This article was written in collaboration with my wife Elizabeth. We wrote this together and the ideas were generated during some of the great discussions we had during our evening 5k runs.
We all remember Prime Minister Trudeau’s famous response when asked about his gender equity promise for filling roles in the cabinet: “because it’s 2015.” And really, this call to action comes quite late in the historical span of modernity, but we’re glad someone at the highest levels of government in a developed nation has strongly proclaimed it. Most of us in Canada and likely around the world, were pleased to see Trudeau had staffed his cabinet with a significant amount of female leaders in important decision-making roles. And now, it’s 2017–a year that has been pivotal to say the least. Last Spring, Canada’s Minister of Science, Dr. Kirsty Duncan announced that universities in Canada are now required to improve their processes for hiring Canada Research Chairs and ensure those practices and review plans are equitable, diverse and inclusive. The government of Canada’s announcement is a call to action to include more women and other underrepresented groups at these levels, and it’s essentially come down to ultimatum: research universities will simply not receive federal funding allocations for these programs unless they take equity, diversity, and inclusion seriously in their recruitment and review processes.
When placed under the spotlight, the situation is a national embarrassment. Currently there is one woman Canada Excellence in Research Chair in this country and for women entrepreneurs the statistics are not much better. Women innovators in the industrial or entrepreneurial sphere are often left hanging without a financial net, largely as a result of a lack of overall support in business environments and major gaps in policy and funding. The good news is that change is happening now, and it’s affecting policies and practices at basic funding and policy levels. Federal and Provincial research granting agencies in Canada are actively responding to the call for more equitable and inclusive review practices within the majority their programs. The message is clear from the current Canadian government: get on board with your EDI policies and practices, or your boat won’t leave the harbour. But there’s always more work to be done.
The Robot Revolution
Combined with our pivotal political moment in history and on-going necessity for a level playing field for underrepresented groups, humans are situated at a crossroads of theory and praxis of human-machine interaction. The current intersection of human and machine certainly has critical implications for the academy, innovation, and our workplaces. It exposes the gaps to see what is possible, and we know the tools are here and must be harnessed for change. Even though we are literally living through mini “revolutions” each day as new technologies, platforms and code stream before our very eyes, humanity has been standing at this major intersection for a couple of centuries or more–at the very least, since the advent of non-human technologies that help humans process information and communicate ideas (cave paintings, the book, the typewriter, Herb Simon’s General Problem Solver). The human-AI link we need to critically assess now however, is how this convergence of the human-machine can work for women and underrepresented groups in the academy and entrepreneurial sectors in powerful ways. When it comes to creating more equitable spaces and providing women with the pay they deserve, we need to move beyond gloomy statements like “the robots are taking our jobs.” We must seek to understand how underrepresented and underpaid people can benefit from robots rather than running from them. And we must seek to understand why women in the academy, industry and other sectors haven’t been using the AI tools in dynamic ways all along. [Some are of course. As evidenced here. Two women business owners harnessed the power of technology to grow their client and customer base by sending emails from a fictional business partner named “Keith.” Client response to “Keith” seemed to do the trick in getting their customers and backers to take them seriously.]
In the psychology of decision making, a bias is usually defined as tendency to make decisions in a particular way. In many cases, the bias can he helpful and adaptive: we all have a bias to avoid painful situations. In other cases the bias can lead us to ignore information that would result in a better decision. An implicit bias refers to a bias that we are unaware of or the unconscious application of a bias that we are aware of. The construct has been investigated in how people apply stereotypes. For example, if you instinctively cross the street to avoid walking past a person of a different race or ethnic group, you are letting an implicit bias direct your behaviours. If you instinctively tend to doubt that a woman who takes a sick day is really sick, but tend to believe the same of a man, you are letting an implicit bias direct your behaviours. Implicit bias has been shown to also affect hiring decisions, teaching evaluations. Grants that are submitted by women scientists often receive lower scores and implicit bias is the most likely culprit. Implicit bias is difficult to avoid because it is implicit. The effect occurs without us being aware of it happening. We can overcome these biases if we are able to be more aware that they are happening. But AI also offers a possible way to overcome these biases as well.
An Engine for Equity at Work
AI and fast-evolving technologies can and should be used by women right now. We need to understand how they can be harnessed to create balanced workplaces, generate opportunity in business, and improve how we make decisions that directly affect women’s advancement and recognition in the academe. What promise or usefulness do AI tools hold for the development of balanced and inclusive forms of governance, review panel practices, opportunities for career advancement and recognition, and funding for start-ups? How can we use the power of these potent and disruptive technologies to improve processes and structures in the academy and elsewhere to make them more equitable and inclusive of all voices? There’s no denying that the tech space is changing things rapidly, but what is most useful to us now for correcting or improving imbalances or fixing inequitable, crumbling, and un-useful patriarchal structures. We need a map to navigate the intersection of rapid tech development and human-machine interaction and use AI effectively to reduce cognitive and unconscious biases in our decision-making; to improve the way we conduct and promote academic research, innovation and governance for women and underrepresented groups of people.
Some forward thinking companies are using the approach now. For example, several startups are using AI to prescreen candidates for possible interviews. In one case, the software (Talent Sonar) structured interviews and extracts candidate qualifications and removes candidate’s names and gender information from the report. These algorithms are designed to help remove implicit bias in hiring by focusing on the candidate’s attributes and workplace competencies without any reference to gender. Companies relying on these kinds of AI algorithms report a notable increase in hiring women. Artificial Intelligence, far from replacing workers, is actually helping to diversify and improve the modern workforce.
Academics have seen this change coming. Donna Haraway, in her Cyborg Manifesto re-conceptualizes modern feminist theory through a radical critique of the relationship between biology, gender, and cybernetics. For Haraway, a focus on the cybernetic–or the artificially intelligent–removes the reliance on gender in changing the way we think about power and how we make decisions about what a person has achieved, or is capable of doing. Can we, for example, start to aggressively incorporate AI methods for removing implicit or explicit bias from grant review panels–or more radically, remove humans from the process entirely? When governing boards place their votes for who will sit on the next Board of Trustees, or when university review committees adjudicate a female colleague’s tenure file in the academy, could this not be done via AI mechanisms or with an application that eliminates gender and uses keyword recognition for assessing the criteria? When we use AI to improve our decision making, we also have the ability to make it more equitable, diverse and inclusive. We can remove implicit or explicit cognitive biases based on gender or orientation, for example, when we are deciding who will be included in the next prestigious cohort of Canada Research Chairs.
AI can, and will continue to change the way human work is recognized in progressive ways: recognition of alternative work during parental leaves, improved governance and funding models, construction of equitable budgets and policy, and enhanced support for women entrepreneurs and innovators. AI is genderless. It is non-hierarchical. It has the power to be tossed like a dynamite stick to disrupt ancient academic structures that inherently favour patriarchal models for advancing up the tenure track. Equalization via AI gives women and underrepresented groups the power to be fully recognized and supported, from the seeds of their innovation (the academy) to the mobilization of those ideas in entrepreneurial spaces. The robots are in fact still working for us–at least, for now.