On Machine Intelligence 3（4’50）
— — Zeynep Yufekci
Audit are great and important, but they don't solve all our problems.
审查是很中哟的， 但是他们不能解决所有问题。
Take facebook's powerful news feed algorithm, you know, the one that ranks everything and decides show you what from all the friends and pages you follow.
拿Facebook强有力的新闻喂食算法来说，你知道的，通过你的朋友圈和你浏览过的页面，来决定你的推荐内容的算法。
should you be shown another baby picture?
你是否应该被推荐另一张婴儿照片？
A sullen（面有愠色的 闷闷不乐的） note from an acquaintance?
来自一个熟人的闷闷不乐的状态？
An important but difficult news item?
一条重要但是晦涩的新闻？
There's no right answer.
没有正确的答案。
Facebook optimizes（使最优化） for engagement on the site: likes, shares, comments.
Facebook通过参与度来优化：喜欢、分享、评论。
So, In August of 2014, protests broke out in Ferguson, Missouri,
因此，在2014年8月，密苏里州佛格森爆发了抗议。
after the killing of an African- American teenager by a white police officer, under murky （污浊的 隐晦的 含糊的）circumstances.
在情况不明下，一个白人警察杀死了一个非裔少年，
The news of the protests was all over my algorithmically unfiltered Twitter feed, but no where on my facebook.
关于抗议的新闻在我的未经算法过滤的Twitter上铺天盖地，但是Facebook上却没有。
Was it my facebooks friends?
是因为我的Facebook好友不关注吗？
I disabled（丧失能力 使无效 使不能运转） Facebook's algorithm, which is hard because Facebook keeps wanting to make you come under the algorithm's control.
我关闭了Facebook的算法，这很难。因为Facebook总是想要使你一直在他的算法控制下。
And saw that my friends were talking about it, it's just that the algorithm wasn't showing it to me.
而看看我的朋友们怎么谈论此事？就是这个算法没有推荐给我这信息。
I researched this and found this was a widespread problem.
我调研了这个，发现这是一个普遍问题。
The story of Ferguson wasn't algorithm, friendly.
这个佛格森的故事不是算法问题，朋友。
It's not likable. who'e going to click on like?
它不是喜好问题，谁会点赞这个呢？
It's not even easy to comment on.
它甚至不是很容易去评论的。
Without likes and comments, the algorithm was likely showing it to even fewer people, so we didn't get to see this.
没有点赞和评论，算法很有可能会将它推荐给更少的朋友，所以我们没有看得到这条新闻。
Instead, that week, Facebook's algorithm highlighted this, which is the ALS Ice Bucket Challenge.
相反的，在那一周，Facebook算法热推了这个，谁是ALS冰桶挑战
Worthy cause, dump ice water, donate to charity, fine.
很有意义，倒冰水，捐款，很好。
But it was super algorithm friendly.
但是它太算法了，友好的。
The machine made this decision for us.
机器替我们做了这个决定。
A very important but difficult conversation might have been smothered（使窒息而死 厚厚的覆盖） had facebook been the only channel.
一个非常重要但是晦涩的会话将会被湮没掉，因为Facebook已经是仅有的渠道。
1. What is a possible danger of using an algorithm to feed it news?
...Important social issues could be ignored.
2. How was news ranks by facebook's new feed algorithm?
...according to the likelihood of user engagement.
3. When you protest something,...
....you strongly object to it.
3. 选词填空
I disabled Facebook's algorithm, which is hard because Facebook keeps wanting to make you come under the algorithm control, and saw that my friends were talking about it.
Now, finally, these systems can also be wrong in ways that don't resemble human systems.
Do you guys remember Watson, IBM's machine-intelligence system that wiped the floor with human contestants on Jeopardy?
It was a great player.
But then, for final Jeopardy, Watson was asked this question:
"It's largest airport is named for a world War 2 hero, its second- largest for a World War 2 battle.
Chicago. The two humans got it right.
Watson, on the other hand, answered “ Toronto" for a US city category.
The impressive system also made an error that a human would never make, a second - grader wouldn't make.
Our machine intelligence can fail in ways that don't fit error patterns of humans, in ways we won't expect and be prepared for.
It'd be lousy not to get a job one is qualified for, but it would triple suck if it was because of stack overflow in some subroutine.
In May of 2010, a flash crash on Wall Street fueled by a feedback loop in Wall Street's sell algorithm wiped a trillion dollars of value in 36 minutes.
I don't even want to think what error means in the context of lethal autonomous weapons.
So, yes, humans have always made biases.
Decision makers and gatekeepers, in courts, in news, in war... they make mistakes; but that's exactly my point.
We cannot escape these difficult questions.
We cannot outsource our responsibilities to machines.
Artificial intelligence does not give us a " Get out of ethics free" card.
1. Why is Tufekci concerned about using machine intelligence to control lethal(致命的 破坏性极大的) weapons?
...Algorithm errors might cause heavy casualties.
2. What does Tufekci mean by artificial intelligence does not give us a " Get out of ethics" cards?
Decisions made by AI don't free people from moral responsibilities.
3. To wipe the floor with someone is...
...to defeat them easily.
4. 选词填空
Our machine intelligence can fail in ways that don't fit error patterns of humans, in ways we won't expect and be prepared for.
Data scientist Fred Benenson calls this math - washing.
We need the opposite.
We need to cultivate algorithm suspicion, scrutiny and investigation.
We need to make sure we have algorithm accountability, auditing and meaningful transparently.
We need to accept that bringing math and computation to messy, value-laden human affairs does not bring objectivity; rather , the complexity of human affairs invades the algorithms.
Yes, we can and we should use computation to help us make better decisions.
But we have to own up our moral responsibilities to judgement,
and use algorithms within that framework.
not as a means to abdicate and outsource our responsibilities to one another as human to human.
Machine intelligence is here.
That means we must hold on ever tighter to human values and human ethics.
Thank you.
1. How does Tufekci end her presentation?
..by emphasizing the importance of human values and ethics.
2. Tufekci believes that...
...machine intelligence needs human oversight.
3. 完形填空
But we have to own up our moral responsibilities to judgement, and use algorithms within that framework, not as a means to abdicate and outsource our responsibilities to one another.
4. 听复述
These systems can also be wrong in ways that don't resemble human systems.
5. Artificial intelligence does not give us a " Get out of ethics free " card.
6. We need to cultivate algorithm suspicion, scrutiny and investigation.
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