihood of depression months before the onset of any symptoms -- months before.?
ps:她的系統(tǒng)可以在任何癥狀出現(xiàn)的幾個月前——幾個月前——預測抑郁的可能性控漠。
predict預言;?預告;?預報
likelihood可能;?可能性
depression抑郁癥;?精神憂郁;?抑郁;?沮喪;?消沉;?蕭條期;?經(jīng)濟衰退;?不景氣
months月;?月份;?約30天的時間;?一個月的時間;?數(shù)月;?很長時間;?month的復數(shù)
before在…以前;?在…面前;?在前面;?到…為止;?到…之前;?以免;?不然;?以前;?過去;?已經(jīng)
onset開端嘀韧，發(fā)生带到，肇始
of any在所有的…當中
symptoms癥狀;?征候;?征兆;?symptom的復數(shù)
No symptoms, there's prediction.
?She hopes it will be used for early intervention. a
ps:她希望它能用于早期干預缚柳。
hopes希望，期望;?希望;?期望;?希望的東西;?期望的事情;?被寄予希望的人;?hope的第三人稱單數(shù)和復數(shù)
will談及將來;?愿沧烈，要掠兄，會像云，定要;?煩勞別人做事時用;?想要;?希望;?愿意;?喜歡;?立定志向;?決心;?決意;?立遺囑將贈與;?立遺囑贈與;?意志;?毅力;?自制力;?意愿;?心愿;?遺囑
early早期的;?初期的;?早先的;?早到的;?提前的;?提早的;?在早期;?在初期;?在開始階段;?提早;?提前;?先前;?早些時候;?…之前
intervention干涉;?干預
Great! But now put this in the context of hiring.
ps:偉大的锌雀！但現(xiàn)在把這個放在招聘的背景下蚂夕。
Great大的;?巨大的;?數(shù)量大的;?眾多的;?很;?非常的;?很多的;?極大的;?名人;?偉人;?偉大的事物;?很好地;?極好地;?很棒地
put放;?安置;?猛推;?用力插入;?將…送往;?使…前往
hiring租用;?租借;?聘用;?錄用;?雇用;?臨時雇用;?hire的現(xiàn)在分詞
So at this human resources managers conference, I approached a high-level manager in a very large company, and I said to her, "Look, what if, unbeknownst to you, your system is weeding out people with high future likelihood of depression??
ps:所以在這次人力資源經(jīng)理會議上，我找到了一家非常大的公司的高級經(jīng)理腋逆，我對她說：“聽著婿牍，如果你不知道，你的系統(tǒng)正在淘汰那些未來很可能患抑郁癥的人呢惩歉？
human resources人力資源;?人力資源部等脂，人事部
approached靠近，接近;?接洽;?建議;?要求;?接近;?approach的過去分詞和過去式
high-level級別高的;?高層的;?位置高的;?在高處的;?高級的;?高等的
what if如果…怎么辦
unbeknownst未知的撑蚌；不為人知的
weeding除雜草;?weed的現(xiàn)在分詞
people with<文>使充滿
They're not depressed now, just maybe in the future, more likely.?
What if it's weeding out women more likely to be pregnant in the next year or two but aren't pregnant now??
ps:如果排除了未來一兩年內(nèi)可能懷孕但現(xiàn)在還沒有懷孕的女性呢上遥？
What if如果…怎么辦
weeding除雜草;?weed的現(xiàn)在分詞
womenwoman 的復數(shù)
more likely適當?shù)??有希望的;?很可能的;?很像真事的;?多半
two二
pregnant懷孕的;?妊娠的;?飽含;?充溢著
now現(xiàn)在;?目前;?此刻;?從現(xiàn)在開始;?表示厭煩;?既然;?由于
What if it's hiring aggressive people because that's your workplace culture?"?
