People are not fearful of algorithms, they’re fearful of agendas that the algorithms represent.
Over the last few weeks I have been discussing and listening to people’s views on the intelligent campus.
One topic which has resulted in a fair bit of controversy is the concept of using artificial intelligence to support teaching and learning. This isn’t some kind of HAL 9000 computer running the campus and refusing to open the library doors until Dave the learner has finished their essay. This is more about a campus system being able to learn from the users, take that data, do some analysis and make suggestions to the user on potential ideas for improvement and useful interventions.
Imagine a learner arriving at campus with the intention of writing an essay, needing a quiet place in which to do this. They check their Campus App on their smartphone and it recommends a location based on the ambient noise levels and the type of environment the learner has used before. It could take into account the distance from the coffee shop, depending on if coffee is used as a distraction or supports the learner in writing their essay. The learner can of course ignore all this and just go to where they want to, the app provides informed guidance and learns as the learner does more learning activities and which spaces they use.
Another scenario, is a teacher planning a session, with some relatively interactive and engaging learning activities. They ask the intelligent campus where is the best place for this to happen! The system takes on board the preferences of the teacher, the availability of rooms, information from previously successful similar sessions and any feedback from learners. The teacher can then make an informed choice about the best space for this session. After the learning, the system asks for feedback so that it can learn from and improve the decisions it makes.
I think some of the issues (or should we call them problems and fears) that people have with a concept such as this is they feel any such algorithm is secret and hidden and will have a built in bias.
As I wrote in my previous blog post on open analytics I said
So if we are to use algorithms to manage the next generation of learning environments, the intelligent campus, support for future apprenticeships and data driven learning gains, how can we ensure that we recognise that there is bias? If we do recognise the bias, how do we mitigate it? Using biased algorithms, can we make people aware that any results have a bias, and what it might mean?
People are not fearful of algorithms, they’re fearful of agendas that the algorithms represent. But if we make these algorithms open and accessible, we could mitigate some of those concerns.
So if we are to use algorithms to support teaching and learning, could we, by making the algorithms open, ensure that, we remove some of those fears and frustrations people have with a data approach? By making the algorithms open could we ensure that staff and learners could see how they work, how they learn and why they produce the results that do?
This does bring up another issue that people have mentioned which is the bias any algorithm has, bias which comes from the people who write it, sometimes consciously, sometimes unconsciously. There is an assumption that these algorithms are static and unchanging and written by people who have an agenda. As we know from using Google and other algorithms, these are constantly changing and being tweaked.
Could we go one step further and allow people to edit or even create their own algorithms? Allowing them to make suggestions on how they could be improved, creating new analytics that could benefit the wider community.
We need to embrace the benefits of a smart campus, because the technology is already here, but we need one which learns from the people who use it; we need to ensure that those people are the ones who inform and guide the development of that learning. They are able to and can decide which intelligent campus decisions to benefit from and which they can ignore. By making the whole process open and accessible, we can provide confidence in the decision making, we can feel we can trust those decisions. We mustn’t forget that giving them that literacy in this area, is perhaps the most important thing of all.
Remember that in both scenarios above the learner and teacher both, ultimately, have the decision to ignore the intelligent campus decisions, they can decide themselves to close the pod bay doors.
2 thoughts on “Open the pod bay doors…”
Open algorithms sounds fine until you realise that many will soon effectively not be created by humans. Well, they will, but they will just be “take a look at all this different information and tell me any useful or interesting patterns”.
Sure, the algorithm behind how that is done could be open, but that won’t be easily understood as it will be abstract from the actual data. And sure, such an algorithm might be able to ‘explain’ why, or more likely, how it connected particular disparate information, but again, it’s likely to be too abstract to ‘mean’ anything to most people.
But, the challenge of trust and confidence in what and how and why certain decisions are made when the person affected by it is just seeing a black box – definitely a challenge.
We trust hidden algorithms throughout our lives all the time – from how long you wait at traffic lights, to what discount you get from an online grocery order after it’s price matching, to what films get suggested on Netflix. For the most part, we seem happy to accept them without question – as long as they sort of work well enough that we don’t become conscious of them.
So is the challenge in making the algorithms so good that they are just accepted because they feel like the sorts of decisions we would have made instead? Perhaps some way to object and be listened to, even if we don’t understand what the problem was exactly or what was done to fix it?
“We need to embrace the benefits of a smart campus”
To me this raises more questions than answers … there are deep of hidden assumptions behind this … hidden assumptions which run through edtech like the stripes in a stick of rock.