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In 2014, Stanford professor Clifford Nass faced a student revolt. Nass’s students claimed that those in one section of his technology interface course received higher grades on the final exam than counterparts in another. Unfortunately, they were right: two different teaching assistants had graded the two different sections’ exams, and one had been more lenient than the other. Students with similar answers had ended up with different grades.
Nass, a computer scientist, recognized the unfairness and created a technical fix: a simple statistical model to adjust scores, where students got a certain percentage boost on their final mark when graded by a TA known to give grades that percentage lower than average. In the spirit of openness, Nass sent out emails to the class with a full explanation of his algorithm. Further complaints poured in, some even angrier than before. Where had he gone wrong?
Companies and governments increasingly rely upon algorithms to make decisions that affect people’s lives and livelihoods – from loan approvals, to recruiting, legal sentencing, and college admissions. Less vital decisions, too, are being delegated to machines, from internet search results to product recommendations, dating matches, and what content goes up on our social media feeds. In response, many experts have called for rules and regulations that would make the inner workings of these algorithms transparent. But as Nass’s experience makes clear, transparency can backfire if not implemented carefully. Fortunately, there is a smart way forward.
Transparency and Trust
Two years after the protests in Nass’s class, René Kizilcec, a young Stanford PhD student who had worked under Nass decided to conduct a study looking at the effects of grading transparency on student trust. He used the massive open online course (MOOC) platform Coursera, which, like many MOOCs, employs peer grading to manage an extraordinarily high volumes of exams. The work gets done, but peer grading exacerbates the problem of grading bias since it involves large numbers of graders with varying personalities and tendencies.
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In Kizilcec’s study, 103 students submitted essays for peer grading and got back two marks: a grade that represented an average peer grade, and a ‘computed’ grade which was the product of an algorithm that adjusted for bias. Some students were told, “Your computed grade is X, which is the grade you received from your peers.” Others were provided greater transparency – in fact an entire paragraph explaining how the grade had been calculated, why adjustments had been made (to account for peers’ “bias and accuracy”), and naming the type of algorithm used (“an expectation maximization algorithm with a prior”). Both groups were then asked to rate their trust in the process.
The students had also been asked what grade they thought they would get, and it turned out that levels of trust in those students whose actual grades hit or exceeded that estimate were unaffected by transparency. But people whose expectations were violated – students who received lower scores than they expected – trusted the algorithm more when they got more of an explanation of how it worked. This was interesting for two reasons: it confirmed a human tendency to apply greater scrutiny to information when expectations are violated. And it showed that the distrust that might accompany negative or disappointing results can be alleviated if people believe that the underlying process is fair.
But how do we reconcile this finding with Nass’s experience? Kizilcec had in fact tested three levels of transparency: low and medium but also high, where the students got not only a paragraph explaining the grading process but also their raw peer-graded scores and how these were each precisely adjusted by the algorithm to get to a final grade. And this is where the results got more interesting. In the experiment, while medium transparency increased trust significantly, high transparency eroded it completely, to the point where trust levels were either equal to or lower than among students experiencing low transparency.
Making Modern AI Transparent: A Fool’s Errand?
What are businesses to take home from this experiment? It suggests that technical transparency – revealing the source code, inputs, and outputs of the algorithm – can build trust in many situations. But most algorithms in the world today are created and managed by for-profit companies, and many businesses regard their algorithms as highly valuable forms of intellectual property that must remain in a “black box.” Some lawmakers have proposed a compromise, suggesting that the source code be revealed to regulators or auditors in the event of a serious problem, and this adjudicator will assure consumers that the process is fair.
This approach merely shifts the burden of belief from the algorithm itself to the regulators. This may a palatable solution in many arenas: for example, few of us fully understand financial markets, so we trust the SEC to take on oversight. But in a world where decisions large and small, personal and societal, are being handed over to algorithms, this becomes less acceptable.
Another problem with technical transparency is that it makes algorithms vulnerable to gaming. If an instructor releases the complete source code for an algorithm grading student essays, it becomes easy for students to exploit loopholes in the code: maybe, for example, the algorithm seeks evidence that the students have done research by looking for phrases such as “according to published research.” A student might then deliberately use this language at the start of every paragraph in her essay.
But the biggest problem is that modern AI is making source code – transparent or not – less relevant compared with other factors in algorithmic functioning. Specifically, machine learning algorithms – and deep learning algorithms in particular – are usually built on just a few hundred lines of code. The algorithms logic is mostly learned from training data and is rarely reflected in its source code. Which is to say, some of today’s best-performing algorithms are often the most opaque. High transparency might involve getting our heads around reams and reams of data – and then still only being able to guess at what lessons the algorithm has learned from it.
This is where Kizilcec’s work becomes relevant – a way to embrace rather than despair over deep learning’s impenetrability. His work shows that users will not trust black box models, but they don’t need – or even want – extremely high levels of transparency. That means responsible companies need not fret over what percentage of source code to reveal, or how to help users “read” massive datasets. Instead, they should work to provide basic insights on the factors driving algorithmic decisions.
Explainable AI: The Way Forward
One of the more important sections of the EU’s groundbreaking General Data Protection Regulation (GDPR) focuses on the right to explanation. Essentially, it mandates that users be able to demand the data behind the algorithmic decisions made for them, including in recommendation systems, credit and insurance risk systems, advertising programs, and social networks. In doing so, it tackles “intentional concealment” by corporations. But it doesn’t address the technical challenges associated with transparency in modern algorithms. Here, a movement called explainable AI (xAI) might be helpful.
xAI systems work by analyzing various inputs used by a decision-making algorithm, measuring the impact of each of the inputs individually and in groups, and finally reporting the set of inputs that had the biggest impact on the final decision. For example, if such a system were applied to an essay-grading algorithm, it might analyze how changes in various inputs such as content, word count, vocabulary level, grammar, or sourcing affected the final grade and provide an explanation like this:
- Tim received a score of 73 on his essay.
- 49 percent of Tim’s score is explained by content matches with key concepts listed in the grading key.
- 18 percent of the score is explained by Tim’s essay exceeding the word-count threshold of 1,000 words but not exceeding the limit of 1,300 words.
- 13 percent of the score is explained by the fact that Tim’s essay mentioned relevant source documents in appropriate contexts.
- The rest of Tim’s score is explained by several other less significant factors.
In some of our ongoing research, we find that achieving this level of transparency is well within the capabilities of today’s machine learning and statistical methods. This kind of analysis could help engineers get around the black box problem – the problem that they themselves don’t always know what is motivating the decisions of their machine learning algorithms. It identifies relationships between inputs and outcomes, spots possible biases, and gives routes into fixing problems. Would it also, for users, hit that transparency sweet spot that Kizilcec identified? It’s too soon to tell. In the meantime, it is worth remembering that building trust in machine learning and analytics will require a system of relationships, where regulators, for example, get high levels of transparency, and users accept medium levels. “Both sides are important,” says Kizilcec of how transparency for auditors versus users can effect buy-in. “If we get only one side right, it won’t work.”