ps:如果因為這是你的工作文化而雇傭有進取心的人呢？”
What if如果…怎么辦
hiring租用;?租借;?聘用;?錄用;?雇用;?臨時雇用;?hire的現(xiàn)在分詞
aggressive好斗的;?挑釁的;?侵略的;?富于攻擊性的;?氣勢洶洶的;?聲勢浩大的;?志在必得的
people人;?人們;?大家;?人民争涌，國民;?民族;?種族;?居住在;?把…擠滿人;?住滿居民;?person的復數(shù)
because that因為粉楚，由于
workplace工作場所
culture文化，文明;?文化;?培養(yǎng)
You can't tell this by looking at gender breakdowns.?
ps:你不能通過性別分類來判斷這一點亮垫。
tell告訴;?告知;?提供;?說明;?顯示;?講述;?說;?表達;?臺形土墩;?講的話;?談話;?傳聞
looking at看;?考慮
gender性別;?性
breakdowns故障模软，損壞;?破裂;?失敗;?數(shù)字細目;?分類;?breakdown的復數(shù)
Those may be balanced.?
And since this is machine learning, not traditional coding, there is no variable there labeled "higher risk of depression," "higher risk of pregnancy," "aggressive guy scale."?
ps:因為這是機器學習，而不是傳統(tǒng)的編碼饮潦，所以沒有變量被標記為“更高的抑郁風險”燃异、“更高的懷孕風險”、“好斗的男人量表”
machine learning機器學習继蜡；機器改進能力
traditional傳統(tǒng)的;?習俗的;?慣例的;?因襲的;?守舊的
coding編碼方法回俐，編碼系統(tǒng);?為…編碼;?把…譯成密碼;?編程序;?編碼;?code的現(xiàn)在分詞
variable多變的;?易變的;?變化無常的;?可更改的;?可變的;?可變情況;?變量;?可變因素
depression抑郁癥;?精神憂郁;?抑郁;?沮喪;?消沉;?蕭條期;?經(jīng)濟衰退;?不景氣
pregnancy懷孕;?妊娠;?孕期
aggressive好斗的;?挑釁的;?侵略的;?富于攻擊性的;?氣勢洶洶的;?聲勢浩大的;?志在必得的
guy男人;?小伙子;?家伙;?一群男人;?伙計們;?兄弟們;?蓋伊模擬像(英國每年11月5日慶祝篝火之夜焚燒的身著古裝的人體模型);?牽拉;?固定;?開…的玩笑;?取笑[俚語]逃亡;?逃走;?亂演某角色
scale規(guī)模，范圍稀并，程度;?等級;?級別;?等級體系;?攀登;?到達…頂點;?去鱗;?刮除牙石
Not only do you not know what your system is selecting on, you don't even know where to begin to look.?
It's a black box. It has predictive power, but you don't understand it.
"What safeguards," I asked, "do you have to make sure that your black box isn't doing something shady?" She looked at me as if I had just stepped on 10 puppy tails.?
ps:“什么樣的保護措施鲫剿，”我問，“你必須確保你的黑匣子沒有做什么陰暗的事嗎稻轨？”她看著我灵莲，好像我剛踩了10條小狗的尾巴。
safeguards保護;?保障;?捍衛(wèi);?安全設施;?保護措施;?safeguard的第三人稱單數(shù)和復數(shù)
do you你愿意嗎
have to必須;?不得不;?勸告或建議時用;?表示一定真實或肯定發(fā)生
make sure that一定要;設法保證;?查明;核實;弄清
black box黑匣子;?黑盒;?飛行記錄儀;?未知框
shady背陰的;?陰涼的;?多陰的;?成蔭的;?可疑的;?鬼祟的;?非法的
as if好像…一樣,仿佛,似乎;?好像,仿佛,似乎;?好像殴俱，仿佛政冻，似乎
stepped邁步;?踩;?踏;?行走;?step的過去分詞和過去式
puppy小狗;?幼犬;?傲慢小子;?自負無禮的青年
tails尾;?尾巴;?有…尾巴的;?尾部;?后部;?跟蹤;?尾隨;?盯梢;?tail的第三人稱單數(shù)和復數(shù)
She stared at me and she said, "I don't want to hear another word about this." And she turned around and walked away.
?Mind you -- she wasn't rude.?
It was clearly: what I don't know isn't my problem, go away, death stare.
Look, such a system may even be less biased than human managers in some ways.?
ps:聽著，這樣一個系統(tǒng)在某些方面甚至可能比人類管理者少一些偏見线欲。
Look看;?瞧;?尋找;?尋求;?注意;?留心;?留神;?查找;?眼神;?表情;?神情;?臉色;?喂明场，聽我說
such這樣的，那樣的李丰，類似的;?這樣的苦锨，下述一類的;?這樣，非常，如此程度
even甚至舟舒，連拉庶，即使;?甚至更，愈加秃励，還;?甚至可以說氏仗，其實，實際上;?平滑的;?平的;?平坦的;?變化不大的夺鲜，均勻的皆尔，平穩(wěn)的;?相等的;?均等的;?平;?相等;?變平
less較少的，更少的;?較少;?較小;?更少;?更小;?沒那么多;?少;?減去;?扣除;?小的;?比較小的;?可愛的币励，可憐的慷蠕，討厭的;?年幼的;?幼小的;?little的比較級
biased有偏見的;?傾向性的;?片面的;?偏重;?偏向;?使有偏見;?使偏心;?使偏向;?bias的過去分詞和過去式
human人的;?顯示人類特有弱點的;?人本性的;?有人情味的;?通人情的;?人
managers經(jīng)理，經(jīng)營者食呻，老板;?經(jīng)理人砌们，經(jīng)紀人，個人經(jīng)理;?主教練;?manager的復數(shù)
in some ways在某些方面;?在某種程度上;?在一些方面
And it could make monetary sense.?
ps:而且它可以在金錢上說得通搁进。
monetary貨幣的浪感，錢的
sense感覺官能;?感覺，意識;?理解力;?判斷力;?感覺到;?意識到;?覺察出;?檢測出
But it could also lead to a steady but stealthy shutting out of the job market of people with higher risk of depression.?
ps:但這也可能導致穩(wěn)定但隱秘地將高抑郁風險人群拒之門外饼问。
lead to通向
steady穩(wěn)步的影兽，持續(xù)的，勻速的;?穩(wěn)定的;?恒定的;?穩(wěn)的;?平穩(wěn)的;?穩(wěn)固的;?使穩(wěn);?使平穩(wěn);?穩(wěn)住;?恢復平穩(wěn);?穩(wěn)定下來;?使平靜;?使冷靜;?使鎮(zhèn)定;?穩(wěn)定地;?持續(xù)地;?穩(wěn)固地;?哎莱革，注意點;?注意峻堰，當心，小心
stealthy偷偷摸摸的;?不聲張的;?秘密的
shutting關閉;?關上;?合上;?停止營業(yè)盅视，關門捐名，打烊;?shut的現(xiàn)在分詞
out of從…里面；離開;?從…的狀態(tài)中;?越出…之外
people with<文>使充滿
higher高等級的;?級別較高的;?較重要的;?在高處;?向高處;?高;?大;?音調(diào)高;?high的比較級
risk危險;?風險;?危險人物;?會帶來風險的事物;?借款人闹击，保險對象;?使…冒風險;?冒…的風險;?冒險做;?大膽做
depression抑郁癥;?精神憂郁;?抑郁;?沮喪;?消沉;?蕭條期;?經(jīng)濟衰退;?不景氣
Is this the kind of society we want to build, without even knowing we've done this, because we turned decision-making to machines we don't totally understand?
ps:難道這就是我們想要建立的社會镶蹋，甚至不知道我們已經(jīng)做到了，因為我們把決策變成了我們不完全理解的機器赏半？
kind of稍微;?有幾分;?有點兒
want to應該
build建筑;?建造;?創(chuàng)建;?開發(fā);?逐漸增強;?體形;?體格;?身材
knowing會意的;?心照不宣的;?知情的;?知道;?知悉;?了解;?認識到;?懂得;?意識到;?確信;?確知;?肯定;?know的現(xiàn)在分詞
've(=value engineering)價值工程學;?同“have”
done完畢;?了結(jié);?結(jié)束;?煮熟;?熟了;?合乎禮儀;?合乎規(guī)矩;?得體;?行贺归，好;?做，干断箫，辦;?做;?行動;?表現(xiàn);?進展拂酣，進行;?do的過去分詞
turned轉(zhuǎn)動，旋轉(zhuǎn);?轉(zhuǎn)身;?扭轉(zhuǎn);?翻轉(zhuǎn);?翻動;?把…翻過來;?turn的過去分詞和過去式
machines機器;?機械裝置;?核心機構(gòu);?制造仲义，加工成型;?machine的第三人稱單數(shù)和復數(shù)
totally完全;?全部地;?整個地
Questions:
1.Why does Tufekci refer to machine learning as a black box?
People can see the results but not the internal process.
ps:人們可以看到結(jié)果婶熬，但看不到內(nèi)部過程剑勾。
2.why did the manager refuse to answer her question?
she didn't want to address the potential ethical issues with the system.
3.To weed somebody out means
to get rid of them.
4.What example does Tufekci give as a potential consequence of using machine intelligence for human hiring?
People with higher risk of depression would be less likely to be employed
5.what does the system develop her friend do?
it predicts the likelihood of depression.
6.to be at the onset of something means to?
be at the beginning of it
Fill in the blanks:
Not only do you not know what your system is selecting on, you don't even know where to begin to look.It's a black box. It has predictive power, but you don't understand it.
Another problem is this: these systems are often trained on data generated by our actions, human imprints.?
ps:另一個問題是：這些系統(tǒng)通常是根據(jù)我們的行為產(chǎn)生的數(shù)據(jù)，即人類的印記來訓練的赵颅。
Another又一;?再一;?另一;?不同的;?類似的
problem棘手的問題;?難題;?困難;?邏輯題;?數(shù)學題;?找麻煩的;?成問題的;?惹亂子的
systems體系;?方法;?制度;?體制;?系統(tǒng);?身體;?system的復數(shù)
often時常;?常常;?往往;?大多
trained訓練;?培訓;?接受訓練;?進行…訓練;?進行體育鍛煉;?教育;?培養(yǎng)…的能力;?train的過去分詞和過去式
data數(shù)據(jù);?資料;?材料;?數(shù)據(jù)資料;?datum的復數(shù)
generated產(chǎn)生;?引起;?generate的過去分詞和過去式
actions行動;?行為過程;?所做之事;?行為;?訴訟;?起訴;?務必做虽另，確保處理;?action的第三人稱單數(shù)和復數(shù)
human人的;?顯示人類特有弱點的;?人本性的;?有人情味的;?通人情的;?人
imprints產(chǎn)生重大影響;?銘刻;?使銘記;?印;?壓印;?印記;?痕跡;?持久影響;?出版商名稱;?imprint的
Well, they could just be reflecting our biases, and these systems could be picking up on our biases and amplifying them and showing them back to us, while we're telling ourselves, "We're just doing objective, neutral computation."
ps:好吧，它們可能只是反映了我們的偏見性含，而這些系統(tǒng)可能會發(fā)現(xiàn)我們的偏見，并放大它們鸳惯，并將它們展示給我們商蕴，而我們告訴自己，“我們只是在做客觀芝发、中立的計算绪商。”
reflecting反映;?映出;?反射;?顯示辅鲸，表明格郁，表達;?reflect的現(xiàn)在分詞
biases偏見;?偏心;?偏向;?偏愛;?特殊能力;?斜裁;?使有偏見;?使偏心;?使偏向;?bias的第三人稱單數(shù)和復數(shù)
and these形形色色
picking選擇;?挑選;?采;?摘;?摘掉，剔除独悴，掐去;?pick的現(xiàn)在分詞;?采摘;?掘;?撬開;?采得物;?贓物
amplifying放大例书，增強;?闡發(fā)，充實;?amplify的現(xiàn)在分詞
ourselves(we的反身形式)我們自己;?(用于強調(diào)或代替we或us)我們自己刻炒，親自
objective目標;?目的;?物鏡;?客觀的;?就事論事的;?不帶個人感情的;?客觀存在的;?基于事實的;?賓格的
neutral中立的;?持平的;?無傾向性的;?中立國的;?中性的;?不含褒貶義的;?空擋的;?中立者;?中立國;?素凈色;?中和色
computation計算;?計算過程
Researchers found that on Google, women are less likely than men to be shown job ads for high-paying jobs.?
And searching for African-American names is more likely to bring up ads suggesting criminal history, even when there is none.?
ps:尋找非裔美國人的名字更有可能出現(xiàn)暗示犯罪歷史的廣告决采，即使沒有
searching深挖細究的;?認真徹底的;?搜索;?搜尋;?搜查;?查找;?搜身;?思索，細想;?search的現(xiàn)在分詞
African-American非洲裔美國人
names名字;?名稱;?名譽;?名聲;?名氣;?有…名稱的;?以…著名的;?有…名聲的;?命名;?給…取名;?說出…的名稱;?叫出…的名字;?確定;?說定;?準確陳述;?name的第三人稱單數(shù)和復數(shù)
more likely適當?shù)??有希望的;?很可能的;?很像真事的;?多半
bring up養(yǎng)育;?撫養(yǎng);?養(yǎng)大;?談及;?提出;?嘔吐;?咳出
suggesting建議;?提議;?推薦;?舉薦;?使想到;?使認為;?表明;?suggest的現(xiàn)在分詞
criminal犯罪的;?犯法的;?涉及犯罪的;?刑法的;?刑事的;?道德上錯誤的;?不道德的;?罪犯
even when即使當
none沒有一個;?毫無;?(與the加比較級連用)一點都不坟奥，絕無;?(與too加形容詞或副詞連用)絕不树瞭，不怎么
Such hidden biases and black-box algorithms that researchers uncover sometimes but sometimes we don't know, can have life-altering consequences.
ps:這種隱藏的偏見和黑盒算法，研究人員有時發(fā)現(xiàn)爱谁，但有時我們不知道晒喷，可能會有改變生活的后果。
Such這樣的访敌，那樣的凉敲，類似的;?這樣的，下述一類的;?這樣寺旺，非常荡陷，如此程度
hidden難以看見的;?隱藏的;?隱秘的;?藏;?隱蔽;?躲避;?隱匿;?遮住;?遮擋;?hide的過去分詞
biases偏見;?偏心;?偏向;?偏愛;?特殊能力;?斜裁;?使有偏見;?使偏心;?使偏向;?bias的第三人稱單數(shù)和復數(shù)
algorithms算法;?計算程序;?algorithm的復數(shù)
researchers研究員;?科研工作者;?researcher的復數(shù)
uncover揭開蓋子;?發(fā)現(xiàn);?揭露;?揭發(fā)
sometimes有時;?間或
know知道;?知悉;?了解;?認識到;?懂得;?意識到;?確信;?確知;?肯定
have有;?持有;?占有;?由…組成;?顯示出，帶有;?與過去分詞連用構(gòu)成完成時
consequences結(jié)果;?后果;?重要性;?consequence的復數(shù)
In Wisconsin, a defendant was sentenced to six years in prison for evading the police.?
ps:在威斯康星州迅涮，一名被告因逃避警察而被判處六年監(jiān)禁
Wisconsin威斯康星州
defendant被告人;?被告
sentenced判決;?宣判;?判刑;?sentence的過去分詞和過去式
six六
years年;?日歷年;?一年時間;?與某事相關的一年;?年度;?year的復數(shù)
in prison在獄中
evading逃脫;?躲開;?躲避;?逃避废赞，規(guī)避;?回避，避開;?evade的現(xiàn)在分詞
police警察部門;?警方;?巡查;?維護治安;?監(jiān)督;?管制
You may not know this, but algorithms are increasingly used in parole and sentencing decisions.?
ps:你可能不知道這一點叮姑，但算法越來越多地用于假釋和判刑決定唉地。
may not不得
know知道;?知悉;?了解;?認識到;?懂得;?意識到;?確信;?確知;?肯定
algorithms算法;?計算程序;?algorithm的復數(shù)
increasingly越來越多地;?不斷增加地
used習慣于;?適應;?用過的;?舊的;?二手的;?使用;?利用;?運用;?消耗;?說据悔，寫，使用;?use的過去分詞和過去式
parole假釋;?有條件的釋放;?言語;?有條件地釋放
sentencing判決;?宣判;?判刑;?sentence的現(xiàn)在分詞
decisions決定耘沼，抉擇;?決斷;?果斷;?作決定;?決策;?decision的復數(shù)
He wanted to know: How is this score calculated? It's a commercial black box.
?The company refused to have its algorithm be challenged in open court.?
ps:該公司拒絕在公開法庭上對其算法提出質(zhì)疑极颓。
refused拒絕;?回絕;?推卻;?拒絕給;?refuse的過去分詞和過去式
have有;?持有;?占有;?由…組成;?顯示出，帶有;?與過去分詞連用構(gòu)成完成時
algorithm算法;?計算程序
challenged傷殘的群嗤，有殘疾的;?對…懷疑;?拒絕接受;?向挑戰(zhàn);?強烈建議;?考查…的能力;?考驗…的技巧;?challenge的過去分詞和過去式
in open court公開審訊菠隆，公開庭審
But ProPublica, an investigative nonprofit, audited that very algorithm with what public data they could find, and found that its outcomes were biased and its predictive power was dismal, barely better than chance, and it was wrongly labeling black defendants as future criminals at twice the rate of white defendants.
ps:但是ProPublica，一個調(diào)查性的非營利組織狂秘，用他們能找到的公共數(shù)據(jù)對算法進行了審計骇径，發(fā)現(xiàn)它的結(jié)果是有偏見的，它的預測能力是令人沮喪的者春，幾乎不比偶然的好破衔，它錯誤地將黑人被告列為未來的罪犯，其比率是白人被告的兩倍钱烟。
audited審計;?稽核;?旁聽;?audit的過去分詞和過去式?audited英 [???d?t?d] 美 [???d?t?d] v.審計;稽核;旁聽(大學課程)audit的過去分詞和過去式
dismal英 [?d?zm?l] 美 [?d?zm?l] adj.憂郁的;凄涼的;慘淡的;陰沉的;不熟練的;差勁的;不怎么樣的派生詞： dismally adv.
better than比…好
wrongly不公正地;?不道德地;?錯誤地
defendants被告人;?被告;?defendant的復數(shù)
rate of【財】匯率
So, consider this case: This woman was late picking up her god sister from a school in Broward County, Florida, running down the street with a friend of hers.?
ps:所以晰筛，考慮一下這個例子：這個女人在佛羅里達州布勞沃德縣的一所學校里，和她的一個朋友在街上跑來跑去接她的上帝妹妹拴袭。
consider this設想一下; 思索一下
woman成年女子;?婦女;?女子;?女人;?來自…的女子
late接近末期;?在晚年;?遲到;?遲發(fā)生;?遲做;?近日暮的;?近深夜的;?遲;?晚;?臨近日暮;?接近午夜
picking選擇;?挑選;?采;?摘;?摘掉读第，剔除，掐去;?pick的現(xiàn)在分詞;?采摘;?掘;?撬開;?采得物;?贓物
god上帝;?天主;?真主;?神;?極受崇拜的人;?被崇拜的偶像
sister姐;?妹;?姐妹;?護士長
Broward布勞沃德
Florida佛羅里達州
running down全熔化
hers她的;?屬于她的
They spotted an unlocked kid's bike and a scooter on a porch and foolishly jumped on it.?
ps:他們發(fā)現(xiàn)門廊上有一輛未上鎖的童車和一輛踏板車拥刻，于是愚蠢地跳了上去卦方。
spotted有花點的;?有斑點的;?看見;?看出;?注意到;?發(fā)現(xiàn);?讓分，讓子泰佳，讓步;?spot的過去分詞和過去式
unlocked未鎖的;?開…的鎖;?發(fā)現(xiàn);?揭示;?揭開;?unlock的過去分詞和過去式
kid小孩;?年輕人;?小山羊;?小山羊皮革;?戲弄;?開玩笑;?欺騙;?哄騙;?妹妹;?弟弟
bike自行車;?腳踏車;?摩托車;?騎自行車;?騎車;?騎摩托車遞送
scooter小型摩托車;?滑板車
porch門廊;?門廳
foolishly愚蠢地盼砍，可笑地
jumped跳;?躍;?跳躍;?跳過;?躍過;?跨越;?快速移動;?突然移動;?jump的過去分詞和過去式
on it大量喝酒
As they were speeding off, a woman came out and said, "Hey! That's my kid's bike!"?
ps:當他們超速行駛時，一個女人出來說：“嘿逝她！那是我孩子的自行車浇坐！”
werebe 的過去時復數(shù)和第二人稱單數(shù)形式;?有時代替 was，用于條件從句黔宛、動詞 wish 之后等
speeding超速駕駛;?超速行駛;?快速前行;?快速運送;?加速;?促進;?speed的現(xiàn)在分詞
off離開;?距近刘，離;?用以表示除去了某物;?起跑;?從落下;?離，距;?偏離;?從…去掉;?從…移開;?不新鮮;?變質(zhì);?不禮貌;?不熱情;?冷淡;?不能接受;?難以容忍;?不行;?殺死
woman成年女子;?婦女;?女子;?女人;?來自…的女子
came來;?來到;?到達臀晃，抵達;?來做;?來取;?來拿;?come的過去式
out出來;?不在家;?不在工作地點;?外出;?出去;?離開邊緣;?回避的方法;?托詞;?出路;?揭露觉渴，公布
said說;?講;?告訴;?念;?朗誦;?背誦;?表達侧到，表述;?say的過去分詞和過去式
Hey嘿淳蔼，喂;?嘿;?就這樣嗦董，怎么樣物延，你說呢
kid小孩;?年輕人;?小山羊;?小山羊皮革;?戲弄;?開玩笑;?欺騙;?哄騙;?妹妹;?弟弟
bike自行車;?腳踏車;?摩托車;?騎自行車;?騎車;?騎摩托車遞送
They dropped it, they walked away, but they were arrested.
She was wrong, she was foolish, but she was also just 18.
?She had a couple of juvenile misdemeanors.?
ps:她有幾次少年輕罪。
juvenile英 [?d?u?v?na?l] 美 [?d?u?v?nl] adj.少年的;未成年的;幼稚的;不成熟的;孩子氣的n.少年
Meanwhile, that man had been arrested for shoplifting in Home Depot -- 85 dollars' worth of stuff, a similar petty crime.?
ps:與此同時玉雾，這名男子因在家得寶（Home Depot）商店行竊被捕——價值85美元的東西椎椰，類似的輕微犯罪哺呜。
Meanwhile同時;?與此同時;?其間;?對比之下;?其時，其間
been曾經(jīng)到過;?are的過去分詞;?be的過去分詞
arrested逮捕;?拘留;?阻止;?中止;?吸引;?arrest的過去分詞和過去式
shoplifting冒充顧客在商店行竊;?在商店行竊;?shoplift的現(xiàn)在分詞
dollars元;?一元;?美元;?dollar的復數(shù)
worth有…價值;?值…錢;?值得瓣距，有價值;?值得;?價值(十元黔帕、40英鎊等)的東西;?能用的東西;?價值;?意義;?作用
stuff東西，物品蹈丸，玩意兒;?活兒成黄，話，念頭逻杖，東西;?基本特征;?特質(zhì);?根本;?基礎;?原料;?填滿;?裝滿;?塞滿;?灌滿;?把…塞進;?在里填入;?給…裝餡
similar相像的;?相仿的;?類似的;?類似物;?相像的人;?相似物
But he had two prior armed robbery convictions.?
ps:但他有過兩次持械搶劫的前科奋岁。
prior先前的;?較早的;?在前的;?優(yōu)先的;?占先的;?較重要的;?在前面的;?院長;?會長，副院長
armed robbery持械搶劫
convictions判罪;?定罪;?證明有罪;?堅定的看法;?深信;?堅信;?堅定;?肯定;?conviction的復數(shù)
But the algorithm scored her as high risk, and not him.
ps:但算法認為她有高風險弧腥，而不是他厦取。
?Two years later, ProPublica found that she had not reoffended.?
It was just hard to get a job for her with her record.?
He, on the other hand, did reoffend and is now serving an eight-year prison term for a later crime.?
Clearly, we need to audit our black boxes and not let them have this kind of unchecked power.
ps:顯然潮太，我們需要審計我們的黑匣子管搪，而不是讓它們擁有這種未經(jīng)檢查的權力。
Clearly清楚地;?清晰地;?明白地;?易懂地;?明顯地;?顯然地
need需要;?必需;?有必要;?必須;?特別需要;?迫切要求;?需要的事物;?欲望
audit審計;?稽核;?審查铡买，檢查;?旁聽
black boxes黑匣子;?黑盒;?飛行記錄儀;?未知框;?black box的復數(shù)
not(構(gòu)成動詞be更鲁、do和have及情態(tài)動詞can或must等的否定形式，称娉縮略為n't)不澡为，沒有;?不;?(用于hope、expect景埃、believe等動詞后媒至，作為否定的回答)不
have有;?持有;?占有;?由…組成;?顯示出，帶有;?與過去分詞連用構(gòu)成完成時
them他們;?她們;?它們;?指性別不詳?shù)娜藭r谷徙，用以代替him或her
kind of稍微;?有幾分;?有點兒
unchecked不加約束的;?不受限制的;?放任的
power控制力;?影響力;?操縱力;?統(tǒng)治;?政權;?能力;?機會;?驅(qū)動拒啰，推動;?迅猛移動，快
Questions:
1.Why is it important to audit machine intelligence ?
to make sure its decisions are accurate and objective.
2.To audit something is
?to closely examine it.
3.why might machine intelligence be biased?
?It is trained on data generated by humans.
4.what was the result of?audited by?ProPublica?
the outcomes of the algorithm were biased against black people.
Fill in the blanks:
these systems could be picking up on our biases and amplifying them and showing them back to us.
Put the sentences into correct order:
This woman was late picking up her god sister from a school in Broward County, Florida, running down the street with a friend of hers. They spotted an unlocked kid's bike and a scooter on a porch and foolishly jumped on it. As they were speeding off, a woman came out and said, "Hey! That's my kid's bike!" They dropped it, they walked away, but they were arrested.
Repeat and read the sentences：
1.?these systems are often trained on data generated by our actions, human imprints.
2.we need to audit our black boxes and not let them have this kind of unchecked power.
3.What if it's weeding out women more likely to be pregnant in the next year or two who aren't pregnant now??
4.they could just be reflecting our biases, and these systems could be picking up on our biases and amplifying them and showing them back to us.
5.algorithms are increasingly used in parole and sentencing decisions.?
6.Such hidden biases and black-box algorithms that researchers uncover sometimes but sometimes we don't know, can have life-altering consequences.
